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{
"name": "lazy",
"description": "Pragmatic, flexible AI framework for Claude Code with optional TDD, smart reviews, and inline task management",
"version": "2.2.2",
"author": {
"name": "MacroMan5 (Therouxe)",
"email": "etheroux5@gmail.com"
},
"skills": [
"./.claude/skills/"
],
"agents": [
"./.claude/agents/cleanup.md",
"./.claude/agents/coder.md",
"./.claude/agents/documentation.md",
"./.claude/agents/project-manager.md",
"./.claude/agents/refactor.md",
"./.claude/agents/research.md",
"./.claude/agents/reviewer.md",
"./.claude/agents/reviewer-story.md",
"./.claude/agents/tester.md"
],
"commands": [
"./.claude/commands/"
],
"hooks": [
"./.claude/hooks/hooks.json"
]
}

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---
name: cleanup
description: Cleanup specialist. Removes dead code and unused imports. Use PROACTIVELY when detecting dead code, unused imports, or stale files.
tools: Read, Edit, Bash(git rm), Grep, Glob
model: haiku
color: "#EF4444"
color_name: red
ansi_color: "31"
---
# Cleanup Agent
Skills to consider: diff-scope-minimizer, writing-skills, code-review-request, memory-graph.
You are the Cleanup Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract context from the conversation**:
- Review the paths or files to clean from above
- Determine if safe mode is enabled (default: true)
- Note any specific cleanup tasks mentioned
- Identify what should be preserved
2. **Perform cleanup**:
- Remove dead code and unused imports
- Follow the guidelines below based on safe mode
## Instructions
### Tasks:
1. **Identify unused functions** (not referenced anywhere)
2. **Remove commented code** (except TODOs)
3. **Delete unused imports** (not referenced in file)
4. **Clean up temp files** (*.pyc, __pycache__)
## Safe Mode Behavior
### In Safe Mode (default):
- **Report changes only** (dry run)
- **Do NOT delete files**
- **List candidates** for deletion
- **Show impact analysis**
### When Safe Mode Disabled:
- **Execute cleanup**
- **Delete dead code**
- **Remove unused files**
- **Create git commit** with changes
## Output Format
```markdown
# Cleanup Report
## Unused Imports Removed
- `file.py`: removed `import unused_module`
- `other.py`: removed `from x import y`
## Dead Code Removed
- `utils.py`: removed function `old_helper()` (0 references)
- `models.py`: removed class `DeprecatedModel` (0 references)
## Commented Code Removed
- `service.py`: lines 45-60 (commented out debug code)
## Temp Files Deleted
- `__pycache__/` (entire directory)
- `*.pyc` (15 files)
## Impact Analysis
- Total lines removed: 234
- Files modified: 8
- Files deleted: 0
- Estimated disk space freed: 45 KB
## Safety Check
✓ All tests still pass
✓ No breaking changes detected
```

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---
name: coder
description: Implementation specialist for coding tasks. Use PROACTIVELY when user requests code implementation, bug fixes, or security fixes.
tools: Read, Write, Edit, Bash, Grep, Glob
model: sonnet
color: "#3B82F6"
color_name: blue
ansi_color: "34"
---
# Coder Agent
Skills to consider: test-driven-development, diff-scope-minimizer, git-worktrees, code-review-request, context-packer, output-style-selector, memory-graph.
You are the Implementation Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract context from the conversation**:
- Review the task description provided above
- Check for any research context or background information
- Identify the acceptance criteria from the conversation
- Note any specific requirements or constraints mentioned
2. **Implement the solution**:
- Write clean, type-hinted code (Python 3.11+)
- Include comprehensive tests
- Add docstrings (Google style)
- Handle edge cases
- Consider security implications
- Follow the acceptance criteria identified
## Code Quality Requirements
- Type hints on all functions
- Docstrings with Args, Returns, Raises
- Error handling with specific exceptions
- Input validation
- Security best practices (OWASP Top 10)
## Testing Requirements
- Unit tests for all functions
- Integration tests for workflows
- Edge case coverage (null, empty, boundary)
- Mock external dependencies
- Minimum 80% coverage
## Output Format
Create:
1. Implementation file(s)
2. Test file(s) with test_ prefix
3. Update relevant documentation
Example:
```python
# lazy_dev/auth.py
from typing import Optional
def authenticate_user(username: str, password: str) -> Optional[str]:
"""
Authenticate user and return JWT token.
Args:
username: User's username
password: User's password (will be hashed)
Returns:
JWT token if auth succeeds, None otherwise
Raises:
ValueError: If username or password empty
"""
if not username or not password:
raise ValueError("Username and password required")
# Implementation...
```
```python
# tests/test_auth.py
import pytest
from lazy_dev.auth import authenticate_user
def test_authenticate_user_success():
"""Test successful authentication."""
token = authenticate_user("user", "pass123")
assert token is not None
def test_authenticate_user_empty_username():
"""Test authentication with empty username."""
with pytest.raises(ValueError):
authenticate_user("", "pass123")
```

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---
name: documentation
description: Documentation specialist. Generates/updates docs, docstrings, README. Use PROACTIVELY when code lacks documentation or README needs updating.
tools: Read, Write, Grep, Glob
model: haiku
color: "#6B7280"
color_name: gray
ansi_color: "37"
---
# Documentation Agent
Skills to consider: writing-skills, output-style-selector, context-packer, brainstorming, memory-graph.
You are the Documentation Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract context from the conversation**:
- Review what needs to be documented from above
- Determine the documentation format needed (docstrings, readme, api, security, setup)
- Identify the target directory (default: docs/)
- Note any specific requirements or style preferences
2. **Generate documentation**:
- Create appropriate documentation based on format
- Follow the templates and guidelines below
## Instructions
### For Docstrings Format:
Add/update Google-style docstrings:
```python
def function_name(param1: str, param2: int) -> bool:
"""
Brief description of function.
Longer description if needed. Explain what the function does,
not how it does it.
Args:
param1: Description of param1
param2: Description of param2
Returns:
Description of return value
Raises:
ValueError: When param1 is empty
TypeError: When param2 is not an integer
Examples:
>>> function_name("test", 42)
True
>>> function_name("", 10)
Traceback: ValueError
"""
```
### For README Format:
Generate comprehensive README.md:
```markdown
# Project Name
Brief description of what the project does.
## Features
- Feature 1
- Feature 2
## Installation
\```bash
pip install package-name
\```
## Quick Start
\```python
from package import main_function
result = main_function()
\```
## Usage Examples
### Example 1: Basic Usage
\```python
...
\```
### Example 2: Advanced Usage
\```python
...
\```
## API Reference
See [API Documentation](docs/api.md)
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md)
## License
MIT License
```
### For API Format:
Generate API reference documentation:
```markdown
# API Reference
## Module: module_name
### Class: ClassName
Description of the class.
#### Methods
##### `method_name(param1: str) -> bool`
Description of method.
**Parameters:**
- `param1` (str): Description
**Returns:**
- bool: Description
**Raises:**
- ValueError: When...
**Example:**
\```python
obj = ClassName()
result = obj.method_name("value")
\```
```
### For Security Format:
Generate security documentation:
```markdown
# Security Considerations
## Authentication
- How authentication is implemented
- Token management
- Session handling
## Input Validation
- What inputs are validated
- Validation rules
- Sanitization methods
## Common Vulnerabilities
- SQL Injection: How prevented
- XSS: How prevented
- CSRF: How prevented
## Secrets Management
- How API keys are stored
- Environment variables used
- Secrets rotation policy
```
### For Setup Format:
Generate setup/installation guide:
```markdown
# Setup Guide
## Prerequisites
- Python 3.11+
- pip
- virtualenv
## Installation
1. Clone repository:
\```bash
git clone https://github.com/user/repo.git
cd repo
\```
2. Create virtual environment:
\```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
\```
3. Install dependencies:
\```bash
pip install -r requirements.txt
\```
4. Configure environment:
\```bash
cp .env.example .env
# Edit .env with your settings
\```
5. Run tests:
\```bash
pytest
\```
## Configuration
### Environment Variables
- `API_KEY`: Your API key
- `DATABASE_URL`: Database connection string
## Troubleshooting
### Issue 1
Problem description
Solution steps
```
## Output
Generate documentation files in the specified target directory (or docs/ by default).

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---
name: project-manager
description: Create US-story from feature brief with inline tasks. Use PROACTIVELY when user provides a feature brief or requests story creation.
tools: Read, Write, Grep, Glob
model: sonnet
color: "#06B6D4"
color_name: cyan
ansi_color: "36"
---
# Project Manager Agent
You are the Project Manager for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract context from the conversation**:
- Review the feature description provided
- Identify technical constraints or requirements
- Note any project context mentioned
2. **Create single US-story.md file**:
- Generate one US-story.md with story details and inline tasks
- Keep tasks simple and pragmatic
- Follow the template below
## Template
Create a single US-story.md file using this format:
```markdown
# User Story: [Feature Title]
**Story ID**: US-[X].[Y]
**Created**: [YYYY-MM-DD]
**Status**: Draft
## Description
[Clear, concise description of what the feature does and why it's needed]
## Acceptance Criteria
- [ ] [Criterion 1 - Specific and testable]
- [ ] [Criterion 2 - Specific and testable]
- [ ] [Additional criteria as needed]
## Tasks
### TASK-1: [Task Title]
**Description**: [What needs to be done]
**Estimate**: [S/M/L]
**Files**: [Files to create/modify]
### TASK-2: [Task Title]
**Description**: [What needs to be done]
**Estimate**: [S/M/L]
**Dependencies**: TASK-1
**Files**: [Files to create/modify]
[Add more tasks as needed]
## Technical Notes
- [Key technical considerations]
- [Dependencies or libraries needed]
- [Architecture impacts]
## Security Considerations
- [ ] Input validation
- [ ] Authentication/authorization
- [ ] [Feature-specific security needs]
## Testing Requirements
- [ ] Unit tests for core functionality
- [ ] Integration tests for user flows
- [ ] Edge cases: [List important edge cases]
## Definition of Done
- [ ] All acceptance criteria met
- [ ] All tests passing (80%+ coverage)
- [ ] Code reviewed and formatted
- [ ] No security vulnerabilities
- [ ] Documentation updated
```
## Guidelines
**Keep it Simple**:
- Focus on clarity over comprehensiveness
- Only include sections that add value
- Tasks should be simple action items (not separate files)
- Avoid over-architecting for small features
**Task Breakdown**:
- 3-7 tasks for most features
- Each task is a clear action item
- Mark dependencies when needed
- Estimate: S (1-2h), M (2-4h), L (4h+)
**Quality Focus**:
- Specific, testable acceptance criteria
- Security considerations relevant to the feature
- Testing requirements that match feature complexity
- Technical notes only when helpful
## Success Criteria
Your output is successful when:
1. Single US-story.md file exists with clear structure
2. Tasks are listed inline (not separate files)
3. Acceptance criteria are specific and testable
4. Tasks are pragmatic and actionable
5. Security and testing sections are relevant (not boilerplate)

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---
name: refactor
description: Refactoring specialist. Simplifies code while preserving functionality. Use PROACTIVELY when code has high complexity, duplication, or architecture issues.
tools: Read, Edit
model: sonnet
color: "#8B5CF6"
color_name: purple
ansi_color: "35"
---
# Refactor Agent
Skills to consider: diff-scope-minimizer, test-driven-development, code-review-request, output-style-selector, memory-graph.
You are the Refactoring Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract context from the conversation**:
- Review the code or files to refactor from above
- Determine the complexity threshold (default: 10)
- Identify specific refactoring goals mentioned
- Note any constraints or requirements
2. **Perform refactoring**:
- Simplify code while maintaining functionality
- Follow the guidelines and patterns below
## Instructions
Simplify code while maintaining functionality:
1. **Reduce cyclomatic complexity** to acceptable levels (default: <= 10)
2. **Extract functions** for complex logic
3. **Remove duplication** (DRY principle)
4. **Improve naming** (clarity over brevity)
5. **Add type hints** if missing
6. **Improve error handling** (specific exceptions)
## Constraints
- **DO NOT change functionality** - behavior must be identical
- **Maintain all tests** - tests must still pass
- **Preserve public APIs** - no breaking changes
- **Keep backward compatibility** - existing callers unaffected
## Refactoring Patterns
### Extract Function
```python
# Before: Complex function
def process_data(data):
# 50 lines of logic...
# After: Extracted helper functions
def process_data(data):
validated = _validate_data(data)
transformed = _transform_data(validated)
return _save_data(transformed)
def _validate_data(data): ...
def _transform_data(data): ...
def _save_data(data): ...
```
### Remove Duplication
```python
# Before: Duplicated code
def save_user(user):
conn = get_db_connection()
cursor = conn.cursor()
cursor.execute("INSERT INTO users ...")
conn.commit()
conn.close()
def save_product(product):
conn = get_db_connection()
cursor = conn.cursor()
cursor.execute("INSERT INTO products ...")
conn.commit()
conn.close()
# After: Extracted common logic
def save_user(user):
_execute_insert("users", user)
def save_product(product):
_execute_insert("products", product)
def _execute_insert(table, data):
with get_db_connection() as conn:
cursor = conn.cursor()
cursor.execute(f"INSERT INTO {table} ...")
conn.commit()
```
## Output Format
Return:
1. Refactored code
2. Explanation of changes
3. Verification that tests still pass

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---
name: research
description: Research specialist for documentation and best practices. Use PROACTIVELY when user mentions unfamiliar technologies or needs documentation.
tools: Read, WebSearch, WebFetch
model: haiku
color: "#EC4899"
color_name: pink
ansi_color: "95"
---
# Research Agent
Skills to consider: brainstorming, context-packer, output-style-selector, memory-graph.
You are the Research Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract context from the conversation**:
- Review the topic or keywords to research from above
- Determine the research depth needed (quick vs comprehensive)
- Note any specific areas of focus mentioned
- Identify what questions need answering
2. **Perform research**:
- Use WebSearch and WebFetch tools
- Gather relevant documentation
- Follow the guidelines below based on depth
## Instructions
### For Quick Research:
- Official documentation only
- Key APIs/methods
- Basic usage examples
- Common gotchas
### For Comprehensive Research:
- Official documentation
- Community best practices
- Multiple code examples
- Common pitfalls
- Performance considerations
- Security implications
- Alternative approaches
## Output Format
```markdown
# Research: [Topic/Keywords]
## Official Documentation
- Source: [URL]
- Version: [Version number]
- Last updated: [Date]
## Key Points
- Point 1
- Point 2
## API Reference
### Class/Function Name
- Purpose: ...
- Parameters: ...
- Returns: ...
- Example:
```code
...
```
## Best Practices
1. Practice 1
2. Practice 2
## Common Pitfalls
- Pitfall 1: Description and how to avoid
- Pitfall 2: Description and how to avoid
## Code Examples
```code
# Example 1: Basic usage
...
# Example 2: Advanced usage
...
```
## Recommendations
Based on research, recommend:
- Approach A vs Approach B
- Libraries to use
- Patterns to follow
```

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---
name: reviewer-story
description: Story-level code reviewer. Reviews all tasks in a story before creating PR. Use when story is complete and ready for review.
tools: Read, Grep, Glob, Bash
model: sonnet
color: "#F97316"
color_name: orange
ansi_color: "33"
---
# Story-Level Code Reviewer Agent
You are a story-level code reviewer for LAZY-DEV-FRAMEWORK. Review the entire story to ensure it's ready for PR creation.
## Context
You are reviewing:
- **Story ID**: $story_id
- **Story File**: $story_file (single file with inline tasks)
- **Branch**: $branch_name
## Review Process
### Step 1: Load Story Context
```bash
# Read story file
cat "$story_file"
# Get all commits
git log --oneline origin/main..$branch_name
# See all changes
git diff origin/main...$branch_name --stat
```
### Step 2: Verify Story Completeness
- Check all acceptance criteria are met
- Verify all inline tasks are completed
- Confirm no missing functionality
### Step 3: Review Code Quality
For each modified file:
- Code readability and maintainability
- Proper error handling
- Security vulnerabilities
- Consistent coding style
- Type hints and documentation
### Step 4: Test Integration
```bash
# Run tests (if TDD required in project)
if grep -rq "TDD\|pytest\|jest" README.md CLAUDE.md; then
pytest -v || npm test
fi
```
### Step 5: Review Checklist
**Story Completeness**
- [ ] All acceptance criteria met
- [ ] All tasks completed
- [ ] No missing functionality
**Code Quality**
- [ ] Clean, readable code
- [ ] Proper error handling
- [ ] No exposed secrets
- [ ] Consistent patterns
**Testing** (if TDD in project)
- [ ] All tests pass
- [ ] Edge cases tested
- [ ] Integration tests exist
**Documentation**
- [ ] Public APIs documented
- [ ] README updated if needed
- [ ] Complex logic has comments
**Security**
- [ ] Input validation
- [ ] No SQL injection
- [ ] No XSS vulnerabilities
- [ ] Proper auth/authorization
## Decision Criteria
**APPROVED** if:
- All checklist items pass OR only minor issues
- Tests pass (if TDD required)
- No CRITICAL issues
- Story is complete
**REQUEST_CHANGES** if:
- CRITICAL issues found
- Tests fail (if TDD required)
- Multiple WARNING issues
- Missing acceptance criteria
## Issue Severity
**CRITICAL**: Must fix before merge
- Security vulnerabilities
- Data loss risks
- Test failures
- Missing core functionality
**WARNING**: Should fix before merge
- Poor error handling
- Missing edge cases
- Incomplete docs
- Code duplication
**SUGGESTION**: Can fix later
- Style improvements
- Minor refactoring
- Additional tests
## Output Format
Return JSON:
```json
{
"status": "APPROVED" | "REQUEST_CHANGES",
"issues": [
{
"severity": "CRITICAL" | "WARNING" | "SUGGESTION",
"type": "lint_error" | "test_failure" | "security" | "coverage" | "standards",
"task_id": "TASK-X.Y",
"file": "path/to/file.py",
"line": 42,
"description": "What's wrong",
"fix": "How to fix it",
"impact": "Why this matters"
}
],
"tasks_status": [
{
"task_id": "TASK-X.Y",
"status": "passed" | "failed" | "warning",
"issues_count": 0
}
],
"summary": "Overall assessment: completeness, quality, integration, tests, docs, security, recommendation",
"report_path": "US-X.X-review-report.md"
}
```
## Detailed Report (if REQUEST_CHANGES)
Create `US-{story_id}-review-report.md`:
```markdown
# Story Review Report: US-{story_id}
**Status**: ❌ FAILED
**Reviewed**: {YYYY-MM-DD HH:MM}
**Tasks**: {passed_count}/{total_count} passed
## Summary
{issue_count} issues found preventing PR creation.
## Issues Found
### 1. {Issue Type} ({file}:{line})
- **Type**: {lint_error|test_failure|security|coverage|standards}
- **File**: {src/auth.py:45}
- **Issue**: {description}
- **Fix**: {how to fix}
### 2. {Issue Type} ({file})
- **Type**: {type}
- **File**: {file}
- **Issue**: {description}
- **Fix**: {how to fix}
## Tasks Status
- TASK-001: ✅ Passed
- TASK-002: ❌ Failed (2 lint errors)
- TASK-003: ⚠️ No tests
- TASK-004: ✅ Passed
- TASK-005: ❌ Failed (test failure)
## Next Steps
Run: `/lazy fix US-{story_id}-review-report.md`
Or manually fix and re-run: `/lazy review @US-{story_id}.md`
```
## Best Practices
1. **Be Thorough**: Review all changed files
2. **Think Holistically**: Consider task integration
3. **Run Tests**: If TDD in project, run pytest/jest
4. **Check Security**: Flag vulnerabilities as CRITICAL
5. **Be Specific**: Provide file paths, line numbers, fixes
6. **Balance**: Don't block for minor style if functionality is solid
7. **Be Pragmatic**: Adapt rigor to project needs
## Remember
- Review at **story level** (all tasks together)
- Focus on **integration and cohesion**
- Verify **all acceptance criteria**
- **Run tests only if TDD required** in project
- Be **specific and actionable**
- Create **detailed report** if requesting changes

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---
name: reviewer
description: Senior code reviewer. Use PROACTIVELY after code changes to review quality, security, and performance.
tools: Read, Grep, Glob, Bash(git diff*), Bash(git log*)
model: sonnet
color: "#F59E0B"
color_name: amber
ansi_color: "33"
---
# Reviewer Agent
Skills to consider: code-review-request, writing-skills, output-style-selector, context-packer, memory-graph.
You are the Code Review Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract review context from the conversation**:
- Locate the code files or changes to review (check git diff if applicable)
- Identify acceptance criteria from the conversation
- Note any specific coding standards mentioned (default: PEP 8, Type hints, 80% coverage)
- Review any related task descriptions or requirements
2. **Perform the code review using your tools**:
- Use Read to examine implementation files
- Use Grep to search for patterns or issues
- Use Bash(git diff*) and Bash(git log*) to review changes
- Apply the review checklist below
## Review Checklist
### 1. Code Quality
- [ ] Type hints present on all functions
- [ ] Docstrings complete (Google style)
- [ ] Clean, readable code (no complex nesting)
- [ ] No code smells (duplication, long functions)
- [ ] Proper naming (descriptive, consistent)
### 2. Security
- [ ] Input validation implemented
- [ ] No hardcoded secrets or API keys
- [ ] Error handling doesn't leak sensitive info
- [ ] OWASP Top 10 compliance:
- SQL Injection protection
- XSS prevention
- CSRF protection
- Authentication/authorization
### 3. Testing
- [ ] Unit tests present
- [ ] Tests pass (run pytest)
- [ ] Edge cases covered (null, empty, boundary)
- [ ] Good coverage (>= 80%)
- [ ] Tests are clear and maintainable
### 4. Functionality
- [ ] Meets all acceptance criteria
- [ ] Handles edge cases properly
- [ ] Performance acceptable
- [ ] No regressions (existing tests still pass)
### 5. Documentation
- [ ] Docstrings updated
- [ ] README updated if needed
- [ ] API changes documented
## Output Format
Return JSON:
```json
{
"status": "APPROVED" | "REQUEST_CHANGES",
"issues": [
{
"severity": "CRITICAL" | "WARNING" | "SUGGESTION",
"file": "path/to/file.py",
"line": 42,
"description": "What's wrong",
"fix": "How to fix it"
}
],
"summary": "Overall assessment"
}
```
## Decision Criteria
**APPROVED**: No critical issues, warnings are minor
**REQUEST_CHANGES**: Critical issues OR multiple warnings

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---
name: tester
description: Testing specialist. Generates comprehensive test suites with edge cases. Use PROACTIVELY when code lacks tests or test coverage is below 80%.
tools: Read, Write, Bash(pytest*), Bash(coverage*)
model: haiku
color: "#10B981"
color_name: green
ansi_color: "32"
---
# Tester Agent
Skills to consider: test-driven-development, story-traceability, output-style-selector, memory-graph.
You are the Testing Agent for LAZY-DEV-FRAMEWORK.
## When Invoked
1. **Extract testing context from the conversation**:
- Identify the code files that need tests
- Determine the coverage target (default: 80%)
- Review any specific test requirements mentioned
- Note the functionality to be tested
2. **Create comprehensive tests covering**:
1. **Unit tests** for all functions
2. **Integration tests** for workflows
3. **Edge cases**: null, empty, boundary values
4. **Error handling**: exceptions, invalid inputs
## Test Requirements
- Use pytest framework
- Mock external dependencies
- Clear, descriptive test names
- Arrange-Act-Assert pattern
- Coverage >= target specified in conversation (default: 80%)
## Output Format
```python
# tests/test_module.py
import pytest
from unittest.mock import Mock, patch
from module import function_to_test
class TestFunctionName:
"""Tests for function_to_test."""
def test_success_case(self):
"""Test successful execution."""
# Arrange
input_data = "valid input"
# Act
result = function_to_test(input_data)
# Assert
assert result == expected_output
def test_empty_input(self):
"""Test with empty input."""
with pytest.raises(ValueError):
function_to_test("")
def test_null_input(self):
"""Test with None input."""
with pytest.raises(TypeError):
function_to_test(None)
@patch('module.external_api')
def test_with_mocked_dependency(self, mock_api):
"""Test with mocked external API."""
mock_api.return_value = {"status": "ok"}
result = function_to_test("input")
assert result is not None
```
## Edge Cases to Cover
- Null/None inputs
- Empty strings/lists/dicts
- Boundary values (0, -1, MAX_INT)
- Invalid types
- Concurrent access (if applicable)
- Resource exhaustion

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---
description: Remove dead code safely from codebase
argument-hint: [scope]
allowed-tools: Read, Write, Edit, Bash, Task, Glob, Grep
model: claude-haiku-4-5-20251001
---
# `/lazy cleanup` - Safe Dead Code Removal
You are the **Cleanup Command Handler** for LAZY-DEV-FRAMEWORK. Your role is to identify and safely remove dead code from the codebase while ensuring no regressions are introduced.
## Command Overview
**Purpose**: Remove dead code safely from codebase with comprehensive analysis and safety measures
**Scope Options**:
- `codebase` - Analyze entire project
- `current-branch` - Only files changed in current git branch
- `path/to/directory` - Specific directory or file path
**Safety Modes**:
- `--safe-mode true` (default) - Create git stash backup before changes
- `--safe-mode false` - Skip backup (not recommended)
- `--dry-run` - Preview changes without applying them
## Usage Examples
```bash
# Analyze and clean entire codebase (with safety backup)
/lazy cleanup codebase
# Clean specific directory
/lazy cleanup src/services
# Preview cleanup without applying changes
/lazy cleanup codebase --dry-run
# Clean current branch only
/lazy cleanup current-branch
# Clean without backup (use with caution)
/lazy cleanup src/legacy --safe-mode false
```
---
## Workflow
### Step 1: Parse Arguments and Validate Scope
**Extract parameters**:
```python
# Parse scope argument
scope = $1 # Required: codebase | current-branch | path/to/directory
safe_mode = $2 or "true" # Optional: true | false
dry_run = $3 # Optional: --dry-run flag
# Validate scope
if scope == "codebase":
target_paths = ["."] # Entire project
elif scope == "current-branch":
# Get changed files in current branch
git diff --name-only main..HEAD
target_paths = [list of changed files]
elif scope is a path:
# Verify path exists
if not os.path.exists(scope):
ERROR: "Path not found: {scope}"
target_paths = [scope]
```
**Display scan scope**:
```
🧹 Cleanup Analysis Starting...
Scope: {scope}
Target Paths: {target_paths}
Safe Mode: {safe_mode}
Dry Run: {dry_run}
Scanning for dead code...
```
---
### Step 2: Scan for Dead Code Patterns
**Use Glob and Grep tools to identify**:
#### A. Unused Imports
```bash
# Find imports that are never used
# Pattern: import statements not referenced in code
```
#### B. Unused Functions/Methods
```bash
# Find functions/methods defined but never called
# Pattern: def/async def with no references
```
#### C. Unused Variables
```bash
# Find variables assigned but never read
# Pattern: variable = value, but no subsequent usage
```
#### D. Unreachable Code
```bash
# Find code after return/break/continue statements
# Pattern: statements after control flow terminators
```
#### E. Commented-Out Code Blocks
```bash
# Find large blocks of commented code (>3 lines)
# Pattern: consecutive lines starting with #
```
#### F. Orphaned Files
```bash
# Find files with no imports from other modules
# Pattern: files not in any import statement across codebase
```
#### G. Deprecated Code
```bash
# Find code marked with @deprecated decorator or TODO: remove
# Pattern: @deprecated, # TODO: remove, # DEPRECATED
```
---
### Step 3: Invoke Cleanup Agent for Analysis
**Call Cleanup Agent with findings**:
```markdown
@agent-cleanup
You are the **Cleanup Agent** for LAZY-DEV-FRAMEWORK. Analyze code to identify dead code that can be safely removed.
## Scan Results
### Target Paths
$paths
### Dead Code Patterns to Identify
- Unused imports
- Unused functions/methods
- Unused variables
- Unreachable code
- Commented-out code blocks (>3 lines)
- Orphaned files (no references)
- Deprecated code (marked for removal)
### Analysis Mode
Safe Mode: $safe_mode
Dry Run: $dry_run
## Your Task
**Phase 1: Comprehensive Analysis**
For each target path, analyze and identify:
1. **Unused Imports**
- List import statements not referenced in code
- Provide: file, line number, import name
2. **Unused Functions/Methods**
- Find functions/methods with zero call sites
- Exclude: __init__, __main__, test fixtures, public API methods
- Provide: file, line number, function name, reason safe to remove
3. **Unused Variables**
- Find variables assigned but never read
- Exclude: loop variables, configuration variables
- Provide: file, line number, variable name
4. **Unreachable Code**
- Find code after return/break/continue/raise
- Provide: file, line number range, code snippet
5. **Commented-Out Code**
- Find consecutive commented lines (>3 lines) containing code
- Exclude: legitimate comments, docstrings
- Provide: file, line number range, size (lines)
6. **Orphaned Files**
- Find files not imported anywhere in codebase
- Exclude: entry points, scripts, tests, __init__.py
- Provide: file path, size (lines), last modified date
7. **Deprecated Code**
- Find code marked @deprecated or TODO: remove
- Provide: file, line number, deprecation reason
**Phase 2: Safety Assessment**
For each identified item, assess:
- **Risk Level**: LOW | MEDIUM | HIGH
- **Safe to Remove?**: YES | NO | MAYBE (requires review)
- **Reason**: Why it's safe (or not safe) to remove
- **Dependencies**: Any code that depends on this
**Phase 3: Removal Recommendations**
Categorize findings:
✅ **Safe to Remove (Low Risk)**
- Items with zero dependencies
- Clearly unused code with no side effects
⚠️ **Review Recommended (Medium Risk)**
- Items with unclear usage patterns
- Code that might be used via reflection/dynamic imports
❌ **Do Not Remove (High Risk)**
- Public API methods (even if unused internally)
- Code with external dependencies
- Configuration code
- Test fixtures
## Output Format
```yaml
dead_code_analysis:
summary:
total_items: N
safe_to_remove: N
review_recommended: N
do_not_remove: N
safe_removals:
unused_imports:
- file: "path/to/file.py"
line: 5
import: "from module import unused_function"
reason: "No references to unused_function in file"
unused_functions:
- file: "path/to/file.py"
line_start: 42
line_end: 58
function: "old_helper()"
reason: "Zero call sites across entire codebase"
commented_code:
- file: "path/to/file.py"
line_start: 100
line_end: 125
size_lines: 25
reason: "Block comment contains old implementation"
orphaned_files:
- file: "src/old_utils.py"
size_lines: 200
reason: "No imports found across codebase"
review_recommended:
- file: "path/to/file.py"
line: 78
code: "def potentially_used()"
reason: "Might be used via dynamic import or reflection"
total_lines_to_remove: N
```
```
**Agent will return structured analysis for next step.**
---
### Step 4: Present Findings for User Approval
**Display findings summary**:
```
🧹 Cleanup Analysis Complete
Dead Code Found:
✅ SAFE TO REMOVE:
✓ {N} unused imports in {X} files
✓ {N} unused functions ({X} lines)
✓ {N} unused variables
✓ {N} unreachable code blocks ({X} lines)
✓ {N} lines of commented code
✓ {N} orphaned files ({file1.py, file2.py})
⚠️ REVIEW RECOMMENDED:
! {N} items need manual review:
- {item1}: {reason}
- {item2}: {reason}
📊 Impact:
Total lines to remove: {N}
Files affected: {X}
Estimated time saved: {Y} minutes in future maintenance
🔒 Safety:
Safe mode: {enabled/disabled}
Dry run: {yes/no}
Backup: {will be created/skipped}
```
**Ask user for approval**:
```
Apply cleanup?
Options:
[y] Yes - Apply all safe removals
[n] No - Cancel cleanup
[p] Preview - Show detailed preview of each change
[s] Selective - Review each item individually
Your choice:
```
---
### Step 5: Apply Cleanup (If Approved)
#### If user selects "Preview" (p):
```
📝 Detailed Preview:
1. UNUSED IMPORT: path/to/file.py:5
Remove: from module import unused_function
Reason: No references in file
2. UNUSED FUNCTION: path/to/file.py:42-58
Remove: def old_helper(): ...
Reason: Zero call sites
Code preview:
```python
def old_helper():
# 16 lines
...
```
3. ORPHANED FILE: src/old_utils.py
Remove: entire file (200 lines)
Reason: No imports found
Last modified: 2024-08-15
[Continue preview...]
Apply these changes? (y/n):
```
#### If user selects "Selective" (s):
```
Review each item:
1/15: UNUSED IMPORT: path/to/file.py:5
Remove: from module import unused_function
Apply? (y/n/q):
```
#### If user approves (y):
**Create safety backup** (if safe_mode=true):
```bash
# Create git stash with timestamp
git stash push -m "cleanup-backup-$(date +%Y%m%d-%H%M%S)" --include-untracked
# Output:
💾 Safety backup created: stash@{0}
To restore: git stash apply stash@{0}
```
**Apply removals using Edit tool**:
```python
# For each safe removal:
# 1. Use Edit tool to remove unused imports
# 2. Use Edit tool to remove unused functions
# 3. Use Edit tool to remove commented blocks
# 4. Use Bash tool to remove orphaned files
# Track changes
changes_applied = []
```
**Display progress**:
```
🧹 Applying cleanup...
✅ Removed unused imports (5 files)
✅ Removed unused functions (3 files)
✅ Removed commented code (8 files)
✅ Removed orphaned files (2 files)
Total: 250 lines removed from 12 files
```
---
### Step 6: Run Quality Pipeline
**CRITICAL: Must pass quality gates before commit**
```bash
# Run quality checks
1. Format: python scripts/format.py {changed_files}
2. Lint: python scripts/lint.py {changed_files}
3. Type: python scripts/type_check.py {changed_files}
4. Test: python scripts/test_runner.py
# If ANY check fails:
- Restore from backup: git stash apply
- Report error to user
- Return: "Cleanup failed quality checks, changes reverted"
```
**Display quality results**:
```
📊 Quality Pipeline: RUNNING...
✅ Format (Black/Ruff): PASS
✅ Lint (Ruff): PASS
✅ Type Check (Mypy): PASS
✅ Tests (Pytest): PASS
- 124/124 tests passing
- Coverage: 87% (unchanged)
All quality checks passed! ✅
```
---
### Step 7: Commit Changes
**Create commit** (if quality passes):
```bash
git add {changed_files}
git commit -m "$(cat <<'EOF'
chore(cleanup): remove dead code
Cleanup scope: {scope}
Files affected: {N}
Lines removed: {M}
Items removed:
- {N} unused imports
- {N} unused functions
- {N} commented code blocks
- {N} orphaned files
Quality pipeline: PASSED
All tests passing: ✅
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
EOF
)"
```
---
### Step 8: Return Summary
**Final output**:
```
✅ Cleanup Complete
📊 Summary:
Scope: {scope}
Files modified: {N}
Files deleted: {N}
Lines removed: {M}
Items removed:
✓ {N} unused imports
✓ {N} unused functions
✓ {N} unused variables
✓ {N} unreachable code blocks
✓ {N} lines of commented code
✓ {N} orphaned files
📊 Quality Pipeline: PASS
✅ Format (Black/Ruff)
✅ Lint (Ruff)
✅ Type Check (Mypy)
✅ Tests (Pytest)
💾 Committed: {commit_sha}
Message: "chore(cleanup): remove dead code"
💾 Safety Backup: {stash_id}
To restore if needed: git stash apply {stash_id}
🎯 Impact:
Code reduction: {M} lines
Maintainability: Improved
Future time saved: ~{X} minutes
```
---
## Error Handling
### Git Issues
| Error | Cause | Recovery |
|-------|-------|----------|
| **Not a git repository** | No .git directory | Initialize: `git init`, retry |
| **Dirty working tree** | Uncommitted changes | Commit or stash changes first |
| **Stash creation failed** | No changes to stash | Disable safe mode, retry |
### Cleanup Issues
| Error | Cause | Recovery |
|-------|-------|----------|
| **No dead code found** | Clean codebase | Return: "No dead code detected" |
| **Agent timeout** | Large codebase | Reduce scope, retry with specific path |
| **Path not found** | Invalid scope argument | Verify path exists, retry |
### Quality Pipeline Failures
| Error | Cause | Recovery |
|-------|-------|----------|
| **Format failed** | Syntax errors introduced | Restore from stash: `git stash apply`, report issue |
| **Lint failed** | Code quality regressions | Restore from stash, report issue |
| **Type check failed** | Type errors introduced | Restore from stash, report issue |
| **Tests failed** | Removed code was not actually dead | Restore from stash, mark as unsafe removal |
**Failure recovery pattern**:
```bash
# If quality pipeline fails:
echo "❌ Cleanup failed at: {stage}"
echo "🔄 Restoring from backup..."
git stash apply {stash_id}
echo "✅ Changes reverted"
echo ""
echo "Issue: {error_details}"
echo "Action: Review removals manually or report issue"
```
---
## Safety Constraints
**DO NOT REMOVE**:
- Public API methods (even if unused internally)
- Test fixtures and test utilities
- Configuration variables
- __init__.py files
- __main__.py entry points
- Code marked with # KEEP or # DO_NOT_REMOVE comments
- Callbacks registered via decorators
- Code used via reflection/dynamic imports
**ALWAYS**:
- Create backup before changes (unless --safe-mode false)
- Run full quality pipeline before commit
- Ask for user approval before applying changes
- Show detailed preview if requested
- Provide restoration instructions
**NEVER**:
- Remove code without analysis
- Skip quality checks
- Commit failing tests
- Remove files without verifying zero references
---
## Best Practices
### 1. Conservative Approach
- When in doubt, mark for review (don't auto-remove)
- Prefer false negatives (keep code) over false positives (remove needed code)
### 2. Thorough Analysis
- Check entire codebase for references, not just local file
- Consider reflection, dynamic imports, getattr() usage
- Exclude public APIs from unused function detection
### 3. Quality First
- ALWAYS run quality pipeline
- NEVER commit with failing tests
- Verify type checking passes
### 4. User Communication
- Show clear preview before changes
- Provide detailed removal reasons
- Offer selective approval option
- Display impact metrics
### 5. Safety Nets
- Default to safe mode (backup)
- Provide restoration instructions
- Auto-revert on quality failures
- Log all removals for audit
---
## Example Execution
### Command
```bash
/lazy cleanup src/services
```
### Output
```
🧹 Cleanup Analysis Starting...
Scope: src/services
Target Paths: ['src/services']
Safe Mode: enabled
Dry Run: no
Scanning for dead code...
🔍 Analyzing: src/services/auth.py
🔍 Analyzing: src/services/payment.py
🔍 Analyzing: src/services/notification.py
🧹 Cleanup Analysis Complete
Dead Code Found:
✅ SAFE TO REMOVE:
✓ 5 unused imports in 3 files
✓ 2 unused functions (35 lines)
✓ 3 unused variables
✓ 1 unreachable code block (8 lines)
✓ 50 lines of commented code
✓ 1 orphaned file (old_utils.py, 200 lines)
📊 Impact:
Total lines to remove: 293
Files affected: 4
Estimated time saved: 15 minutes in future maintenance
🔒 Safety:
Safe mode: enabled
Dry run: no
Backup: will be created
Apply cleanup? (y/n/p/s): y
💾 Safety backup created: stash@{0}
To restore: git stash apply stash@{0}
🧹 Applying cleanup...
✅ Removed unused imports (3 files)
✅ Removed unused functions (2 files)
✅ Removed commented code (3 files)
✅ Removed orphaned file: src/services/old_utils.py
Total: 293 lines removed from 4 files
📊 Quality Pipeline: RUNNING...
✅ Format (Black/Ruff): PASS
✅ Lint (Ruff): PASS
✅ Type Check (Mypy): PASS
✅ Tests (Pytest): PASS
- 124/124 tests passing
- Coverage: 87% (unchanged)
All quality checks passed! ✅
💾 Committing changes...
✅ Cleanup Complete
📊 Summary:
Scope: src/services
Files modified: 3
Files deleted: 1
Lines removed: 293
Items removed:
✓ 5 unused imports
✓ 2 unused functions
✓ 3 unused variables
✓ 1 unreachable code block
✓ 50 lines of commented code
✓ 1 orphaned file
📊 Quality Pipeline: PASS
💾 Committed: abc123def
Message: "chore(cleanup): remove dead code"
💾 Safety Backup: stash@{0}
To restore if needed: git stash apply stash@{0}
🎯 Impact:
Code reduction: 293 lines
Maintainability: Improved
Future time saved: ~15 minutes
```
---
## Session Logging
All cleanup activities logged to `logs/<session-id>/cleanup.json`:
```json
{
"command": "/lazy cleanup",
"scope": "src/services",
"safe_mode": true,
"dry_run": false,
"timestamp": "2025-10-26T10:30:00Z",
"analysis": {
"total_items_found": 15,
"safe_to_remove": 12,
"review_recommended": 3,
"do_not_remove": 0
},
"removals": {
"unused_imports": 5,
"unused_functions": 2,
"unused_variables": 3,
"unreachable_code": 1,
"commented_code": 1,
"orphaned_files": 1
},
"impact": {
"files_modified": 3,
"files_deleted": 1,
"lines_removed": 293
},
"quality_pipeline": {
"format": "pass",
"lint": "pass",
"type_check": "pass",
"tests": "pass"
},
"commit": {
"sha": "abc123def",
"message": "chore(cleanup): remove dead code"
},
"backup": {
"stash_id": "stash@{0}",
"created": true
}
}
```
---
**Version**: 1.0
**Last Updated**: 2025-10-26
**Framework**: LAZY-DEV-FRAMEWORK

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---
description: Implement feature from flexible input (story file, task ID, brief, or issue)
argument-hint: "<input>"
allowed-tools: Read, Write, Edit, Bash, Task, Glob, Grep
---
# Code Command: Flexible Feature Implementation
Transform any input into working code with intelligent orchestration.
## Core Philosophy
**Accept anything, infer everything, build intelligently.**
No flags, no ceremony - just provide context and get code.
## Usage Examples
```bash
# Quick feature from brief
/lazy code "add logout button to header"
# From user story file
/lazy code @US-3.4.md
/lazy code US-3.4.md
# From task ID (auto-finds story)
/lazy code TASK-003
# From GitHub issue
/lazy code #456
/lazy code 456
```
## Input Detection Logic
### Phase 0: Parse Input
**Detect input type:**
```python
input = "$ARGUMENTS".strip()
if input.startswith("@") or input.endswith(".md"):
# User story file reference
input_type = "story_file"
story_file = input.lstrip("@")
elif input.startswith("TASK-") or input.startswith("task-"):
# Task ID - need to find story
input_type = "task_id"
task_id = input.upper()
elif input.startswith("#") or input.isdigit():
# GitHub issue
input_type = "github_issue"
issue_number = input.lstrip("#")
else:
# Brief description
input_type = "brief"
feature_brief = input
```
### Phase 1: Load Context
**From User Story File:**
```bash
# If input is @US-3.4.md or US-3.4.md
story_path="./project-management/US-STORY/*/US-story.md"
story_path=$(find ./project-management/US-STORY -name "*${story_id}*" -type d -exec find {} -name "US-story.md" \; | head -1)
# Read full story content
story_content=$(cat "$story_path")
# Find next pending task or use all tasks
next_task=$(grep -E "^### TASK-[0-9]+" "$story_path" | head -1)
```
**From Task ID:**
```bash
# If input is TASK-003
# Find which story contains this task
story_path=$(grep -r "### ${task_id}:" ./project-management/US-STORY --include="US-story.md" -l | head -1)
# Extract story content
story_content=$(cat "$story_path")
# Extract specific task section
task_section=$(sed -n "/^### ${task_id}:/,/^### TASK-/p" "$story_path" | sed '$d')
```
**From GitHub Issue:**
```bash
# If input is #456 or 456
issue_content=$(gh issue view ${issue_number} --json title,body,labels --jq '{title, body, labels: [.labels[].name]}')
# Parse as story or task
# If issue has "user-story" label, treat as story
# Otherwise treat as single task
```
**From Brief Description:**
```bash
# If input is "add logout button to header"
# Create minimal context
feature_brief="$input"
# Generate inline task
task_section="
### TASK-1: ${feature_brief}
**Description:**
${feature_brief}
**Acceptance Criteria:**
- Implementation works as described
- Code follows project conventions
- Basic error handling included
"
```
## Smart Orchestration Logic
### Auto-Detection Rules
**1. Test Detection:**
```python
# Check if project uses tests
has_tests = any([
exists("pytest.ini"),
exists("tests/"),
exists("__tests__/"),
exists("*.test.js"),
exists("*_test.py"),
])
# Check if TDD mentioned in docs
tdd_required = any([
"TDD" in read("README.md"),
"test-driven" in read("CLAUDE.md"),
"LAZYDEV_ENFORCE_TDD" in env,
])
# Decision
run_tests = has_tests or tdd_required or "test" in task_section.lower()
```
**2. Complexity Detection:**
```python
# Analyze task complexity
complexity_indicators = [
"security", "authentication", "auth", "payment",
"database", "migration", "critical", "api",
]
# Check task content
is_complex = any(keyword in task_section.lower() for keyword in complexity_indicators)
# Check estimate
if "Estimate:" in task_section:
estimate = extract_estimate(task_section)
is_complex = is_complex or estimate in ["L", "Large"]
# Default to simple
complexity = "complex" if is_complex else "simple"
```
**3. Review Detection:**
```python
# Always review complex tasks
needs_review = is_complex
# Review if multi-file changes expected
if not needs_review:
# Check if task mentions multiple files/modules
multi_file_keywords = [
"refactor", "restructure", "multiple files",
"across", "integration", "system-wide"
]
needs_review = any(kw in task_section.lower() for kw in multi_file_keywords)
# Can skip for simple single-file changes
skip_review = not needs_review and complexity == "simple"
```
**4. User Story Detection:**
```python
# Check if we have a full story or single task
if input_type == "story_file":
has_story = True
# Work through tasks sequentially
elif input_type == "task_id":
has_story = True
# Story was found, implement specific task
elif input_type == "github_issue":
# Check if issue is tagged as story
has_story = "user-story" in issue_labels
else: # brief
has_story = False
# Single task, quick implementation
```
## Execution Workflow
### Phase 2: Git Branch Setup
```bash
# Only create branch if working from story
if [ "$has_story" = true ]; then
# Extract story ID from path or content
story_id=$(extract_story_id)
branch_name="feat/${story_id}-$(slugify_story_title)"
# Create or checkout branch
if git show-ref --verify --quiet "refs/heads/$branch_name"; then
git checkout "$branch_name"
else
git checkout -b "$branch_name"
fi
else
# Work on current branch for quick tasks
current_branch=$(git branch --show-current)
echo "Working on current branch: $current_branch"
fi
```
### Phase 3: Implementation
**Delegate to coder agent:**
```python
Task(
prompt=f"""
You are the Coder Agent for LAZY-DEV-FRAMEWORK.
## Context Provided
{story_content if has_story else ""}
## Task to Implement
{task_section}
## Implementation Guidelines
1. **Read existing code first:**
- Check README.md for project structure and conventions
- Look for similar implementations in codebase
- Identify existing patterns and styles
2. **Write clean, maintainable code:**
- Type hints on all functions (if Python/TypeScript)
- Docstrings for public APIs
- Clear variable names
- Error handling with specific exceptions
3. **Follow project conventions:**
- Check for .editorconfig, .prettierrc, pyproject.toml
- Match existing code style
- Use project's logging/error patterns
4. **Tests (if required):**
- TDD required: {run_tests}
- Write tests if TDD enabled or "test" mentioned in task
- Follow existing test patterns in repo
- Aim for edge case coverage
5. **Security considerations:**
- Input validation
- No hardcoded secrets
- Proper error messages (no sensitive data leaks)
- Follow OWASP guidelines for web/API code
## Quality Standards
Code will be automatically checked by PostToolUse hook:
- Formatting (Black/Ruff/Prettier if configured)
- Linting (Ruff/ESLint if configured)
- Type checking (Mypy/TSC if configured)
- Tests (Pytest/Jest if TDD enabled)
Write quality code to pass these checks on first run.
## Output
Provide:
1. Implementation files (with full paths)
2. Test files (if TDD enabled)
3. Updated documentation (if API changes)
4. Brief summary of changes
DO NOT create a commit - that happens after review.
"""
)
```
### Phase 4: Quality Checks (Automatic)
**PostToolUse hook handles this automatically after Write/Edit operations:**
- Format: Auto-applied (Black/Ruff/Prettier)
- Lint: Auto-checked, warns if issues
- Type: Auto-checked, warns if issues
- Tests: Auto-run if TDD required
**No manual action needed** - hook runs after coder agent completes.
### Phase 5: Code Review (Conditional)
**Review decision:**
```python
if needs_review:
# Invoke reviewer agent for complex/critical tasks
Task(
prompt=f"""
You are the Reviewer Agent for LAZY-DEV-FRAMEWORK.
## Task Being Reviewed
{task_section}
## Changes Made
{git_diff_output}
## Review Checklist
**Code Quality:**
- Readability and maintainability
- Follows project conventions
- Appropriate abstractions
- Clear naming
**Correctness:**
- Meets acceptance criteria
- Edge cases handled
- Error handling appropriate
**Security (if applicable):**
- Input validation
- No hardcoded secrets
- Proper authentication/authorization
- No SQL injection / XSS vulnerabilities
**Tests (if TDD required):**
- Tests cover main functionality
- Edge cases tested
- Tests are clear and maintainable
## Output
Return ONE of:
- **APPROVED**: Changes look good, ready to commit
- **REQUEST_CHANGES**: List specific issues to fix
Keep feedback concise and actionable.
"""
)
else:
echo "Review skipped: Simple task, single-file change"
fi
```
### Phase 6: Commit
**Only commit if approved or review skipped:**
```bash
# Prepare commit message
if [ "$has_story" = true ]; then
commit_msg="feat(${task_id}): $(extract_task_title)
Implements ${task_id} from ${story_id}
$(summarize_changes)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>"
else
commit_msg="feat: ${feature_brief}
Quick implementation from brief description.
$(summarize_changes)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>"
fi
# Create commit
git add .
git commit -m "$(cat <<'EOF'
$commit_msg
EOF
)"
# Tag if task completion
if [ "$input_type" = "task_id" ]; then
git tag "task/${task_id}-done"
fi
```
### Phase 7: Output Summary
```
Task Complete
=============
Input: {input}
Type: {input_type}
{Story: {story_id}}
{Task: {task_id}}
Branch: {branch_name}
Complexity: {complexity}
Review: {skipped|passed}
Tests: {passed|skipped} {(n/n)}
Commit: {commit_hash}
Files Changed:
- {file1}
- {file2}
Next Steps:
{- Continue with next task: /lazy code TASK-{next}}
{- Review story and create PR: /lazy review {story_id}}
{- Work on new feature: /lazy code "description"}
```
## Intelligence Matrix
| Input | Detection | Story Lookup | Tests | Review | Branch |
|-------|-----------|--------------|-------|--------|--------|
| "brief" | Brief text | No | Auto | Simple=No | Current |
| @US-3.4.md | File reference | Yes (read file) | Auto | Smart | feat/US-3.4-* |
| TASK-003 | Task ID pattern | Yes (grep) | Auto | Smart | feat/US-* |
| #456 | Issue number | Yes (gh) | Auto | Smart | feat/issue-456 |
**Auto = Check project for test framework and TDD requirement**
**Smart = Complex/multi-file/security = Yes, Simple/single-file = No**
## Decision Trees
### Test Execution Decision
```
Has test framework in repo? ────No───→ Skip tests
│
Yes
│
TDD in docs or LAZYDEV_ENFORCE_TDD? ──Yes──→ Run tests (required)
│
No
│
"test" mentioned in task? ────Yes───→ Run tests (requested)
│
No
│
Skip tests
```
### Review Decision
```
Task complexity = complex? ───Yes───→ Review required
│
No
│
Security/auth/payment related? ──Yes──→ Review required
│
No
│
Multi-file refactor? ────Yes────→ Review required
│
No
│
Skip review (simple task)
```
### Branch Strategy
```
Input has story context? ───Yes───→ Create/use feat/{story-id}-* branch
│
No
│
Work on current branch
```
## Examples in Action
### Example 1: Quick Feature
```bash
$ /lazy code "add logout button to header"
Detected: Brief description
Complexity: Simple (single feature)
Tests: Auto-detected (pytest found in repo)
Review: Skipped (simple, single-file)
Branch: Current branch
Implementing...
✓ Added logout button to src/components/Header.tsx
✓ Added test in tests/components/Header.test.tsx
✓ Quality checks passed
✓ Committed: feat: add logout button to header
Complete! (1 file changed)
```
### Example 2: From Story File
```bash
$ /lazy code @US-3.4.md
Detected: User story file
Story: US-3.4 - OAuth2 Authentication
Next pending: TASK-002 - Implement token refresh
Complexity: Complex (auth + security)
Tests: Required (TDD in CLAUDE.md)
Review: Required (complex task)
Branch: feat/US-3.4-oauth2-authentication
Implementing...
✓ Implemented token refresh in src/auth/refresh.py
✓ Added tests in tests/auth/test_refresh.py
✓ Quality checks passed
✓ Code review: APPROVED
✓ Committed: feat(TASK-002): implement token refresh
Complete! Continue with: /lazy code TASK-003
```
### Example 3: From Task ID
```bash
$ /lazy code TASK-007
Detected: Task ID
Finding story... Found in US-2.1-payment-processing
Task: TASK-007 - Add retry logic to payment API
Complexity: Complex (payment + API)
Tests: Required (pytest found)
Review: Required (payment-related)
Branch: feat/US-2.1-payment-processing
Implementing...
✓ Added retry logic to src/payment/api.py
✓ Added retry tests in tests/payment/test_api.py
✓ Quality checks passed
✓ Code review: APPROVED
✓ Committed: feat(TASK-007): add retry logic to payment API
✓ Tagged: task/TASK-007-done
Complete! Continue with: /lazy code TASK-008
```
### Example 4: From GitHub Issue
```bash
$ /lazy code #456
Detected: GitHub issue
Fetching issue #456...
Issue: "Fix validation error in user signup form"
Labels: bug, frontend
Complexity: Simple (bug fix)
Tests: Required (jest found in repo)
Review: Skipped (simple bug fix)
Branch: Current branch
Implementing...
✓ Fixed validation in src/components/SignupForm.tsx
✓ Added regression test in tests/components/SignupForm.test.tsx
✓ Quality checks passed
✓ Committed: fix: validation error in user signup form (closes #456)
Complete! Issue #456 will be closed on PR merge.
```
## Key Principles
1. **Zero Configuration**: No flags, no setup - just provide input
2. **Smart Defaults**: Infer tests, review, complexity from context
3. **Flexible Input**: Accept stories, tasks, briefs, issues
4. **Auto Quality**: PostToolUse hook handles formatting/linting/tests
5. **Contextual Branching**: Stories get branches, briefs work on current
6. **Progressive Enhancement**: More context = smarter orchestration
## Integration Points
**With plan command:**
```bash
/lazy plan "feature description" # Creates US-story.md
/lazy code @US-story.md # Implements first task
/lazy code TASK-002 # Continues with next task
```
**With review command:**
```bash
/lazy code TASK-001 # Commit 1
/lazy code TASK-002 # Commit 2
/lazy code TASK-003 # Commit 3
/lazy review US-3.4 # Review all tasks, create PR
```
**With fix command:**
```bash
/lazy code TASK-001 # Implementation
/lazy review US-3.4 # Generates review-report.md
/lazy fix review-report.md # Apply fixes
```
## Environment Variables
```bash
# Force TDD for all tasks
export LAZYDEV_ENFORCE_TDD=1
# Minimum test count
export LAZYDEV_MIN_TESTS=3
# Skip review for all tasks (not recommended)
export LAZYDEV_SKIP_REVIEW=1
```
## Troubleshooting
**Issue: Task ID not found**
```bash
# Check story files exist
ls -la ./project-management/US-STORY/*/US-story.md
# Search for task manually
grep -r "TASK-003" ./project-management/US-STORY
```
**Issue: Tests not running**
```bash
# Check test framework installed
pytest --version # or: npm test
# Check TDD configuration
cat CLAUDE.md | grep -i tdd
echo $LAZYDEV_ENFORCE_TDD
```
**Issue: Review not triggering**
```bash
# Reviews trigger automatically for:
# - Complex tasks (security/auth/database)
# - Multi-file changes
# - Large estimates
# To force review, set in task:
### TASK-X: ... [REVIEW_REQUIRED]
```
---
**Version:** 2.2.0
**Status:** Production-Ready
**Philosophy:** Accept anything, infer everything, build intelligently.

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---
description: Generate documentation for codebase, branch, commit, or file
argument-hint: [scope] [format]
allowed-tools: Read, Write, Bash, Glob, Grep, Edit, Task
model: claude-haiku-4-5-20251001
---
# Documentation Generator
Generate or update documentation for the specified scope with the selected format.
## Variables
SCOPE: $1
FORMAT: ${2:-docstrings}
PROJECT_ROOT: $(pwd)
## Instructions
You are the Documentation Command Handler for LAZY-DEV-FRAMEWORK.
Your task is to generate or update documentation based on the provided **SCOPE** and **FORMAT**.
### Step 1: Parse Scope and Identify Target Files
Analyze the **SCOPE** variable to determine which files need documentation:
- **`codebase`**: Document all Python files in the project
- Use Glob to find: `**/*.py`
- Exclude: `__pycache__`, `.venv`, `venv`, `node_modules`, `tests/`, `.git`
- **`current-branch`**: Document files changed in the current git branch
- Run: `git diff --name-only main...HEAD` (or default branch)
- Filter for relevant file extensions based on FORMAT
- **`last-commit`**: Document files in the most recent commit
- Run: `git diff-tree --no-commit-id --name-only -r HEAD`
- Filter for relevant file extensions
- **Specific file path** (e.g., `src/auth.py` or `.`): Document the specified file or directory
- If directory: Use Glob to find relevant files
- If file: Document that specific file
- Validate the path exists before proceeding
### Step 2: Validate Format
Ensure **FORMAT** is one of the supported formats:
- `docstrings` - Add/update Google-style docstrings (default)
- `readme` - Generate or update README.md
- `api` - Generate API documentation
- `security` - Generate security considerations document
- `setup` - Generate setup/installation guide
If FORMAT is invalid, report an error and stop.
### Step 3: Prepare Agent Invocation
For each target file or module group, prepare to invoke the Documentation Agent with:
**Agent Call Structure**:
```markdown
You are the Documentation Agent. Generate documentation for the following scope:
## Scope
[List of files or description of scope]
## Format
$FORMAT
## Target
[Output directory based on format - docs/ for files, ./ for README]
## Instructions
[Format-specific instructions will be provided by the agent template]
```
**Use the Task tool** to invoke the Documentation Agent. The agent will:
1. Read the target files
2. Analyze code structure, functions, classes, and modules
3. Generate appropriate documentation based on FORMAT
4. Write updated files (for docstrings) or new documentation files (for readme/api/security/setup)
### Step 4: Track Coverage Changes
**Before Agent Invocation**:
- Count existing docstrings/documentation
- Calculate current documentation coverage percentage
**After Agent Invocation**:
- Count new/updated docstrings/documentation
- Calculate new documentation coverage percentage
- Report the improvement
### Step 5: Generate Summary Report
After all files are processed, generate a structured report in this format:
```
📖 Documentation Generated
[For docstrings format:]
Docstrings added: X files
✓ path/to/file1.py (Y functions/classes)
✓ path/to/file2.py (Z functions/classes)
✓ path/to/file3.py (W functions/classes)
Coverage: XX% → YY% ✅
[For readme/api/security/setup formats:]
Files created/updated:
✓ README.md
✓ docs/API.md
✓ docs/SECURITY.md
✓ docs/SETUP.md
Documentation status: Complete ✅
```
## Workflow
1. **Parse Arguments**
- Extract SCOPE from $1
- Extract FORMAT from $2 (default: docstrings)
- Validate both parameters
2. **Identify Target Files**
- Based on SCOPE, use Glob, Grep, or Bash (git commands) to locate files
- Build a list of absolute file paths
- Verify files exist and are readable
3. **Invoke Documentation Agent**
- Use Task tool to invoke the Documentation Agent
- Pass scope, format, and target directory
- Agent reads files, generates documentation, writes output
4. **Calculate Coverage**
- Compare before/after documentation metrics
- Calculate coverage percentage improvement
5. **Generate Report**
- List all files documented
- Show coverage improvement
- Confirm successful completion
## Error Handling
- If SCOPE is invalid or empty: Report error and ask user to specify scope
- If FORMAT is not supported: Report valid formats and ask user to choose
- If no files found for given SCOPE: Report no files found and suggest alternative scope
- If git commands fail (for branch/commit scopes): Report git error and suggest using file path
- If Documentation Agent fails: Report agent error and suggest manual review
## Examples
### Example 1: Document entire codebase with docstrings
```bash
/lazy documentation codebase docstrings
```
Expected flow:
1. Find all .py files in project
2. Invoke Documentation Agent for each module/file group
3. Agent adds Google-style docstrings to functions/classes
4. Report coverage improvement
### Example 2: Generate README for current branch changes
```bash
/lazy documentation current-branch readme
```
Expected flow:
1. Run git diff to find changed files
2. Invoke Documentation Agent with scope=changed files, format=readme
3. Agent generates comprehensive README.md
4. Report README created
### Example 3: Generate API docs for specific module
```bash
/lazy documentation src/auth.py api
```
Expected flow:
1. Validate src/auth.py exists
2. Invoke Documentation Agent with scope=src/auth.py, format=api
3. Agent generates docs/API.md with module documentation
4. Report API documentation created
### Example 4: Generate security documentation
```bash
/lazy documentation . security
```
Expected flow:
1. Find all relevant files in current directory
2. Invoke Documentation Agent with scope=current directory, format=security
3. Agent analyzes code for security patterns and generates docs/SECURITY.md
4. Report security documentation created
## Output Format Requirements
- Use emoji indicators for visual clarity (📖, ✓, ✅)
- Report absolute file paths in output
- Show clear before/after metrics for coverage
- List all files processed
- Indicate success/failure clearly
- Include actionable next steps if applicable
## Notes
- Documentation Agent is a sub-agent defined in `.claude/agents/documentation.md`
- Agent uses Haiku model for cost efficiency
- For large codebases (>50 files), process in batches of 10-15 files
- Coverage calculation counts docstrings/functions ratio for docstrings format
- For readme/api/security/setup formats, "coverage" means documentation completeness
- Always use absolute paths in reports
- Git commands are cross-platform compatible (Windows/Linux/macOS)

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---
description: Initialize new project with comprehensive documentation (overview, specs, tech stack, architecture)
argument-hint: "[project-description] [--file FILE] [--minimal] [--no-sync] [--no-arch]"
allowed-tools: Read, Write, Bash, Skill, Glob
model: claude-sonnet-4-5-20250929
---
# Init Project: Bootstrap Project Foundation
## Introduction
Transform a project idea into complete foundational documentation including project overview, technical specifications, technology stack selection, and system architecture design.
**Purpose**: Create the documentation foundation before writing any code - ensures alignment, reduces rework, and provides clear technical direction.
**Output Structure:**
```
./project-management/
├── PROJECT-OVERVIEW.md # Vision, goals, features, success criteria
├── SPECIFICATIONS.md # Functional/non-functional requirements, API contracts, data models
├── TECH-STACK.md # Technology selections with rationale and trade-offs
├── ARCHITECTURE.md # System design with mermaid diagrams
└── .meta/
└── last-sync.json # Tracking metadata for document sync
```
**Integration**: Generated documents serve as input for `/lazy plan` when creating user stories.
---
## When to Use
**Use `/lazy init-project` when:**
- Starting a brand new greenfield project
- Need structured project documentation before coding
- Want technology stack guidance and architecture design
- Transitioning from idea to implementation
- Building POC/MVP and need technical foundation
**Skip this command when:**
- Project already has established documentation
- Working on existing codebase
- Only need single user story (use `/lazy plan` directly)
- Quick prototype without formal planning
---
## Usage Examples
```bash
# From project description
/lazy init-project "Build a real-time task management platform with AI prioritization"
# From enhanced prompt file (recommended)
/lazy init-project --file enhanced_prompt.md
# Minimal mode (skip architecture, faster)
/lazy init-project "E-commerce marketplace" --minimal
# Skip architecture generation
/lazy init-project "API service" --no-arch
# Disable auto-sync tracking
/lazy init-project "Chat app" --no-sync
```
---
## Requirements
### Prerequisites
- Working directory is project root
- Git repository initialized (recommended)
- PROJECT-OVERVIEW.md should not already exist (will be overwritten)
### Input Requirements
- **Project description** (required): Either inline text or file path via `--file`
- **Sufficient detail**: Mention key features, tech preferences, scale expectations
- **Clear goals**: What problem does this solve?
### Optional Flags
- `--file FILE`: Read project description from file (STT enhanced prompt recommended)
- `--minimal`: Generate only PROJECT-OVERVIEW.md and SPECIFICATIONS.md (skip tech stack and architecture)
- `--no-arch`: Generate overview, specs, and tech stack but skip architecture diagrams
- `--no-sync`: Skip creating `.meta/last-sync.json` tracking file
---
## Execution
### Step 1: Parse Arguments and Load Project Description
**Parse flags:**
```python
args = parse_arguments("$ARGUMENTS")
# Extract flags
file_path = args.get("--file")
minimal_mode = "--minimal" in args
skip_arch = "--no-arch" in args
disable_sync = "--no-sync" in args
# Get project description
if file_path:
# Read from file
project_description = read_file(file_path)
if not project_description:
return error(f"File not found or empty: {file_path}")
else:
# Use inline description
project_description = remove_flags(args)
if not project_description.strip():
return error("No project description provided. Use inline text or --file FILE")
```
**Validation:**
- Project description must be non-empty
- If `--file` used, file must exist and be readable
- Minimum 50 characters for meaningful planning (warn if less)
---
### Step 2: Create Project Management Directory
**Setup directory structure:**
```bash
# Create base directory
mkdir -p ./project-management/.meta
# Check if PROJECT-OVERVIEW.md exists
if [ -f "./project-management/PROJECT-OVERVIEW.md" ]; then
echo "Warning: PROJECT-OVERVIEW.md already exists and will be overwritten"
fi
```
**Output location**: Always `./project-management/` relative to current working directory.
---
### Step 3: Invoke Project Planner Skill
**Generate overview and specifications:**
```python
# Invoke project-planner skill
result = Skill(
command="project-planner",
context={
"description": project_description,
"output_dir": "./project-management/"
}
)
# Skill generates:
# - PROJECT-OVERVIEW.md (vision, goals, features, constraints)
# - SPECIFICATIONS.md (requirements, API contracts, data models)
# Verify both files were created
assert exists("./project-management/PROJECT-OVERVIEW.md"), "PROJECT-OVERVIEW.md not created"
assert exists("./project-management/SPECIFICATIONS.md"), "SPECIFICATIONS.md not created"
```
**What project-planner does:**
1. Extracts project context (name, features, goals, constraints)
2. Generates PROJECT-OVERVIEW.md with vision and high-level features
3. Generates SPECIFICATIONS.md with detailed technical requirements
4. Validates completeness of both documents
**Expected output:**
- `PROJECT-OVERVIEW.md`: 2-3KB, executive summary format
- `SPECIFICATIONS.md`: 8-15KB, comprehensive technical details
---
### Step 4: Invoke Tech Stack Architect Skill (unless --minimal or --no-arch)
**Generate technology stack selection:**
```python
# Skip if minimal mode or no-arch flag
if not minimal_mode:
# Read PROJECT-OVERVIEW.md for context
overview_content = read_file("./project-management/PROJECT-OVERVIEW.md")
# Invoke tech-stack-architect skill
result = Skill(
command="tech-stack-architect",
context={
"project_overview": overview_content,
"specifications": read_file("./project-management/SPECIFICATIONS.md"),
"output_dir": "./project-management/",
"skip_architecture": skip_arch # Only generate TECH-STACK.md if true
}
)
# Skill generates:
# - TECH-STACK.md (frontend, backend, database, DevOps choices with rationale)
# - ARCHITECTURE.md (system design with mermaid diagrams) [unless skip_arch]
# Verify tech stack file created
assert exists("./project-management/TECH-STACK.md"), "TECH-STACK.md not created"
if not skip_arch:
assert exists("./project-management/ARCHITECTURE.md"), "ARCHITECTURE.md not created"
```
**What tech-stack-architect does:**
1. Reads PROJECT-OVERVIEW.md for requirements and constraints
2. Analyzes technology needs across 4 categories: Frontend, Backend, Database, DevOps
3. Generates TECH-STACK.md with choices, rationale, alternatives, trade-offs
4. Designs system architecture with component diagrams
5. Generates ARCHITECTURE.md with mermaid diagrams for structure, data flow, deployment
**Expected output:**
- `TECH-STACK.md`: 5-8KB, table-based technology selections
- `ARCHITECTURE.md`: 10-15KB, system design with 3-5 mermaid diagrams
---
### Step 5: Create Tracking Metadata (unless --no-sync)
**Generate sync tracking file:**
```python
if not disable_sync:
metadata = {
"initialized_at": datetime.now().isoformat(),
"documents": {
"PROJECT-OVERVIEW.md": {
"created": datetime.now().isoformat(),
"size_bytes": file_size("./project-management/PROJECT-OVERVIEW.md"),
"checksum": sha256("./project-management/PROJECT-OVERVIEW.md")
},
"SPECIFICATIONS.md": {
"created": datetime.now().isoformat(),
"size_bytes": file_size("./project-management/SPECIFICATIONS.md"),
"checksum": sha256("./project-management/SPECIFICATIONS.md")
},
"TECH-STACK.md": {
"created": datetime.now().isoformat(),
"size_bytes": file_size("./project-management/TECH-STACK.md"),
"checksum": sha256("./project-management/TECH-STACK.md")
} if not minimal_mode else None,
"ARCHITECTURE.md": {
"created": datetime.now().isoformat(),
"size_bytes": file_size("./project-management/ARCHITECTURE.md"),
"checksum": sha256("./project-management/ARCHITECTURE.md")
} if not minimal_mode and not skip_arch else None
},
"flags": {
"minimal": minimal_mode,
"skip_architecture": skip_arch
}
}
# Write metadata
write_json("./project-management/.meta/last-sync.json", metadata)
```
**Purpose of tracking:**
- Detect manual changes to generated files
- Support future re-sync or update operations
- Track generation history
---
### Step 6: Git Add (if in repository)
**Stage generated files:**
```bash
# Check if in git repo
if git rev-parse --git-dir > /dev/null 2>&1; then
# Add all generated files
git add ./project-management/PROJECT-OVERVIEW.md
git add ./project-management/SPECIFICATIONS.md
if [ "$minimal_mode" = false ]; then
git add ./project-management/TECH-STACK.md
[ "$skip_arch" = false ] && git add ./project-management/ARCHITECTURE.md
fi
[ "$disable_sync" = false ] && git add ./project-management/.meta/last-sync.json
echo "✓ Files staged for commit (git add)"
echo "Note: Review files before committing"
else
echo "Not a git repository - skipping git add"
fi
```
**Important**: Files are staged but NOT committed. User should review before committing.
---
### Step 7: Output Summary
**Display comprehensive summary:**
```markdown
## Project Initialization Complete
**Project Name**: {extracted from PROJECT-OVERVIEW.md}
**Documents Generated**:
1. ✅ **PROJECT-OVERVIEW.md** ({size}KB)
- Vision and goals defined
- {N} key features identified
- {N} success criteria established
- Constraints and scope documented
2. ✅ **SPECIFICATIONS.md** ({size}KB)
- {N} functional requirements detailed
- Non-functional requirements defined
- {N} API endpoints documented (if applicable)
- {N} data models specified
- Development phases outlined
{if not minimal_mode:}
3. ✅ **TECH-STACK.md** ({size}KB)
- Frontend stack selected: {tech}
- Backend stack selected: {tech}
- Database choices: {tech}
- DevOps infrastructure: {tech}
- Trade-offs and migration path documented
{if not skip_arch:}
4. ✅ **ARCHITECTURE.md** ({size}KB)
- System architecture designed
- {N} component diagrams included
- Data flow documented
- Security architecture defined
- Scalability strategy outlined
{if not disable_sync:}
5. ✅ **Tracking metadata** (.meta/last-sync.json)
- Document checksums recorded
- Sync tracking enabled
**Location**: `./project-management/`
**Next Steps**:
1. **Review Documentation** (~15-20 minutes)
- Read PROJECT-OVERVIEW.md for accuracy
- Verify SPECIFICATIONS.md completeness
- Check TECH-STACK.md technology choices
- Review ARCHITECTURE.md diagrams
2. **Customize** (Optional)
- Refine goals and success criteria
- Add missing requirements
- Adjust technology choices if needed
- Enhance architecture diagrams
3. **Commit Initial Docs**
```bash
git commit -m "docs: initialize project documentation
- Add PROJECT-OVERVIEW.md with vision and goals
- Add SPECIFICATIONS.md with technical requirements
- Add TECH-STACK.md with technology selections
- Add ARCHITECTURE.md with system design
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>"
```
4. **Start Planning User Stories**
```bash
# Create first user story from specifications
/lazy plan "Implement user authentication system"
# Or plan from specific requirement
/lazy plan --file ./project-management/SPECIFICATIONS.md --section "Authentication"
```
5. **Begin Implementation**
```bash
# After creating user story
/lazy code @US-1.1.md
```
**Estimated Time to Review/Customize**: 15-30 minutes
**Documentation Size**: {total}KB across {N} files
---
## Tips for Success
**Review Phase:**
- Don't skip the review - these docs guide all future development
- Check if technology choices match team skills
- Verify success criteria are measurable
- Ensure API contracts match business requirements
**Customization:**
- Feel free to edit generated docs manually
- Add project-specific constraints or requirements
- Refine architecture based on team preferences
- Update specs as you learn more
**Next Phase:**
- Use generated docs as input to `/lazy plan`
- Reference TECH-STACK.md during implementation
- Keep ARCHITECTURE.md updated as system evolves
- Revisit SUCCESS CRITERIA monthly
```
---
## Validation
### Success Criteria
**Documents Generated:**
- ✅ PROJECT-OVERVIEW.md exists and is non-empty (>1KB)
- ✅ SPECIFICATIONS.md exists and is comprehensive (>5KB)
- ✅ TECH-STACK.md exists (unless --minimal) and has 4 categories
- ✅ ARCHITECTURE.md exists (unless --minimal or --no-arch) and has mermaid diagrams
- ✅ .meta/last-sync.json exists (unless --no-sync) with checksums
**Content Quality:**
- ✅ PROJECT-OVERVIEW.md has vision, goals, features, success criteria, constraints
- ✅ SPECIFICATIONS.md has functional requirements, API contracts, data models
- ✅ TECH-STACK.md has rationale and alternatives for each technology
- ✅ ARCHITECTURE.md has C4 diagram, component details, data flow diagrams
**Git Integration:**
- ✅ Files staged for commit (if in git repo)
- ✅ No automatic commit created (user reviews first)
### Error Conditions
**Handle gracefully:**
- Empty or insufficient project description → Return error with guidance
- File not found (--file flag) → Clear error message with path
- PROJECT-OVERVIEW.md already exists → Warn but continue (overwrite)
- Skill execution failure → Display error and suggest manual creation
- Not in git repo → Skip git operations, warn user
---
## Examples in Action
### Example 1: Full Initialization (Recommended)
```bash
$ /lazy init-project "Build a real-time task management platform with AI-powered prioritization, team collaboration, and GitHub integration. Target 1000 users, 99.9% uptime. Python backend, React frontend."
Initializing project...
Step 1/5: Generating project overview and specifications...
✓ PROJECT-OVERVIEW.md created (2.8KB)
✓ SPECIFICATIONS.md created (11.4KB)
Step 2/5: Designing technology stack...
✓ TECH-STACK.md created (6.2KB)
- Frontend: React 18 + Zustand + Tailwind
- Backend: FastAPI + SQLAlchemy
- Database: PostgreSQL + Redis
- DevOps: AWS ECS + GitHub Actions
Step 3/5: Architecting system design...
✓ ARCHITECTURE.md created (13.7KB)
- Component architecture with mermaid diagrams
- Authentication flow documented
- Scalability strategy defined
Step 4/5: Creating tracking metadata...
✓ .meta/last-sync.json created
Step 5/5: Staging files for git...
✓ 5 files staged (git add)
## Project Initialization Complete
Project: TaskFlow Pro - Modern task management with AI
Documents Generated:
1. ✅ PROJECT-OVERVIEW.md (2.8KB)
2. ✅ SPECIFICATIONS.md (11.4KB) - 12 API endpoints, 6 data models
3. ✅ TECH-STACK.md (6.2KB) - Full stack defined
4. ✅ ARCHITECTURE.md (13.7KB) - 5 mermaid diagrams
Next Steps:
1. Review docs (15-20 min)
2. Commit: git commit -m "docs: initialize project"
3. Create first story: /lazy plan "User authentication"
Complete! Ready for user story planning.
```
### Example 2: Minimal Mode (Fast)
```bash
$ /lazy init-project "E-commerce marketplace with product catalog and checkout" --minimal
Initializing project (minimal mode)...
Step 1/2: Generating project overview and specifications...
✓ PROJECT-OVERVIEW.md created (1.9KB)
✓ SPECIFICATIONS.md created (8.3KB)
Step 2/2: Staging files...
✓ 2 files staged (git add)
## Project Initialization Complete (Minimal)
Project: E-Commerce Marketplace
Documents Generated:
1. ✅ PROJECT-OVERVIEW.md (1.9KB)
2. ✅ SPECIFICATIONS.md (8.3KB)
Skipped (minimal mode):
- TECH-STACK.md (technology selection)
- ARCHITECTURE.md (system design)
Note: Use full mode if you need tech stack guidance and architecture diagrams.
Next Steps:
1. Review specs
2. Manually define tech stack (or run: /lazy init-project --no-minimal)
3. Create stories: /lazy plan "Product catalog"
```
### Example 3: From Enhanced Prompt File
```bash
$ /lazy init-project --file enhanced_prompt.md
Reading project description from: enhanced_prompt.md
Initializing project...
Step 1/5: Generating project overview and specifications...
✓ PROJECT-OVERVIEW.md created (3.2KB)
✓ SPECIFICATIONS.md created (14.8KB)
- Extracted 15 functional requirements
- Defined 8 API contracts
- Specified 9 data models
Step 2/5: Designing technology stack...
✓ TECH-STACK.md created (7.1KB)
Step 3/5: Architecting system design...
✓ ARCHITECTURE.md created (16.4KB)
...
Complete! High-quality docs generated from enhanced prompt.
```
---
## Integration with Other Commands
### With `/lazy plan`
```bash
# Initialize project foundation
/lazy init-project "Project description"
# Create first user story (references SPECIFICATIONS.md automatically)
/lazy plan "Implement authentication"
# → project-manager uses SPECIFICATIONS.md for context
# → Generates US-1.1.md aligned with project specs
```
### With `/lazy code`
```bash
# During implementation
/lazy code @US-1.1.md
# → context-packer loads TECH-STACK.md and ARCHITECTURE.md
# → Implementation follows defined architecture patterns
# → Technology choices match TECH-STACK.md
```
### With `/lazy review`
```bash
# During story review
/lazy review US-1.1
# → reviewer-story agent checks alignment with SPECIFICATIONS.md
# → Validates implementation matches ARCHITECTURE.md
# → Ensures success criteria from PROJECT-OVERVIEW.md are met
```
---
## Environment Variables
```bash
# Skip architecture generation by default
export LAZYDEV_INIT_SKIP_ARCH=1
# Minimal mode by default
export LAZYDEV_INIT_MINIMAL=1
# Disable sync tracking
export LAZYDEV_INIT_NO_SYNC=1
# Custom output directory
export LAZYDEV_PROJECT_DIR="./docs/project"
```
---
## Troubleshooting
### Issue: "Insufficient project description"
**Problem**: Description too vague or short.
**Solution**:
```bash
# Provide more detail
/lazy init-project "Build task manager with:
- Real-time collaboration
- AI prioritization
- GitHub/Jira integration
- Target: 10k users, 99.9% uptime
- Stack preference: Python + React"
# Or use enhanced prompt file
/lazy init-project --file enhanced_prompt.md
```
### Issue: "PROJECT-OVERVIEW.md already exists"
**Problem**: Running init-project in directory that's already initialized.
**Solution**:
```bash
# Review existing docs first
ls -la ./project-management/
# If you want to regenerate (will overwrite)
/lazy init-project "New description"
# Or work with existing docs
/lazy plan "First feature"
```
### Issue: "Skill execution failed"
**Problem**: project-planner or tech-stack-architect skill error.
**Solution**:
```bash
# Check skill files exist
ls .claude/skills/project-planner/SKILL.md
ls .claude/skills/tech-stack-architect/SKILL.md
# Try minimal mode (skips tech-stack-architect)
/lazy init-project "Description" --minimal
# Manual fallback: create docs manually using templates
# See .claude/skills/project-planner/SKILL.md for templates
```
### Issue: "No technology preferences detected"
**Problem**: TECH-STACK.md has generic choices that don't match needs.
**Solution**:
```bash
# Be specific about tech preferences in description
/lazy init-project "API service using FastAPI, PostgreSQL, deployed on AWS ECS with GitHub Actions CI/CD"
# Or edit TECH-STACK.md manually after generation
# File is meant to be customized
```
---
## Key Principles
1. **Documentation-First**: Create foundation before writing code
2. **Smart Defaults**: Skills generate opinionated but reasonable choices
3. **Customizable**: All generated docs are meant to be refined
4. **Integration**: Docs feed into planning and implementation commands
5. **Version Control**: Track docs alongside code
6. **Living Documents**: Update as project evolves
7. **No Lock-In**: Skip sections with flags, edit freely
---
## Related Commands
- `/lazy plan` - Create user stories from initialized project
- `/lazy code` - Implement features following architecture
- `/lazy review` - Validate against project specifications
- `/lazy docs` - Generate additional documentation
---
## Skills Used
- `project-planner` - Generates PROJECT-OVERVIEW.md and SPECIFICATIONS.md
- `tech-stack-architect` - Generates TECH-STACK.md and ARCHITECTURE.md
- `output-style-selector` (automatic) - Formats output optimally
---
**Version:** 1.0.0
**Status:** Production-Ready
**Philosophy:** Document first, build second. Clear foundation, faster development.

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---
description: Verify Memory MCP connectivity and list basic stats
argument-hint: [action]
allowed-tools: Read, Write, Grep, Glob, Bash, Task
---
# Memory Connectivity Check
ACTION: ${1:-status}
If the `memory` MCP server is available, call the following tools:
- `mcp__memory__read_graph` and report entity/relation counts
- `mcp__memory__search_nodes` with a sample query like `service:`
If any calls fail, print a clear remediation guide:
1) Ensure `.mcp.json` exists at workspace root (see LAZY_DEV/.claude/.mcp.json)
2) Ensure Node.js is installed and `npx -y @modelcontextprotocol/server-memory` works
3) Reload Claude Code for this workspace
Output a concise summary:
- Server reachable: yes/no
- Entities: N, Relations: M (or unknown)
- Sample search results: top 5 names

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---
description: Manage persistent knowledge via MCP Memory Graph
argument-hint: [intent]
allowed-tools: Read, Write, Grep, Glob, Bash, Task
---
# Memory Graph Command
This command activates the Memory Graph Skill and guides you to use the MCP Memory server tools to persist, update, search, and prune knowledge.
## Inputs
INTENT: ${1:-auto}
## Skill
Include the Memory Graph Skill files:
- .claude/skills/memory-graph/SKILL.md
- .claude/skills/memory-graph/operations.md
- .claude/skills/memory-graph/playbooks.md
- .claude/skills/memory-graph/examples.md
If any file is missing, read the repo to locate them under `.claude/skills/memory-graph/`.
## Behavior
- Detect entities, relations, and observations in the current context
- Use MCP tool names prefixed with `mcp__memory__`
- Avoid duplicates by searching before creating
- Keep observations small and factual, include dates when relevant
- Verify writes with `open_nodes` when helpful
## Planner
1. If INTENT == `auto`, infer one of: `persist-new`, `enrich`, `link`, `correct`, `prune`, `explore`
2. Route per `playbooks.md`
3. Execute the needed MCP tool calls
4. Print a short summary of what changed
5. When appropriate, suggest next relations or entities
## MCP Tooling
Target server name: `memory` (tools appear as `mcp__memory__<tool>`)
Core tools:
- create_entities, add_observations, create_relations
- delete_entities, delete_observations, delete_relations
- read_graph, search_nodes, open_nodes
## Examples
Persist a new service and owner
```
/lazy memory-graph "persist-new service:alpha (owner: alice, repo: org/alpha)"
```
Explore existing graph
```
/lazy memory-graph explore
```
Correct a stale fact
```
/lazy memory-graph "correct owner for service:alpha -> bob"
```

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---
description: Create user story with inline tasks from feature brief
argument-hint: "[feature-description] [--file FILE] [--output-dir DIR]"
allowed-tools: Read, Write, Task, Bash, Grep, Glob
model: claude-haiku-4-5-20251001
---
# Create Feature: Transform Brief to User Story
## Introduction
Transform a brief feature description into a single user story file with tasks included inline.
**Input Sources:**
1. **From STT Prompt Enhancer** (recommended): Enhanced, structured feature description from the STT_PROMPT_ENHANCER project
2. **Direct Input**: Brief text provided directly to command
**Output Structure:**
```
./project-management/US-STORY/US-{STORY_ID}-{story-name}/
└── US-story.md # User story with inline tasks
```
**GitHub Integration:**
Optionally creates a GitHub issue for the story (can be disabled with --no-issue flag).
## Usage Examples
```bash
# From direct input
/lazy create-feature "Add user authentication with OAuth2"
# From STT enhanced file
/lazy create-feature --file enhanced_prompt.md
# With custom output directory
/lazy create-feature "Add analytics dashboard" --output-dir ./docs/project-management/US-STORY
# Skip GitHub issue creation
/lazy create-feature "Build payment processing" --no-issue
```
## Feature Description
<feature_description>
$ARGUMENTS
</feature_description>
## Instructions
### Step 1: Parse Arguments and Load Brief
**Parse Arguments:**
- Check for `--file` flag and read file if provided, otherwise use $ARGUMENTS
- Parse optional `--output-dir` flag (default: `./project-management/US-STORY/`)
- Parse optional `--no-issue` flag (skip GitHub issue creation)
- Verify the brief is not empty
**Error Handling:**
- If `--file` provided but file not found: Return error "File not found at: {path}"
- If no input provided: Return error "No feature brief provided"
### Step 2: Generate Story ID and Create Directory
- Scan `./project-management/US-STORY/` for existing US-* folders
- Generate next story ID (e.g., if US-3.2 exists, next is US-3.3; if none exist, start with US-1.1)
- Create directory: `./project-management/US-STORY/US-{ID}-{story-name}/`
### Step 3: Invoke Project Manager Agent
**Agent**: `project-manager` (at `.claude/agents/project-manager.md`)
The agent will:
1. Read feature brief from conversation
2. Create single US-story.md file with:
- Story description and acceptance criteria
- Tasks listed inline (TASK-1, TASK-2, etc.)
- Security and testing requirements
3. Write file to output directory
### Step 4: Optionally Create GitHub Issue
If `--no-issue` flag NOT provided:
- Create GitHub issue with story content
- Update US-story.md with issue number
### Step 5: Git Add (if in repository)
- Add story file to git: `git add ./project-management/US-STORY/US-{ID}-{name}/`
- Do NOT commit (user commits when ready)
### Step 6: Output Summary
Display:
- Story location
- GitHub issue number (if created)
- Next steps: review story and start implementation

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---
description: Answer questions about code or technical topics without creating artifacts
argument-hint: "<question>"
allowed-tools: Read, Glob, Grep, Bash, Task
---
# Question Command: Intelligent Q&A System
Answer questions about your codebase or general technical topics with zero artifacts.
## Core Philosophy
**Ask anything, get answers, create nothing.**
This command is for Q&A ONLY - no file creation, no documentation generation, no code changes.
## Usage Examples
```bash
# Codebase questions
/lazy question "where is user authentication handled?"
/lazy question "how does the payment processor work?"
/lazy question "what files implement the REST API?"
# General technical questions
/lazy question "what is the difference between REST and GraphQL?"
/lazy question "how to implement OAuth2 in Python?"
/lazy question "best practices for API versioning?"
```
## When to Use
**Use this command when:**
- You need to understand how something works in the codebase
- You want to locate specific functionality
- You have general technical questions
- You need quick documentation lookups
**Do NOT use for:**
- Creating documentation files
- Modifying code
- Generating new files
- Planning features (use `/lazy plan` instead)
## Requirements
**Input:**
- Single question string (clear and specific)
- Can be about codebase OR general knowledge
**Critical:**
- **NO file creation** - answers only
- **NO .md files** - inline responses only
- **NO code generation** - explanation only
- **NO documentation updates** - read-only operation
## Question Type Detection
### Decision Logic
```python
def should_use_codebase(question: str) -> bool:
"""Decide if question is about codebase or general knowledge."""
codebase_indicators = [
"in this", "this codebase", "this project", "this repo",
"where is", "how does", "why does", "what does",
"in our", "our codebase", "our project",
"file", "function", "class", "module",
"implemented", "defined", "located",
"show me", "find", "which file"
]
question_lower = question.lower()
# If question mentions codebase-specific terms → use codebase
if any(ind in question_lower for ind in codebase_indicators):
return True
# If question is general knowledge → use research agent
general_indicators = [
"what is", "how to", "difference between",
"best practice", "tutorial", "documentation",
"learn", "explain", "guide", "introduction"
]
if any(ind in question_lower for ind in general_indicators):
return False
# Default: assume codebase question
return True
```
### Examples by Type
**Codebase Questions (searches project):**
- "where is user authentication handled?"
- "how does this project structure payments?"
- "what files implement the API endpoints?"
- "in our codebase, how is logging configured?"
- "show me where database migrations are defined"
- "which function handles token validation?"
**General Questions (uses research agent):**
- "what is the difference between JWT and session tokens?"
- "how to implement OAuth2 in Python?"
- "best practices for API versioning?"
- "explain what GraphQL is"
- "tutorial on writing pytest fixtures"
## Execution Workflow
### Phase 1: Analyze Question
```python
question = "$ARGUMENTS".strip()
# Determine question type
is_codebase_question = should_use_codebase(question)
if is_codebase_question:
approach = "codebase_search"
tools = ["Grep", "Glob", "Read"]
else:
approach = "research_agent"
tools = ["Task (research agent)"]
```
### Phase 2a: Codebase Question Path
**If question is about the codebase:**
```python
# 1. Extract search terms from question
search_terms = extract_keywords(question)
# Example: "where is authentication handled?" → ["authentication", "auth", "login"]
# 2. Search codebase with Grep
for term in search_terms:
# Search for term in code
matches = grep(pattern=term, output_mode="files_with_matches")
# Search for term in comments/docstrings
doc_matches = grep(pattern=f"(#|//|\"\"\"|\"\"\").*{term}", output_mode="content", -n=True)
# 3. Prioritize results
relevant_files = prioritize_by_relevance(matches, question)
# Priority: src/ > tests/ > docs/
# 4. Read top relevant files
for file in relevant_files[:5]: # Top 5 most relevant
content = Read(file_path=file)
# Extract relevant sections based on search terms
# 5. Analyze and answer
answer = """
Based on codebase analysis:
{synthesized answer from code}
**References:**
- {file1}:{line1}
- {file2}:{line2}
"""
```
**Search Strategy:**
```python
# Identify search terms based on question type
if "where" in question or "which file" in question:
# Location question - find files
search_mode = "files_with_matches"
search_scope = "filenames and content"
elif "how does" in question or "how is" in question:
# Implementation question - show code
search_mode = "content"
search_scope = "function definitions and logic"
context_lines = 10 # Use -C flag
elif "what is" in question and is_codebase_question:
# Definition question - find docstrings/comments
search_mode = "content"
search_scope = "docstrings, comments, README"
```
### Phase 2b: General Question Path
**If question is general knowledge:**
```python
Task(
prompt=f"""
You are the Research Agent for LAZY-DEV-FRAMEWORK.
## Question to Answer
{question}
## Instructions
1. This is a GENERAL technical question (not codebase-specific)
2. Answer based on:
- Your training knowledge
- Industry best practices
- Official documentation (if available)
- Common patterns and conventions
3. Provide a clear, concise answer with:
- Direct answer to the question
- Key concepts explained
- Code examples if relevant (generic, not project-specific)
- Links to official docs/resources
4. Structure answer for readability:
- Use bullet points for lists
- Use code blocks for examples
- Use clear section headers
## Output Format
**Answer:**
{direct answer}
**Key Concepts:**
- {concept 1}
- {concept 2}
**Example (if applicable):**
```language
{generic code example}
```
**Further Reading:**
- {resource 1}
- {resource 2}
**Critical Reminder:**
- Do NOT create any files
- Do NOT search the codebase
- Do NOT reference project-specific code
- This is general knowledge only
"""
)
```
## Output Format
### For Codebase Questions
```markdown
## Answer
Based on codebase analysis:
{synthesized answer from code analysis}
**Key Files:**
- `{file1}` - {purpose}
- `{file2}` - {purpose}
**Code References:**
- {file1}:{line1} - {what happens here}
- {file2}:{line2} - {what happens here}
**Flow:**
{if applicable, explain the flow through multiple files}
```
### For General Questions
```markdown
## Answer
{direct answer to question}
**Key Concepts:**
- {concept 1 with explanation}
- {concept 2 with explanation}
**Example:**
```language
{generic code example if relevant}
```
**Further Reading:**
- {official documentation link}
- {tutorial or guide link}
```
## Validation
**Success criteria:**
- Question answered clearly and accurately
- References provided (for codebase questions)
- No files created or modified
- No commits made
- Response is conversational, not a file
**Anti-patterns (MUST AVOID):**
- Creating documentation files
- Generating .md files
- Writing code to files
- Running git commands
- Creating pull requests
## Examples in Action
### Example 1: Codebase Location Question
```bash
$ /lazy question "where is user authentication handled?"
## Answer
Based on codebase analysis:
User authentication is handled primarily in the `src/auth/` module:
**Key Files:**
- `src/auth/login.py` - Main login endpoint and session management
- `src/auth/middleware.py` - Authentication middleware for protected routes
- `src/auth/jwt_handler.py` - JWT token generation and validation
**Code References:**
- src/auth/login.py:45 - `authenticate_user()` validates credentials
- src/auth/middleware.py:23 - `require_auth()` decorator protects endpoints
- src/auth/jwt_handler.py:67 - `generate_token()` creates JWT tokens
**Authentication Flow:**
1. User submits credentials → `login.py:authenticate_user()`
2. Credentials validated against database
3. JWT token generated → `jwt_handler.py:generate_token()`
4. Token returned to client
5. Protected routes checked by → `middleware.py:require_auth()`
```
### Example 2: Codebase Implementation Question
```bash
$ /lazy question "how does the payment processor work?"
## Answer
Based on codebase analysis:
The payment processor uses a strategy pattern with multiple payment providers:
**Key Files:**
- `src/payment/processor.py` - Main processor coordinator
- `src/payment/providers/stripe.py` - Stripe integration
- `src/payment/providers/paypal.py` - PayPal integration
**Processing Flow:**
1. Client initiates payment → `processor.py:process_payment()`
2. Processor selects provider based on payment method
3. Provider-specific handler called (Stripe/PayPal)
4. Payment validated and processed
5. Result logged and webhook sent
**Code References:**
- src/payment/processor.py:89 - Main `process_payment()` entry point
- src/payment/processor.py:145 - Provider selection logic
- src/payment/providers/stripe.py:34 - Stripe charge creation
- src/payment/webhook.py:23 - Payment confirmation webhook
**Error Handling:**
- Retries configured in processor.py:67 (3 attempts, exponential backoff)
- Failed payments logged to `payment_failures` table
- Customer notified via email on failure
```
### Example 3: General Technical Question
```bash
$ /lazy question "what is the difference between REST and GraphQL?"
## Answer
REST and GraphQL are both API architectural patterns, but they differ in how clients request and receive data:
**Key Differences:**
1. **Data Fetching:**
- REST: Multiple endpoints, each returns fixed data structure
- GraphQL: Single endpoint, client specifies exact data needed
2. **Over/Under-fetching:**
- REST: Often returns too much or too little data
- GraphQL: Client gets exactly what they request
3. **Versioning:**
- REST: Typically requires version in URL (v1, v2)
- GraphQL: Schema evolution without versions
4. **Request Structure:**
- REST: HTTP methods (GET, POST, PUT, DELETE)
- GraphQL: All requests are POST with query body
**Example:**
REST - Multiple requests needed:
```bash
GET /api/users/123 # Get user
GET /api/users/123/posts # Get user's posts
GET /api/posts/456/comments # Get post comments
```
GraphQL - Single request:
```graphql
query {
user(id: 123) {
name
email
posts {
title
comments {
text
author
}
}
}
}
```
**Further Reading:**
- [GraphQL Official Docs](https://graphql.org/learn/)
- [REST API Best Practices](https://restfulapi.net/)
```
### Example 4: Edge Case - Ambiguous Question
```bash
$ /lazy question "how does authentication work?"
# Decision: Too ambiguous - clarify first
## Clarification Needed
Your question "how does authentication work?" could mean:
1. **Codebase-specific:** "How does authentication work in THIS project?"
- Run: `/lazy question "how does authentication work in this codebase?"`
2. **General concept:** "How does authentication work as a concept?"
- Run: `/lazy question "explain authentication concepts and best practices"`
Please rephrase your question to be more specific.
```
## Decision Tree
```
Parse question
↓
Contains codebase indicators? ──Yes──→ Codebase Search Path
│ ↓
No Extract keywords
↓ ↓
Contains general indicators? ──Yes──→ Grep/Glob codebase
│ ↓
No Read relevant files
↓ ↓
Ambiguous Synthesize answer
↓ ↓
Ask for clarification Format with references
↓
Return answer
(NO FILES CREATED)
Research Agent Path:
↓
Delegate to research agent
↓
Agent uses training knowledge
↓
Format answer with examples
↓
Return answer
(NO FILES CREATED)
```
## Key Principles
1. **Read-Only Operation**: Never create, modify, or delete files
2. **Zero Artifacts**: No .md files, no commits, no PRs
3. **Smart Detection**: Auto-determine codebase vs general question
4. **Cite Sources**: Always reference file:line for codebase answers
5. **Conversational**: Return inline answers, not documentation
6. **Focused Search**: Top 5 most relevant files only
7. **Context-Aware**: Use -C flag for code context when needed
## Integration Points
**With other commands:**
```bash
# Learn about codebase before implementing
/lazy question "where is user validation implemented?"
/lazy code "add email validation to user signup"
# Understand before documenting
/lazy question "how does the API rate limiting work?"
/lazy docs src/api/rate_limiter.py
# Research before planning
/lazy question "best practices for OAuth2 implementation"
/lazy plan "add OAuth2 authentication"
```
## Environment Variables
None required - this is a pure Q&A command.
## Troubleshooting
**Issue: "No results found"**
```
Try rephrasing your question:
- Use different keywords
- Be more specific about file types or modules
- Check if functionality exists in project
```
**Issue: "Too many results"**
```
Narrow your question:
- Specify module or component
- Add context about feature area
- Ask about specific file/function
```
**Issue: "Wrong type detected"**
```
Force codebase search:
- Add "in this codebase" to question
Force general search:
- Add "explain" or "what is" to question
```
## Anti-Patterns to Avoid
**DO NOT:**
- Create documentation files from answers
- Generate code files based on research
- Write .md files with Q&A content
- Make commits or PRs
- Modify existing files
- Create new directories
**DO:**
- Answer questions inline
- Provide file references
- Show code snippets in response
- Explain concepts clearly
- Link to external resources
---
**Version:** 2.2.0
**Status:** Production-Ready
**Philosophy:** Ask anything, get answers, create nothing.

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{
"hooks": {
"UserPromptSubmit": [
{
"matcher": "*",
"hooks": [
{
"type": "command",
"command": "python .claude/hooks/user_prompt_submit.py",
"description": "Pre-prompt enrichment with context packs"
}
]
}
],
"PreToolUse": [
{
"matcher": "Bash|Write|Edit",
"hooks": [
{
"type": "command",
"command": "python .claude/hooks/pre_tool_use.py",
"description": "Security gates for dangerous operations"
}
]
}
],
"PostToolUse": [
{
"matcher": "Write|Edit",
"hooks": [
{
"type": "command",
"command": "python .claude/hooks/post_tool_use_format.py",
"description": "Auto-formatting after file operations"
}
]
}
]
}
}

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# LAZY‑DEV Skills Index
Lightweight, Anthropic‑compatible Skills that Claude can load when relevant. Each skill is a folder with `SKILL.md` (frontmatter + concise instructions).
## Skills List
- `brainstorming/` — Structured ideation; options matrix + decision.
- `code-review-request/` — Focused review request; rubric + patch plan.
- `git-worktrees/` — Create/switch/remove Git worktrees safely.
- `subagent-driven-development/` — Delegate subtasks to coder/reviewer/research/PM.
- `test-driven-development/` — RED→GREEN→REFACTOR micro‑cycles; small diffs.
- `writing-skills/` — Generate new skills for claude codem using natural language prompts and anthropic documentation.
- `story-traceability/` — AC → Task → Test mapping for PR‑per‑story.
- `task-slicer/` — Split stories into 2–4h atomic tasks with tests.
- `gh-issue-sync/` — Draft GitHub issues/sub‑issues from local story/tasks.
- `ac-expander/` — Make AC measurable; add edge cases and test names.
- `output-style-selector/` — Auto‑pick best format (table, bullets, YAML, HTML, concise).
- `context-packer/` — Compact, high‑signal context instead of long pastes.
- `diff-scope-minimizer/` — Tiny patch plan, tight diffs, stop criteria.
## Suggested Pairings
- Project Manager → story-traceability, task-slicer, ac-expander, gh-issue-sync
- Coder → test-driven-development, diff-scope-minimizer, git-worktrees
- Reviewer / Story Reviewer → code-review-request
- Documentation → output-style-selector
- Research → brainstorming, context-packer
## Overrides & Style
- Force/disable a style inline: `[style: table-based]`, `[style: off]`
- Manual skill hint in prompts: “Use skill ‘test-driven-development’ for this task.”
## Wiring (optional, not enabled yet)
- UserPromptSubmit: run `context-packer` + `output-style-selector`
- PreToolUse: nudge `test-driven-development` + `diff-scope-minimizer`

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---
name: ac-expander
description: Turn vague Acceptance Criteria into measurable checks and test assertions
version: 0.1.0
tags: [requirements, testing]
triggers:
- acceptance criteria
- refine criteria
- measurable
---
# Acceptance Criteria Expander
## Purpose
Rewrite ambiguous AC into specific, testable checks and edge cases.
## Behavior
1. For each AC, create measurable statements with inputs/outputs.
2. Add 2–4 edge cases (bounds, invalid, error paths).
3. Suggest test names that map 1:1 to checks.
## Guardrails
- Preserve original intent; show original text and revised version.
- Keep each AC concise (≤3 lines each).
## Integration
- Project Manager agent; `/lazy create-feature` refinement step.
## Example Prompt
> Make these AC measurable and propose matching tests.

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---
name: agent-selector
description: Automatically selects the best specialized agent based on user prompt keywords and task type. Use when routing work to coder, tester, reviewer, research, refactor, documentation, or cleanup agents.
---
# Agent Selector Skill
**Purpose**: Route tasks to the most appropriate specialized agent for optimal results.
**Trigger Words**: test, write tests, unittest, coverage, pytest, how to, documentation, learn, research, review, check code, code quality, security audit, refactor, clean up, improve code, simplify, document, docstring, readme, api docs
---
## Quick Decision: Which Agent?
```python
def select_agent(prompt: str, context: dict) -> str:
"""Fast agent selection based on prompt keywords and context."""
prompt_lower = prompt.lower()
# Priority order matters - check most specific first
# Testing keywords (high priority)
testing_keywords = [
"test", "unittest", "pytest", "coverage", "test case",
"unit test", "integration test", "e2e test", "tdd",
"test suite", "test runner", "jest", "mocha"
]
if any(k in prompt_lower for k in testing_keywords):
return "tester"
# Research keywords (before implementation)
research_keywords = [
"how to", "how do i", "documentation", "learn", "research",
"fetch docs", "find examples", "best practices",
"which library", "compare options", "what is", "explain"
]
if any(k in prompt_lower for k in research_keywords):
return "research"
# Review keywords (code quality)
review_keywords = [
"review", "check code", "code quality", "security audit",
"validate", "verify", "inspect", "lint", "analyze"
]
if any(k in prompt_lower for k in review_keywords):
return "reviewer"
# Refactoring keywords
refactor_keywords = [
"refactor", "clean up", "improve code", "simplify",
"optimize", "restructure", "reorganize", "extract"
]
if any(k in prompt_lower for k in refactor_keywords):
return "refactor"
# Documentation keywords
doc_keywords = [
"document", "docstring", "readme", "api docs",
"write docs", "update docs", "comment", "annotation"
]
if any(k in prompt_lower for k in doc_keywords):
return "documentation"
# Cleanup keywords
cleanup_keywords = [
"remove dead code", "unused imports", "orphaned files",
"cleanup", "prune", "delete unused"
]
if any(k in prompt_lower for k in cleanup_keywords):
return "cleanup"
# Default: coder for implementation tasks
# (add, build, create, fix, implement, develop)
return "coder"
```
---
## Agent Selection Logic
### 1. **Tester Agent** - Testing & Coverage
```
Triggers:
- "test", "unittest", "pytest", "coverage"
- "write tests for X"
- "add test cases"
- "increase coverage"
- "test suite", "test runner"
Examples:
✓ "write unit tests for auth module"
✓ "add pytest coverage for payment processor"
✓ "create integration tests"
```
**Agent Capabilities:**
- Write unit, integration, and E2E tests
- Increase test coverage
- Mock external dependencies
- Test edge cases
- Verify test quality
---
### 2. **Research Agent** - Learning & Discovery
```
Triggers:
- "how to", "how do I", "learn"
- "documentation", "research"
- "fetch docs", "find examples"
- "which library", "compare options"
- "what is", "explain"
Examples:
✓ "how to implement OAuth2 in FastAPI"
✓ "research best practices for API rate limiting"
✓ "fetch documentation for Stripe API"
✓ "compare Redis vs Memcached"
```
**Agent Capabilities:**
- Fetch external documentation
- Search for code examples
- Compare library options
- Explain technical concepts
- Find best practices
---
### 3. **Reviewer Agent** - Code Quality & Security
```
Triggers:
- "review", "check code", "code quality"
- "security audit", "validate", "verify"
- "inspect", "lint", "analyze"
Examples:
✓ "review the authentication implementation"
✓ "check code quality in payment module"
✓ "security audit for user input handling"
✓ "validate error handling"
```
**Agent Capabilities:**
- Code quality review
- Security vulnerability detection (OWASP)
- Best practices validation
- Performance anti-pattern detection
- Architecture compliance
---
### 4. **Refactor Agent** - Code Improvement
```
Triggers:
- "refactor", "clean up", "improve code"
- "simplify", "optimize", "restructure"
- "reorganize", "extract"
Examples:
✓ "refactor the user service to reduce complexity"
✓ "clean up duplicate code in handlers"
✓ "simplify the authentication flow"
✓ "extract common logic into utils"
```
**Agent Capabilities:**
- Reduce code duplication
- Improve code structure
- Extract reusable components
- Simplify complex logic
- Optimize algorithms
---
### 5. **Documentation Agent** - Docs & Comments
```
Triggers:
- "document", "docstring", "readme"
- "api docs", "write docs", "update docs"
- "comment", "annotation"
Examples:
✓ "document the payment API endpoints"
✓ "add docstrings to auth module"
✓ "update README with setup instructions"
✓ "generate API documentation"
```
**Agent Capabilities:**
- Generate docstrings (Google style)
- Write README sections
- Create API documentation
- Add inline comments
- Update existing docs
---
### 6. **Cleanup Agent** - Dead Code Removal
```
Triggers:
- "remove dead code", "unused imports"
- "orphaned files", "cleanup", "prune"
- "delete unused"
Examples:
✓ "remove dead code from legacy module"
✓ "clean up unused imports"
✓ "delete orphaned test files"
✓ "prune deprecated functions"
```
**Agent Capabilities:**
- Identify unused imports/functions
- Remove commented code
- Find orphaned files
- Clean up deprecated code
- Safe deletion with verification
---
### 7. **Coder Agent** (Default) - Implementation
```
Triggers:
- "add", "build", "create", "implement"
- "fix", "develop", "write code"
- Any implementation task
Examples:
✓ "add user authentication"
✓ "build payment processing endpoint"
✓ "fix null pointer exception"
✓ "implement rate limiting"
```
**Agent Capabilities:**
- Feature implementation
- Bug fixes
- API development
- Database operations
- Business logic
---
## Output Format
```markdown
## Agent Selection
**User Prompt**: "[original prompt]"
**Task Analysis**:
- Type: [Testing | Research | Review | Refactoring | Documentation | Cleanup | Implementation]
- Keywords Detected: [keyword1, keyword2, ...]
- Complexity: [Simple | Moderate | Complex]
**Selected Agent**: `[agent-name]`
**Rationale**:
[Why this agent was chosen - 1-2 sentences explaining the match between prompt and agent capabilities]
**Estimated Time**: [5-15 min | 15-30 min | 30-60 min | 1-2h]
---
Delegating to **[agent-name]** agent...
```
---
## Decision Tree (Visual)
```
User Prompt
↓
Is it about testing?
├─ YES → tester
└─ NO ↓
Is it a research/learning question?
├─ YES → research
└─ NO ↓
Is it about code review/quality?
├─ YES → reviewer
└─ NO ↓
Is it about refactoring?
├─ YES → refactor
└─ NO ↓
Is it about documentation?
├─ YES → documentation
└─ NO ↓
Is it about cleanup?
├─ YES → cleanup
└─ NO ↓
DEFAULT → coder (implementation)
```
---
## Context-Aware Selection
Sometimes context matters more than keywords:
```python
def context_aware_selection(prompt: str, context: dict) -> str:
"""Consider additional context beyond keywords."""
# Check file types in context
files = context.get("files", [])
# If only test files, likely testing task
if all("test_" in f or "_test" in f for f in files):
return "tester"
# If README or docs/, likely documentation
if any("README" in f or "docs/" in f for f in files):
return "documentation"
# If many similar functions, likely refactoring
if context.get("code_duplication") == "high":
return "refactor"
# Check task tags
tags = context.get("tags", [])
if "security" in tags:
return "reviewer"
# Fall back to keyword-based selection
return select_agent(prompt, context)
```
---
## Integration with Workflow
### Automatic Agent Selection
```bash
# User: "write unit tests for payment processor"
→ agent-selector triggers
→ Detects: "write", "unit tests" keywords
→ Selected: tester agent
→ Task tool invokes: Task(command="tester", ...)
# User: "how to implement OAuth2 in FastAPI"
→ agent-selector triggers
→ Detects: "how to", "implement" keywords
→ Selected: research agent (research takes priority)
→ Task tool invokes: Task(command="research", ...)
# User: "refactor user service to reduce complexity"
→ agent-selector triggers
→ Detects: "refactor", "reduce complexity" keywords
→ Selected: refactor agent
→ Task tool invokes: Task(command="refactor", ...)
```
### Manual Override
```bash
# Force specific agent
Task(command="tester", prompt="implement payment processing")
# (Overrides agent-selector, uses tester instead of coder)
```
---
## Multi-Agent Tasks
Some tasks need multiple agents in sequence:
```python
def requires_multi_agent(prompt: str) -> List[str]:
"""Detect tasks needing multiple agents."""
prompt_lower = prompt.lower()
# Research → Implement → Test
if "build new feature" in prompt_lower:
return ["research", "coder", "tester"]
# Implement → Document
if "add api endpoint" in prompt_lower:
return ["coder", "documentation"]
# Refactor → Test → Review
if "refactor and validate" in prompt_lower:
return ["refactor", "tester", "reviewer"]
# Single agent (most common)
return [select_agent(prompt, {})]
```
**Example Output:**
```markdown
## Multi-Agent Task Detected
**Agents Required**: 3
1. research - Learn best practices for OAuth2
2. coder - Implement authentication endpoints
3. tester - Write test suite with >80% coverage
**Execution Plan**:
1. Research agent: 15 min
2. Coder agent: 45 min
3. Tester agent: 30 min
**Total Estimate**: 1.5 hours
Executing agents sequentially...
```
---
## Special Cases
### 1. **Debugging Tasks**
```
User: "debug why payment API returns 500"
→ NO dedicated debug agent
→ Route to: coder (for implementation fixes)
→ Skills: Use error-handling-completeness skill
```
### 2. **Story Planning**
```
User: "plan a feature for user authentication"
→ NO dedicated agent
→ Route to: project-manager (via /lazy plan command)
```
### 3. **Mixed Tasks**
```
User: "implement OAuth2 and write tests"
→ Multiple agents needed
→ Route to:
1. coder (implement OAuth2)
2. tester (write tests)
```
---
## Performance Metrics
```markdown
## Agent Selection Metrics
**Accuracy**: 95% correct agent selection
**Speed**: <100ms selection time
**Fallback Rate**: 5% default to coder
### Common Mismatches
1. "test the implementation" → coder (should be tester)
2. "document how to use" → coder (should be documentation)
### Improvements
- Add more context signals (file types, tags)
- Learn from user feedback
- Support multi-agent workflows
```
---
## Configuration
```bash
# Disable automatic agent selection
export LAZYDEV_DISABLE_AGENT_SELECTOR=1
# Force specific agent for all tasks
export LAZYDEV_FORCE_AGENT=coder
# Log agent selection decisions
export LAZYDEV_LOG_AGENT_SELECTION=1
```
---
## What This Skill Does NOT Do
❌ Invoke agents directly (Task tool does that)
❌ Execute agent code
❌ Modify agent behavior
❌ Replace /lazy commands
❌ Handle multi-step workflows
✅ **DOES**: Analyze prompt and recommend best agent
---
## Testing the Skill
```bash
# Manual test
Skill(command="agent-selector")
# Test cases
1. "write unit tests" → tester ✓
2. "how to use FastAPI" → research ✓
3. "review this code" → reviewer ✓
4. "refactor handler" → refactor ✓
5. "add docstrings" → documentation ✓
6. "remove dead code" → cleanup ✓
7. "implement login" → coder ✓
```
---
## Quick Reference: Agent Selection
| Keywords | Agent | Use Case |
|----------|-------|----------|
| test, unittest, pytest, coverage | tester | Write/run tests |
| how to, learn, research, docs | research | Learn & discover |
| review, audit, validate, check | reviewer | Quality & security |
| refactor, clean up, simplify | refactor | Code improvement |
| document, docstring, readme | documentation | Write docs |
| remove, unused, dead code | cleanup | Delete unused code |
| add, build, implement, fix | coder | Feature implementation |
---
**Version**: 1.0.0
**Agents Supported**: 7 (coder, tester, research, reviewer, refactor, documentation, cleanup)
**Accuracy**: ~95%
**Speed**: <100ms

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---
name: brainstorming
description: Structured ideation for options, trade-offs, and a clear decision
version: 0.1.0
tags: [planning, design, options]
triggers:
- brainstorm
- options
- approaches
- design choices
---
# Brainstorming
## Purpose
Quickly generate several viable approaches with pros/cons and pick a default. Keep output compact and decision-oriented.
## When to Use
- Early feature shaping (before `/lazy create-feature`)
- Choosing patterns, libraries, or refactor strategies
## Behavior
1. Produce 3–5 options with one-sentence descriptions.
2. Table: Option | Pros | Cons | Effort | Risk.
3. Recommend one option with a 2–3 line rationale.
4. List immediate next steps (3–5 bullets).
## Output Style
- Prefer `table-based` for the options matrix + short bullets.
## Guardrails
- No long essays; keep to 1 table + short bullets.
- Avoid speculative claims; cite known repo facts when used.
## Integration
- Feed the selected option into `/lazy create-feature` and the Project Manager agent as context for story creation.
## Example Prompt
> Brainstorm approaches for adding rate limiting to the API.

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---
name: breaking-change-detector
description: Detects backward-incompatible changes to public APIs, function signatures, endpoints, and data schemas before they break production. Suggests migration paths.
---
# Breaking Change Detector Skill
**Purpose**: Catch breaking changes early, not after customers complain.
**Trigger Words**: API, endpoint, route, public, schema, model, interface, contract, signature, rename, remove, delete
---
## Quick Decision: Is This Breaking?
```python
def is_breaking_change(change: dict) -> tuple[bool, str]:
"""Fast breaking change evaluation."""
breaking_patterns = {
# Method signatures
"removed_parameter": True,
"renamed_parameter": True,
"changed_parameter_type": True,
"removed_method": True,
"renamed_method": True,
# API endpoints
"removed_endpoint": True,
"renamed_endpoint": True,
"changed_response_format": True,
"removed_response_field": True,
# Data models
"removed_field": True,
"renamed_field": True,
"changed_field_type": True,
"made_required": True,
# Return types
"changed_return_type": True,
}
# Safe changes (backward compatible)
safe_patterns = {
"added_parameter_with_default": False,
"added_optional_field": False,
"added_endpoint": False,
"added_response_field": False,
"deprecated_but_kept": False,
}
change_type = change.get("type")
return breaking_patterns.get(change_type, False), change_type
```
---
## Common Breaking Changes (With Fixes)
### 1. **Removed Function Parameter** ❌ BREAKING
```python
# BEFORE (v1.0)
def process_payment(amount, currency, user_id):
pass
# AFTER (v2.0) - BREAKS EXISTING CODE
def process_payment(amount, user_id): # Removed currency!
pass
# ✅ FIX: Keep parameter with default
def process_payment(amount, user_id, currency="USD"):
"""
Args:
currency: Deprecated in v2.0, always uses USD
"""
pass
```
**Migration Path**: Add default value, deprecate, document.
---
### 2. **Renamed Function/Method** ❌ BREAKING
```python
# BEFORE
def getUserProfile(user_id):
pass
# AFTER - BREAKS CALLS
def get_user_profile(user_id): # Renamed!
pass
# ✅ FIX: Keep both, deprecate old
def get_user_profile(user_id):
"""Get user profile (v2.0+ naming)."""
pass
def getUserProfile(user_id):
"""Deprecated: Use get_user_profile() instead."""
warnings.warn("getUserProfile is deprecated, use get_user_profile", DeprecationWarning)
return get_user_profile(user_id)
```
**Migration Path**: Alias old name → new name, add deprecation warning.
---
### 3. **Changed Response Format** ❌ BREAKING
```python
# BEFORE - Returns dict
@app.route("/api/user/<id>")
def get_user(id):
return {"id": id, "name": "Alice", "email": "alice@example.com"}
# AFTER - Returns list - BREAKS CLIENTS!
@app.route("/api/user/<id>")
def get_user(id):
return [{"id": id, "name": "Alice", "email": "alice@example.com"}]
# ✅ FIX: Keep format, add new endpoint
@app.route("/api/v2/user/<id>") # New version
def get_user_v2(id):
return [{"id": id, "name": "Alice"}]
@app.route("/api/user/<id>") # Keep v1
def get_user(id):
return {"id": id, "name": "Alice", "email": "alice@example.com"}
```
**Migration Path**: Version the API (v1, v2), keep old version alive.
---
### 4. **Removed Endpoint** ❌ BREAKING
```python
# BEFORE
@app.route("/users")
def get_users():
pass
# AFTER - REMOVED! Breaks clients.
# (endpoint deleted)
# ✅ FIX: Redirect to new endpoint
@app.route("/users")
def get_users():
"""Deprecated: Use /api/v2/accounts instead."""
return redirect("/api/v2/accounts", code=301) # Permanent redirect
```
**Migration Path**: Keep endpoint, redirect with 301, document deprecation.
---
### 5. **Changed Required Fields** ❌ BREAKING
```python
# BEFORE - email optional
class User:
def __init__(self, name, email=None):
self.name = name
self.email = email
# AFTER - email required! Breaks existing code.
class User:
def __init__(self, name, email): # No default!
self.name = name
self.email = email
# ✅ FIX: Keep optional, validate separately
class User:
def __init__(self, name, email=None):
self.name = name
self.email = email
def validate(self):
"""Validate required fields."""
if not self.email:
raise ValueError("Email is required (new in v2.0)")
```
**Migration Path**: Keep optional in constructor, add validation method.
---
### 6. **Removed Response Field** ❌ BREAKING
```python
# BEFORE
{
"id": 123,
"name": "Alice",
"age": 30,
"email": "alice@example.com"
}
# AFTER - Removed age! Breaks clients expecting it.
{
"id": 123,
"name": "Alice",
"email": "alice@example.com"
}
# ✅ FIX: Keep field with null/default
{
"id": 123,
"name": "Alice",
"age": null, # Deprecated, always null in v2.0
"email": "alice@example.com"
}
```
**Migration Path**: Keep field with null, document deprecation.
---
## Non-Breaking Changes ✅ (Safe)
### 1. **Added Optional Parameter**
```python
# BEFORE
def process_payment(amount):
pass
# AFTER - Safe! Has default
def process_payment(amount, currency="USD"):
pass
# Old calls still work:
process_payment(100) # ✅ Works
```
---
### 2. **Added Response Field**
```python
# BEFORE
{"id": 123, "name": "Alice"}
# AFTER - Safe! Added field
{"id": 123, "name": "Alice", "created_at": "2025-10-30"}
# Old clients ignore new field: ✅ Works
```
---
### 3. **Added New Endpoint**
```python
# New endpoint added
@app.route("/api/v2/users")
def get_users_v2():
pass
# Old endpoint unchanged: ✅ Safe
```
---
## Detection Strategy
### Automatic Checks
1. **Function signatures**: Compare old vs new parameters, types, names
2. **API routes**: Check for removed/renamed endpoints
3. **Data schemas**: Validate field additions/removals/renames
4. **Return types**: Detect type changes
### When to Run
- ✅ Before committing changes to public APIs
- ✅ During code review
- ✅ Before releasing new version
---
## Output Format
```markdown
## Breaking Change Report
**Status**: [✅ NO BREAKING CHANGES | ⚠️ BREAKING CHANGES DETECTED]
---
### Breaking Changes: 2
1. **[CRITICAL] Removed endpoint: GET /users**
- **Impact**: External API clients will get 404
- **File**: api/routes.py:45
- **Fix**:
```python
# Keep endpoint, redirect to new one
@app.route("/users")
def get_users():
return redirect("/api/v2/accounts", code=301)
```
- **Migration**: Add to CHANGELOG.md, notify users
2. **[HIGH] Renamed parameter: currency → currency_code**
- **Impact**: Existing function calls will fail
- **File**: payments.py:23
- **Fix**:
```python
# Accept both, deprecate old name
def process_payment(amount, currency_code=None, currency=None):
# Support old name temporarily
if currency is not None:
warnings.warn("currency is deprecated, use currency_code")
currency_code = currency
```
---
### Safe Changes: 1
1. **[SAFE] Added optional parameter: timeout (default=30)**
- **File**: api_client.py:12
- **Impact**: None, backward compatible
---
**Recommendation**:
1. Fix 2 breaking changes before merge
2. Document breaking changes in CHANGELOG.md
3. Bump major version (v1.x → v2.0) per semver
4. Notify API consumers 2 weeks before release
```
---
## Integration with Workflow
```bash
# Automatic trigger when modifying APIs
/lazy code "rename /users endpoint to /accounts"
→ breaking-change-detector triggers
→ Detects: Endpoint rename is breaking
→ Suggests: Keep /users, redirect to /accounts
→ Developer applies fix
→ Re-check: ✅ Backward compatible
# Before PR
/lazy review US-3.4
→ breaking-change-detector runs
→ Checks all API changes in PR
→ Reports breaking changes
→ PR blocked if breaking without migration plan
```
---
## Version Bumping Guide
```bash
# Semantic versioning
Given version: MAJOR.MINOR.PATCH
# Breaking change detected → Bump MAJOR
1.2.3 → 2.0.0
# New feature (backward compatible) → Bump MINOR
1.2.3 → 1.3.0
# Bug fix (backward compatible) → Bump PATCH
1.2.3 → 1.2.4
```
---
## What This Skill Does NOT Do
❌ Catch internal/private API changes (only public APIs)
❌ Test runtime compatibility (use integration tests)
❌ Manage database migrations (separate tool)
❌ Generate full migration scripts
✅ **DOES**: Detect public API breaking changes, suggest fixes, enforce versioning.
---
## Configuration
```bash
# Strict mode: flag all changes (even safe ones)
export LAZYDEV_BREAKING_STRICT=1
# Disable breaking change detection
export LAZYDEV_DISABLE_BREAKING_DETECTOR=1
# Check only specific types
export LAZYDEV_BREAKING_CHECK="endpoints,schemas"
```
---
**Version**: 1.0.0
**Follows**: Semantic Versioning 2.0.0
**Speed**: <3 seconds for typical PR

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---
name: code-review-request
description: Request and process code review efficiently with a simple rubric and patch plan
version: 0.1.0
tags: [review, quality]
triggers:
- request review
- code review
- feedback on diff
---
# Code Review Request
## Purpose
Summarize changes and request focused review with clear findings and an actionable fix plan.
## When to Use
- After quality pipeline passes in `/lazy task-exec`
- Before commit or before `/lazy story-review`
## Behavior
1. Summarize: files changed, purpose, risks (≤5 bullets).
2. Table rubric: Issue | Severity (Critical/Warning/Suggestion) | File:Line | Fix Plan.
3. Patch plan: 3–6 concrete steps grouped by severity.
4. Optional: produce a PR-ready comment block.
## Output Style
- `table-based` for findings; short bullets for summary and steps.
## Guardrails
- No auto-commits; propose diffs only.
- Separate criticals from suggestions.
## Integration
- Coder/Reviewer agents; `/lazy task-exec` before commit; `/lazy story-review` pre-PR.
## Example Prompt
> Request review for changes in `src/payments/processor.py` and tests.

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---
name: context-packer
description: Build a compact, high-signal context brief (files, symbols, recent commits) instead of pasting large code blocks
version: 0.1.0
tags: [context, tokens]
triggers:
- context
- summarize repo
- what files matter
---
# Context Packer
## Purpose
Reduce token usage by summarizing only what’s needed for the task.
## Behavior
1. Produce a 10–20 line brief:
- File map (key paths)
- Key symbols/functions/classes
- Last 3 relevant commits (subject only)
- Pointers to exact files/lines if code is needed
2. Include ≤1 short code window only if critical.
## Guardrails
- Never paste large files; link paths/lines instead.
- Prefer bullets over prose.
## Integration
- `UserPromptSubmit` enrichment; before sub-agent calls.
## Example Prompt
> Pack context to implement auth middleware with minimal tokens.

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---
name: diff-scope-minimizer
description: Keep changes narrowly scoped with a tiny patch plan and stop criteria
version: 0.1.0
tags: [refactor, productivity]
triggers:
- small diff
- minimal change
- refactor plan
---
# Diff Scope Minimizer
## Purpose
Focus on the smallest viable change to solve the problem and reduce churn.
## Behavior
1. Propose a 3–5 step patch plan with target files.
2. Estimate diff size (files/lines) and define stop criteria.
3. Re-evaluate after each step; stop if criteria met.
## Guardrails
- Avoid touching unrelated files.
- If diff grows >2× estimate, pause and re-plan.
## Integration
- `/lazy task-exec` before edits; Coder and Refactor agents.
## Example Prompt
> Plan the smallest patch to fix null handling in `src/api/users.py`.

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---
name: error-handling-completeness
description: Evaluates if error handling is sufficient for new code - checks try-catch coverage, logging, user messages, retry logic. Focuses on external calls and user-facing code.
---
# Error Handling Completeness Skill
**Purpose**: Prevent production crashes with systematic error handling.
**Trigger Words**: API call, external, integration, network, database, file, user input, async, promise, await
---
## Quick Decision: Needs Error Handling Check?
```python
def needs_error_check(code_context: dict) -> bool:
"""Decide if error handling review is needed."""
# High-risk operations (always check)
high_risk = [
"fetch", "axios", "requests", "http", # HTTP calls
"db.", "query", "execute", # Database
"open(", "read", "write", # File I/O
"json.loads", "json.parse", # JSON parsing
"int(", "float(", # Type conversions
"subprocess", "exec", # External processes
"await", "async", # Async operations
]
code = code_context.get("code", "").lower()
return any(risk in code for risk in high_risk)
```
---
## Error Handling Checklist (Fast)
### 1. **External API Calls** (Most Critical)
```python
# ❌ BAD - No error handling
def get_user_data(user_id):
response = requests.get(f"https://api.example.com/users/{user_id}")
return response.json() # What if network fails? 404? Timeout?
# ✅ GOOD - Complete error handling
def get_user_data(user_id):
try:
response = requests.get(
f"https://api.example.com/users/{user_id}",
timeout=5 # Timeout!
)
response.raise_for_status() # Check HTTP errors
return response.json()
except requests.Timeout:
logger.error(f"Timeout fetching user {user_id}")
raise ServiceUnavailableError("User service timeout")
except requests.HTTPError as e:
if e.response.status_code == 404:
raise UserNotFoundError(f"User {user_id} not found")
logger.error(f"HTTP error fetching user: {e}")
raise
except requests.RequestException as e:
logger.error(f"Network error: {e}")
raise ServiceUnavailableError("Cannot reach user service")
```
**Quick Checks**:
- ✅ Timeout set?
- ✅ HTTP errors handled?
- ✅ Network errors caught?
- ✅ Logged?
- ✅ User-friendly error returned?
---
### 2. **Database Operations**
```python
# ❌ BAD - Swallows errors
def delete_user(user_id):
try:
db.execute("DELETE FROM users WHERE id = ?", [user_id])
except Exception:
pass # Silent failure!
# ✅ GOOD - Specific handling
def delete_user(user_id):
try:
result = db.execute("DELETE FROM users WHERE id = ?", [user_id])
if result.rowcount == 0:
raise UserNotFoundError(f"User {user_id} not found")
except db.IntegrityError as e:
logger.error(f"Cannot delete user {user_id}: {e}")
raise DependencyError("User has related records")
except db.OperationalError as e:
logger.error(f"Database error: {e}")
raise DatabaseUnavailableError()
```
**Quick Checks**:
- ✅ Specific exceptions (not bare `except`)?
- ✅ Logged?
- ✅ User-friendly error?
---
### 3. **File Operations**
```python
# ❌ BAD - File might not exist
def read_config():
with open("config.json") as f:
return json.load(f)
# ✅ GOOD - Handle missing file
def read_config():
try:
with open("config.json") as f:
return json.load(f)
except FileNotFoundError:
logger.warning("config.json not found, using defaults")
return DEFAULT_CONFIG
except json.JSONDecodeError as e:
logger.error(f"Invalid JSON in config.json: {e}")
raise ConfigurationError("Malformed config.json")
except PermissionError:
logger.error("Permission denied reading config.json")
raise
```
**Quick Checks**:
- ✅ FileNotFoundError handled?
- ✅ JSON parse errors caught?
- ✅ Permission errors handled?
---
### 4. **Type Conversions**
```python
# ❌ BAD - Crash on invalid input
def process_age(age_str):
age = int(age_str) # What if "abc"?
return age * 2
# ✅ GOOD - Validated
def process_age(age_str):
try:
age = int(age_str)
if age < 0 or age > 150:
raise ValueError("Age out of range")
return age * 2
except ValueError:
raise ValidationError(f"Invalid age: {age_str}")
```
**Quick Checks**:
- ✅ ValueError caught?
- ✅ Range validation?
- ✅ Clear error message?
---
### 5. **Async/Await** (JavaScript/Python)
```javascript
// ❌ BAD - Unhandled promise rejection
async function fetchUser(id) {
const user = await fetch(`/api/users/${id}`);
return user.json(); // What if network fails?
}
// ✅ GOOD - Handled
async function fetchUser(id) {
try {
const response = await fetch(`/api/users/${id}`);
if (!response.ok) {
throw new Error(`HTTP ${response.status}`);
}
return await response.json();
} catch (error) {
console.error(`Failed to fetch user ${id}:`, error);
throw new ServiceError("Cannot fetch user");
}
}
```
**Quick Checks**:
- ✅ Try-catch around await?
- ✅ HTTP status checked?
- ✅ Logged?
---
## Error Handling Patterns
### Pattern 1: Retry with Exponential Backoff
```python
def call_api_with_retry(url, max_retries=3):
for attempt in range(max_retries):
try:
response = requests.get(url, timeout=5)
response.raise_for_status()
return response.json()
except requests.Timeout:
if attempt < max_retries - 1:
wait = 2 ** attempt # 1s, 2s, 4s
logger.warning(f"Timeout, retrying in {wait}s...")
time.sleep(wait)
else:
raise
```
**When to use**: Transient failures (network, rate limits)
---
### Pattern 2: Fallback Values
```python
def get_user_avatar(user_id):
try:
return fetch_from_cdn(user_id)
except CDNError:
logger.warning(f"CDN failed for user {user_id}, using default")
return DEFAULT_AVATAR_URL
```
**When to use**: Non-critical operations, graceful degradation
---
### Pattern 3: Circuit Breaker
```python
class CircuitBreaker:
def __init__(self, max_failures=5):
self.failures = 0
self.max_failures = max_failures
self.is_open = False
def call(self, func):
if self.is_open:
raise ServiceUnavailableError("Circuit breaker open")
try:
result = func()
self.failures = 0 # Reset on success
return result
except Exception as e:
self.failures += 1
if self.failures >= self.max_failures:
self.is_open = True
logger.error("Circuit breaker opened")
raise
```
**When to use**: Preventing cascading failures
---
## Output Format
```markdown
## Error Handling Report
**Status**: [✅ COMPLETE | ⚠️ GAPS FOUND]
---
### Missing Error Handling: 3
1. **[HIGH] No timeout on API call (api_client.py:45)**
- **Issue**: `requests.get()` has no timeout
- **Risk**: Indefinite hang if service slow
- **Fix**:
```python
response = requests.get(url, timeout=5)
```
2. **[HIGH] Unhandled JSON parse error (config.py:12)**
- **Issue**: `json.load()` not wrapped in try-catch
- **Risk**: Crash on malformed JSON
- **Fix**:
```python
try:
config = json.load(f)
except json.JSONDecodeError as e:
logger.error(f"Invalid JSON: {e}")
return DEFAULT_CONFIG
```
3. **[MEDIUM] Silent exception swallowing (db.py:89)**
- **Issue**: `except Exception: pass`
- **Risk**: Failures go unnoticed
- **Fix**: Log error or use specific exception
---
**Good Practices Found**: 2
- ✅ Database errors logged properly (db.py:34)
- ✅ Retry logic on payment API (payments.py:67)
---
**Next Steps**:
1. Add timeout to API calls (5 min)
2. Wrap JSON parsing in try-catch (2 min)
3. Remove silent exception handlers (3 min)
```
---
## What This Skill Does NOT Do
❌ Catch every possible exception (too noisy)
❌ Force try-catch everywhere (only where needed)
❌ Replace integration tests
❌ Handle business logic errors (validation, etc.)
✅ **DOES**: Check critical error-prone operations (network, I/O, parsing)
---
## Configuration
```bash
# Strict mode: check all functions
export LAZYDEV_ERROR_HANDLING_STRICT=1
# Disable error handling checks
export LAZYDEV_DISABLE_ERROR_CHECKS=1
```
---
**Version**: 1.0.0
**Focus**: External calls, I/O, parsing, async
**Speed**: <2 seconds per file

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---
name: finishing-a-development-branch
description: Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
---
# Finishing a Development Branch
## Overview
Guide completion of development work by presenting clear options and handling chosen workflow.
**Core principle:** Verify tests → Present options → Execute choice → Clean up.
**Announce at start:** "I'm using the finishing-a-development-branch skill to complete this work."
## The Process
### Step 1: Verify Tests
**Before presenting options, verify tests pass:**
```bash
# Run project's test suite
npm test / cargo test / pytest / go test ./...
```
**If tests fail:**
```
Tests failing (<N> failures). Must fix before completing:
[Show failures]
Cannot proceed with merge/PR until tests pass.
```
Stop. Don't proceed to Step 2.
**If tests pass:** Continue to Step 2.
### Step 2: Determine Base Branch
```bash
# Try common base branches
git merge-base HEAD main 2>/dev/null || git merge-base HEAD master 2>/dev/null
```
Or ask: "This branch split from main - is that correct?"
### Step 3: Present Options
Present exactly these 4 options:
```
Implementation complete. What would you like to do?
1. Merge back to <base-branch> locally
2. Push and create a Pull Request
3. Keep the branch as-is (I'll handle it later)
4. Discard this work
Which option?
```
**Don't add explanation** - keep options concise.
### Step 4: Execute Choice
#### Option 1: Merge Locally
```bash
# Switch to base branch
git checkout <base-branch>
# Pull latest
git pull
# Merge feature branch
git merge <feature-branch>
# Verify tests on merged result
<test command>
# If tests pass
git branch -d <feature-branch>
```
Then: Cleanup worktree (Step 5)
#### Option 2: Push and Create PR
```bash
# Push branch
git push -u origin <feature-branch>
# Create PR
gh pr create --title "<title>" --body "$(cat <<'EOF'
## Summary
<2-3 bullets of what changed>
## Test Plan
- [ ] <verification steps>
EOF
)"
```
Then: Cleanup worktree (Step 5)
#### Option 3: Keep As-Is
Report: "Keeping branch <name>. Worktree preserved at <path>."
**Don't cleanup worktree.**
#### Option 4: Discard
**Confirm first:**
```
This will permanently delete:
- Branch <name>
- All commits: <commit-list>
- Worktree at <path>
Type 'discard' to confirm.
```
Wait for exact confirmation.
If confirmed:
```bash
git checkout <base-branch>
git branch -D <feature-branch>
```
Then: Cleanup worktree (Step 5)
### Step 5: Cleanup Worktree
**For Options 1, 2, 4:**
Check if in worktree:
```bash
git worktree list | grep $(git branch --show-current)
```
If yes:
```bash
git worktree remove <worktree-path>
```
**For Option 3:** Keep worktree.
## Quick Reference
| Option | Merge | Push | Keep Worktree | Cleanup Branch |
|--------|-------|------|---------------|----------------|
| 1. Merge locally | ✓ | - | - | ✓ |
| 2. Create PR | - | ✓ | ✓ | - |
| 3. Keep as-is | - | - | ✓ | - |
| 4. Discard | - | - | - | ✓ (force) |
## Common Mistakes
**Skipping test verification**
- **Problem:** Merge broken code, create failing PR
- **Fix:** Always verify tests before offering options
**Open-ended questions**
- **Problem:** "What should I do next?" → ambiguous
- **Fix:** Present exactly 4 structured options
**Automatic worktree cleanup**
- **Problem:** Remove worktree when might need it (Option 2, 3)
- **Fix:** Only cleanup for Options 1 and 4
**No confirmation for discard**
- **Problem:** Accidentally delete work
- **Fix:** Require typed "discard" confirmation
## Red Flags
**Never:**
- Proceed with failing tests
- Merge without verifying tests on result
- Delete work without confirmation
- Force-push without explicit request
**Always:**
- Verify tests before offering options
- Present exactly 4 options
- Get typed confirmation for Option 4
- Clean up worktree for Options 1 & 4 only
## Integration
**Called by:**
- **subagent-driven-development** (Step 7) - After all tasks complete
- **executing-plans** (Step 5) - After all batches complete
**Pairs with:**
- **using-git-worktrees** - Cleans up worktree created by that skill

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---
name: gh-issue-sync
description: Create or update GitHub issue for the story and sub-issues for tasks
version: 0.1.0
tags: [github, pm]
triggers:
- create github issue
- sync issues
- sub-issues
---
# GitHub Issue Sync
## Purpose
Keep GitHub issues in sync with local USER-STORY and TASK files.
## Behavior
1. Draft issue titles/bodies from local files (story + tasks).
2. Propose labels and links (paths, story ID, task IDs).
3. Output GitHub CLI commands (dry-run by default); confirm before executing.
## Guardrails
- Do not post without explicit confirmation.
- Reflect exactly what exists on disk; no invented tasks.
## Integration
- After `/lazy create-feature` creates files; optional during `/lazy story-review`.
## Example Prompt
> Prepare GitHub issues for US-20251027-001 and its tasks (dry run).

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---
name: git-worktrees
description: Use Git worktrees to isolate tasks and keep diffs small and parallelizable
version: 0.1.0
tags: [git, workflow]
triggers:
- worktree
- parallelize tasks
- spike branch
---
# Git Worktrees
## Purpose
Create parallel worktrees for distinct tasks to keep changes isolated and reviews clean.
## When to Use
- Parallel task execution; spikes; conflicting changes
## Behavior
1. Pre-check: `git status --porcelain` must be clean.
2. Suggest names: `wt-TASK-<id>` or `wt-<short-topic>`.
3. Commands:
- Create: `git worktree add ../<name> <base-branch>`
- Switch: open the new dir; confirm branch
- Remove (after merge): `git worktree remove ../<name>`
4. Cleanup checklist.
## Guardrails
- Never create/remove with dirty status.
- Echo exact commands; do not execute automatically.
## Integration
- `/lazy task-exec` (optional), Coder agent setup phase.
## Example Prompt
> Create a dedicated worktree for TASK-1.2 on top of `feature/auth`.

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---
name: memory-graph
description: Persistent memory graph skill using the MCP Memory server
audience: agents
visibility: project
---
# Memory Graph Skill
This skill teaches you how to create, update, search, and prune a persistent knowledge graph using the Model Context Protocol (MCP) Memory server.
When connected, Memory tools appear as MCP tools named like `mcp__memory__<tool>`. Use these tools proactively whenever you identify durable facts, entities, or relationships you want to persist across sessions.
See `operations.md` for exact tool I/O shapes and `playbooks.md` for common patterns and routing rules.
## When To Use
- New durable facts emerge (requirements, decisions, owners, IDs, endpoints)
- You meet a new entity (person, team, service, repository, dataset)
- You discover relationships ("Service A depends on Service B", "Alice owns Repo X")
- You want to reference prior sessions or quickly search memory
- You need to prune or correct stale memory
## Golden Rules
- Prefer small, well-typed entities over long notes
- Record relationships in active voice: `relationType` describes how `from` relates to `to`
- Add observations as atomic strings; include dates or sources when helpful
- Before creating, search existing nodes to avoid duplicates
- When correcting, prefer `delete_observations` then `add_observations` over overwriting
## Auto Triggers
- UserPromptSubmit adds a Memory Graph activation block when durable facts or explicit memory intents are detected. Disable with `LAZYDEV_DISABLE_MEMORY_SKILL=1`.
- PostToolUse emits lightweight suggestions when tool results include durable facts. Disable with `LAZYDEV_DISABLE_MEMORY_SUGGEST=1`.
## Tooling Summary (server "memory")
- `create_entities`, `add_observations`, `create_relations`
- `delete_entities`, `delete_observations`, `delete_relations`
- `read_graph`, `search_nodes`, `open_nodes`
Always call tools with the fully-qualified MCP name, for example: `mcp__memory__create_entities`.
## Minimal Flow
1) `mcp__memory__search_nodes` for likely duplicates
2) `mcp__memory__create_entities` as needed
3) `mcp__memory__add_observations` with concise facts
4) `mcp__memory__create_relations` to wire the graph
5) Optional: `mcp__memory__open_nodes` to verify saved nodes
## Error Handling
- If create fails due to existing name, switch to `add_observations`
- If `add_observations` fails (unknown entity), retry with `create_entities`
- All delete tools are safe on missing targets (no-op)
## Examples
See `examples.md` for end-to-end examples covering projects, APIs, and people.

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# Examples
All examples assume the Memory MCP server is connected under the name `memory`, so tool names are `mcp__memory__...`.
## Project/Service
Persist a service and its basics.
1) Prevent duplicates
```
tool: mcp__memory__search_nodes
input: {"query": "service:alpha"}
```
2) Create entity if missing
```
tool: mcp__memory__create_entities
input: {
"entities": [
{
"name": "service:alpha",
"entityType": "service",
"observations": [
"owner: alice",
"repo: github.com/org/alpha",
"primary_language: python",
"deploy_url: https://alpha.example.com"
]
}
]
}
```
3) Add relation to its owner
```
tool: mcp__memory__create_relations
input: {
"relations": [
{"from": "service:alpha", "to": "person:alice", "relationType": "owned_by"}
]
}
```
## People
Create or enrich people entities.
```
tool: mcp__memory__create_entities
input: {"entities": [{"name": "person:alice", "entityType": "person", "observations": ["email: alice@example.com"]}]}
```
Add title change
```
tool: mcp__memory__add_observations
input: {"observations": [{"entityName": "person:alice", "contents": ["title: Staff Engineer (2025-10-27)"]}]}
```
## Corrections
Remove stale owner, add new owner.
```
tool: mcp__memory__delete_observations
input: {"deletions": [{"entityName": "service:alpha", "observations": ["owner: alice"]}]}
```
```
tool: mcp__memory__add_observations
input: {"observations": [{"entityName": "service:alpha", "contents": ["owner: bob"]}]}
```

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# Memory Graph Operations (I/O)
Use the fully-qualified tool names with the MCP prefix: `mcp__memory__<tool>`.
All tools below belong to the server `memory`.
## create_entities
Create multiple new entities. Skips any entity whose `name` already exists.
Input
```
{
"entities": [
{
"name": "string",
"entityType": "string",
"observations": ["string", "string"]
}
]
}
```
## create_relations
Create multiple relations. Skips duplicates.
Input
```
{
"relations": [
{
"from": "string",
"to": "string",
"relationType": "string" // active voice, e.g. "depends_on", "owned_by"
}
]
}
```
## add_observations
Add observations to existing entities. Fails if `entityName` doesn’t exist.
Input
```
{
"observations": [
{
"entityName": "string",
"contents": ["string", "string"]
}
]
}
```
## delete_entities
Remove entities and cascade their relations. No-op if missing.
Input
```
{ "entityNames": ["string", "string"] }
```
## delete_observations
Remove specific observations from entities. No-op if missing.
Input
```
{
"deletions": [
{
"entityName": "string",
"observations": ["string", "string"]
}
]
}
```
## delete_relations
Remove specific relations. No-op if missing.
Input
```
{
"relations": [
{
"from": "string",
"to": "string",
"relationType": "string"
}
]
}
```
## read_graph
Return the entire graph.
Input: none
## search_nodes
Fuzzy search across entity names, types, and observations.
Input
```
{ "query": "string" }
```
## open_nodes
Return specific nodes and relations connecting them. Skips non-existent names.
Input
```
{ "names": ["string", "string"] }
```

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# Memory Graph Playbooks
Use these routing patterns to decide which tools to call and in what order.
## 1) Persist a New Entity (+ facts)
1. `mcp__memory__search_nodes` with the proposed name
2. If not found → `mcp__memory__create_entities`
3. Then `mcp__memory__add_observations`
4. Optionally `mcp__memory__open_nodes` to verify
Example intent → tools
- Intent: “Remember service Alpha (owner: Alice, repo: org/alpha)”
- Tools:
- `create_entities` → name: "service:alpha", type: "service"
- `add_observations` → key facts (owner, repo URL, language, deploy URL)
## 2) Add Relations Between Known Entities
1. `mcp__memory__open_nodes` for both
2. If either missing → create it first
3. `mcp__memory__create_relations`
Relation guidance
- Use active voice `relationType`: `depends_on`, `owned_by`, `maintained_by`, `deployed_to`, `docs_at`
- Prefer directional relations; add reverse relation only if it has a different meaning
## 3) Correct or Update Facts
1. `mcp__memory__open_nodes`
2. `mcp__memory__delete_observations` to remove stale/incorrect facts
3. `mcp__memory__add_observations` to append correct facts
## 4) Remove Entities or Links
- `mcp__memory__delete_relations` for just the link
- `mcp__memory__delete_entities` for full removal (cascades relations)
## 5) Explore or Export
- `mcp__memory__read_graph` to dump entire graph
- `mcp__memory__search_nodes` to find relevant nodes by keyword
- For focused context, use `mcp__memory__open_nodes` with names
## 6) Session Rhythm
- Before deep work: `search_nodes` or `open_nodes` for today’s entities
- During work: add small observations at decision points
- After work: link new entities and summarize outcomes as observations

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---
name: output-style-selector
description: Automatically choose the best output style (tables, bullets, YAML, HTML, concise) to improve scanability and save tokens
version: 0.1.0
tags: [formatting, context]
triggers:
- style
- format
- output style
---
# Output Style Selector
## Purpose
Select a response style that maximizes readability and minimizes back-and-forth.
## Behavior
1. Infer intent from prompt keywords and task type.
2. Choose one of: table-based, bullet-points, yaml-structured, html-structured, genui, ultra-concise, markdown-focused.
3. Emit a short “Style Block” (1–2 lines) describing the chosen style.
4. Respect overrides: `[style: <name>]` or `[style: off]`.
## Guardrails
- Only inject when helpful; avoid long style instructions.
- Keep the Style Block compact.
## Integration
- `UserPromptSubmit` and sub-agent prompts (documentation, reviewer, PM).
## Example Style Block
> Style: Table Based. Use a summary paragraph and then tables for comparisons and actions.

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---
name: performance-budget-checker
description: Detects performance anti-patterns like N+1 queries, nested loops, large file operations, and inefficient algorithms. Suggests fast fixes before issues reach production.
---
# Performance Budget Checker Skill
**Purpose**: Catch performance killers before they slow production.
**Trigger Words**: query, database, loop, for, map, filter, file, read, load, fetch, API, cache
---
## Quick Decision: Check Performance?
```python
def needs_perf_check(code_context: dict) -> bool:
"""Fast performance risk evaluation."""
# Performance-critical patterns
patterns = [
"for ", "while ", "map(", "filter(", # Loops
"db.", "query", "select", "fetch", # Database
".all()", ".filter(", ".find(", # ORM queries
"open(", "read", "readlines", # File I/O
"json.loads", "pickle.load", # Deserialization
"sorted(", "sort(", # Sorting
"in list", "in array", # Linear search
]
code = code_context.get("code", "").lower()
return any(p in code for p in patterns)
```
---
## Performance Anti-Patterns (Quick Fixes)
### 1. **N+1 Query Problem** (Most Common) ⚠️
```python
# ❌ BAD - 1 + N queries (slow!)
def get_users_with_posts():
users = User.query.all() # 1 query
for user in users:
user.posts = Post.query.filter_by(user_id=user.id).all() # N queries!
return users
# Performance: 101 queries for 100 users
# ✅ GOOD - 1 query with JOIN
def get_users_with_posts():
users = User.query.options(joinedload(User.posts)).all() # 1 query
return users
# Performance: 1 query for 100 users
# Or use prefetch
def get_users_with_posts():
users = User.query.all()
user_ids = [u.id for u in users]
posts = Post.query.filter(Post.user_id.in_(user_ids)).all()
# Group posts by user_id manually
return users
```
**Quick Fix**: Use `joinedload()`, `selectinload()`, or batch fetch.
---
### 2. **Nested Loops** ⚠️
```python
# ❌ BAD - O(n²) complexity
def find_common_items(list1, list2):
common = []
for item1 in list1: # O(n)
for item2 in list2: # O(n)
if item1 == item2:
common.append(item1)
return common
# Performance: 1,000,000 operations for 1000 items each
# ✅ GOOD - O(n) with set
def find_common_items(list1, list2):
return list(set(list1) & set(list2))
# Performance: 2000 operations for 1000 items each
```
**Quick Fix**: Use set intersection, dict lookup, or hash map.
---
### 3. **Inefficient Filtering** ⚠️
```python
# ❌ BAD - Fetch all, then filter in Python
def get_active_users():
all_users = User.query.all() # Fetch 10,000 users
active = [u for u in all_users if u.is_active] # Filter in memory
return active
# Performance: 10,000 rows transferred, filtered in Python
# ✅ GOOD - Filter in database
def get_active_users():
return User.query.filter_by(is_active=True).all()
# Performance: Only active users transferred
```
**Quick Fix**: Push filtering to database with WHERE clause.
---
### 4. **Large File Loading** ⚠️
```python
# ❌ BAD - Load entire file into memory
def process_large_file(filepath):
with open(filepath) as f:
data = f.read() # 1GB file → 1GB memory!
for line in data.split('\n'):
process_line(line)
# ✅ GOOD - Stream line by line
def process_large_file(filepath):
with open(filepath) as f:
for line in f: # Streaming, ~4KB at a time
process_line(line.strip())
```
**Quick Fix**: Stream files instead of loading fully.
---
### 5. **Missing Pagination** ⚠️
```python
# ❌ BAD - Return all 100,000 records
@app.route("/api/users")
def get_users():
return User.query.all() # 100,000 rows!
# ✅ GOOD - Paginate
@app.route("/api/users")
def get_users():
page = request.args.get('page', 1, type=int)
per_page = request.args.get('per_page', 50, type=int)
return User.query.paginate(page=page, per_page=per_page)
```
**Quick Fix**: Add pagination to list endpoints.
---
### 6. **No Caching** ⚠️
```python
# ❌ BAD - Recompute every time
def get_top_products():
# Expensive computation every request
products = Product.query.all()
sorted_products = sorted(products, key=lambda p: p.sales, reverse=True)
return sorted_products[:10]
# ✅ GOOD - Cache for 5 minutes
from functools import lru_cache
import time
@lru_cache(maxsize=1)
def get_top_products_cached():
cache_key = int(time.time() // 300) # 5 min buckets
return _compute_top_products()
def _compute_top_products():
products = Product.query.all()
sorted_products = sorted(products, key=lambda p: p.sales, reverse=True)
return sorted_products[:10]
```
**Quick Fix**: Add caching for expensive computations.
---
### 7. **Linear Search in List** ⚠️
```python
# ❌ BAD - O(n) lookup
user_ids = [1, 2, 3, ..., 10000] # List
if 9999 in user_ids: # Scans entire list
pass
# ✅ GOOD - O(1) lookup
user_ids = {1, 2, 3, ..., 10000} # Set
if 9999 in user_ids: # Instant lookup
pass
```
**Quick Fix**: Use set/dict for lookups instead of list.
---
### 8. **Synchronous I/O in Loop** ⚠️
```python
# ❌ BAD - Sequential API calls (slow)
def fetch_user_data(user_ids):
results = []
for user_id in user_ids: # 100 users
data = requests.get(f"/api/users/{user_id}").json() # 200ms each
results.append(data)
return results
# Performance: 100 × 200ms = 20 seconds!
# ✅ GOOD - Parallel requests
import asyncio
import aiohttp
async def fetch_user_data(user_ids):
async with aiohttp.ClientSession() as session:
tasks = [fetch_one(session, uid) for uid in user_ids]
results = await asyncio.gather(*tasks)
return results
async def fetch_one(session, user_id):
async with session.get(f"/api/users/{user_id}") as resp:
return await resp.json()
# Performance: ~200ms total (parallel)
```
**Quick Fix**: Use async/await or threading for I/O-bound operations.
---
## Performance Budget Guidelines
| Operation | Acceptable | Warning | Critical |
|-----------|-----------|---------|----------|
| API response time | <200ms | 200-500ms | >500ms |
| Database query | <50ms | 50-200ms | >200ms |
| List endpoint | <100 items | 100-1000 | >1000 |
| File operation | <1MB | 1-10MB | >10MB |
| Loop iterations | <1000 | 1000-10000 | >10000 |
---
## Output Format
```markdown
## Performance Report
**Status**: [✅ WITHIN BUDGET | ⚠️ ISSUES FOUND]
---
### Performance Issues: 2
1. **[HIGH] N+1 Query in get_user_posts() (api.py:34)**
- **Issue**: 1 + 100 queries (101 total)
- **Impact**: ~500ms for 100 users
- **Fix**:
```python
# Change this:
users = User.query.all()
for user in users:
user.posts = Post.query.filter_by(user_id=user.id).all()
# To this:
users = User.query.options(joinedload(User.posts)).all()
```
- **Expected**: 500ms → 50ms (10x faster)
2. **[MEDIUM] No pagination on /api/products (routes.py:45)**
- **Issue**: Returns all 5,000 products
- **Impact**: 2MB response, slow load
- **Fix**:
```python
@app.route("/api/products")
def get_products():
page = request.args.get('page', 1, type=int)
return Product.query.paginate(page=page, per_page=50)
```
---
### Optimizations Applied: 1
- ✅ Used set() for user_id lookup (utils.py:23) - O(1) instead of O(n)
---
**Next Steps**:
1. Fix N+1 query with joinedload (5 min fix)
2. Add pagination to /api/products (10 min)
3. Consider adding Redis cache for top products
```
---
## When to Skip Performance Checks
✅ Skip for:
- Prototypes/POCs
- Admin-only endpoints (low traffic)
- One-time scripts
- Small datasets (<100 items)
⚠️ Always check for:
- Public APIs
- User-facing endpoints
- High-traffic pages
- Data processing pipelines
---
## What This Skill Does NOT Do
❌ Run actual benchmarks (use profiling tools)
❌ Optimize algorithms (focus on anti-patterns)
❌ Check infrastructure (servers, CDN, etc.)
❌ Replace load testing
✅ **DOES**: Detect common performance anti-patterns with quick fixes.
---
## Configuration
```bash
# Strict mode: check all loops and queries
export LAZYDEV_PERF_STRICT=1
# Disable performance checks
export LAZYDEV_DISABLE_PERF_CHECKS=1
# Set custom thresholds
export LAZYDEV_PERF_MAX_QUERY_TIME=100 # ms
export LAZYDEV_PERF_MAX_LOOP_SIZE=5000
```
---
## Quick Reference: Common Fixes
| Anti-Pattern | Fix | Time Complexity |
|--------------|-----|-----------------|
| N+1 queries | `joinedload()` | O(n) → O(1) |
| Nested loops | Use set/dict | O(n²) → O(n) |
| Load full file | Stream lines | O(n) memory → O(1) |
| No pagination | `.paginate()` | O(n) → O(page_size) |
| Linear search | Use set | O(n) → O(1) |
| Sync I/O loop | async/await | O(n×t) → O(t) |
---
**Version**: 1.0.0
**Focus**: Database, loops, I/O, caching
**Speed**: <3 seconds per file

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---
name: project-docs-sync
description: Automatically synchronize project documentation when major changes occur (new tech, architecture changes, requirements shifts). Detects significant updates and propagates changes across TECH-STACK.md, ARCHITECTURE.md, and SPECIFICATIONS.md.
---
# Project Documentation Sync Skill
**Purpose**: Keep project documentation consistent without manual syncing overhead.
**Trigger**: Auto-invoked by PostToolUse hook when files in `project-management/` are edited.
---
## Decision Logic: Should We Sync?
```python
def should_sync(change: dict) -> tuple[bool, str]:
"""Conservative sync decision - only on big changes."""
# Track last sync state
last_sync = load_last_sync() # from .meta/last-sync.json
significant_changes = {
# Technology changes
"added_technology": True, # New language, framework, library
"removed_technology": True, # Deprecated/removed tech
"upgraded_major_version": True, # React 17 → 18, Python 3.10 → 3.11
# Architecture changes
"added_service": True, # New microservice, component
"removed_service": True, # Deprecated service
"changed_data_flow": True, # New integration pattern
"added_integration": True, # New third-party API
# Requirements changes
"new_security_requirement": True,
"new_performance_requirement": True,
"changed_api_contract": True,
"added_compliance_need": True,
}
# Skip minor changes
minor_changes = {
"typo_fix": False,
"formatting": False,
"comment_update": False,
"example_clarification": False,
}
change_type = classify_change(change, last_sync)
return significant_changes.get(change_type, False), change_type
```
---
## What Gets Synced (Conservative Strategy)
### 1. TECH-STACK.md Changed → Update ARCHITECTURE.md
**Triggers:**
- Added new language/framework (e.g., added Redis)
- Removed technology (e.g., removed MongoDB)
- Major version upgrade (e.g., React 17 → 18)
**Sync Actions:**
```markdown
TECH-STACK.md shows:
+ Redis 7.x (added for caching)
→ Update ARCHITECTURE.md:
- Add Redis component to architecture diagram
- Add caching layer to data flow
- Document Redis connection pattern
```
**Example Output:**
```
✓ Synced TECH-STACK.md → ARCHITECTURE.md
- Added: Redis caching layer
- Updated: Data flow diagram (added cache lookup)
- Reason: New technology requires architectural integration
```
---
### 2. ARCHITECTURE.md Changed → Update SPECIFICATIONS.md
**Triggers:**
- New service/component added
- API gateway pattern introduced
- Data model changed
- Integration pattern modified
**Sync Actions:**
```markdown
ARCHITECTURE.md shows:
+ API Gateway (Kong) added between clients and services
→ Update SPECIFICATIONS.md:
- Add API Gateway endpoints
- Update authentication flow
- Add rate limiting specs
- Update API contract examples
```
**Example Output:**
```
✓ Synced ARCHITECTURE.md → SPECIFICATIONS.md
- Added: API Gateway endpoint specs
- Updated: Authentication flow (now via gateway)
- Reason: Architectural change affects API contracts
```
---
### 3. PROJECT-OVERVIEW.md Changed → Validate Consistency
**Triggers:**
- Project scope changed
- New requirement category added
- Compliance requirement added
- Target users changed
**Sync Actions:**
```markdown
PROJECT-OVERVIEW.md shows:
+ Compliance: GDPR data privacy required
→ Validate across all docs:
- Check TECH-STACK.md has encryption libraries
- Check ARCHITECTURE.md has data privacy layer
- Check SPECIFICATIONS.md has GDPR endpoints (data export, deletion)
- Flag missing pieces
```
**Example Output:**
```
⚠ Validation: PROJECT-OVERVIEW.md → ALL DOCS
- Missing in TECH-STACK.md: No encryption library listed
- Missing in SPECIFICATIONS.md: No GDPR data export endpoint
- Recommendation: Add encryption lib + GDPR API specs
```
---
## Change Detection Algorithm
```python
def classify_change(file_path: str, diff: str, last_sync: dict) -> str:
"""Classify change significance using diff analysis."""
# Parse diff
added_lines = [line for line in diff.split('\n') if line.startswith('+')]
removed_lines = [line for line in diff.split('\n') if line.startswith('-')]
# Check for technology changes
tech_keywords = ['framework', 'library', 'language', 'database', 'cache']
if any(kw in line.lower() for line in added_lines for kw in tech_keywords):
if any(removed_lines): # Replacement
return "upgraded_major_version"
return "added_technology"
# Check for architecture changes
arch_keywords = ['service', 'component', 'layer', 'gateway', 'microservice']
if any(kw in line.lower() for line in added_lines for kw in arch_keywords):
return "added_service"
# Check for requirement changes
req_keywords = ['security', 'performance', 'compliance', 'GDPR', 'HIPAA']
if any(kw in line.lower() for line in added_lines for kw in req_keywords):
return "new_security_requirement"
# Check for API contract changes
if 'endpoint' in diff.lower() or 'route' in diff.lower():
return "changed_api_contract"
# Default: minor change (skip sync)
if len(added_lines) < 3 and not removed_lines:
return "typo_fix"
return "unknown_change"
```
---
## Sync State Tracking
**Storage**: `.meta/last-sync.json`
```json
{
"last_sync_timestamp": "2025-10-30T14:30:00Z",
"synced_files": {
"project-management/TECH-STACK.md": {
"hash": "abc123",
"last_modified": "2025-10-30T14:00:00Z",
"change_type": "added_technology"
},
"project-management/ARCHITECTURE.md": {
"hash": "def456",
"last_modified": "2025-10-30T14:30:00Z",
"synced_from": "TECH-STACK.md"
}
},
"pending_syncs": []
}
```
**Update Logic**:
1. After Write/Edit to `project-management/*.md`
2. Calculate file hash (md5 of content)
3. Compare with last sync state
4. If different + significant change → Trigger sync
5. Update `.meta/last-sync.json`
---
## Sync Execution Flow
```
PostToolUse Hook Fires
↓
File edited: project-management/TECH-STACK.md
↓
Load .meta/last-sync.json
↓
Calculate diff from last sync
↓
Classify change: "added_technology" (Redis)
↓
Decision: should_sync() → TRUE
↓
┌────────────────────────────────────┐
│ Sync: TECH-STACK → ARCHITECTURE │
│ - Read TECH-STACK.md additions │
│ - Identify: Redis 7.x (cache) │
│ - Update ARCHITECTURE.md: │
│ + Add Redis component │
│ + Update data flow │
└────────────────────────────────────┘
↓
Write updated ARCHITECTURE.md
↓
Update .meta/last-sync.json
↓
Log sync action
↓
Output brief sync report
```
---
## Sync Report Format
```markdown
## Documentation Sync Report
**Trigger**: TECH-STACK.md modified (added Redis)
**Timestamp**: 2025-10-30T14:30:00Z
---
### Changes Detected: 1
1. **[SIGNIFICANT] Added technology: Redis 7.x**
- **Source**: project-management/TECH-STACK.md:45
- **Purpose**: Caching layer for API responses
---
### Syncs Applied: 2
1. **TECH-STACK.md → ARCHITECTURE.md**
- ✓ Added: Redis component to architecture diagram
- ✓ Updated: Data flow (added cache lookup step)
- ✓ File: project-management/ARCHITECTURE.md:120-135
2. **TECH-STACK.md → SPECIFICATIONS.md**
- ✓ Added: Cache invalidation API endpoint
- ✓ Updated: Response time expectations (now <100ms with cache)
- ✓ File: project-management/SPECIFICATIONS.md:78-82
---
### Validation Checks: 2
✓ TECH-STACK.md consistency: OK
✓ ARCHITECTURE.md alignment: OK
---
**Result**: Documentation synchronized successfully.
**Next Action**: Review changes in next commit.
```
---
## Integration with PostToolUse Hook
**Hook Location**: `.claude/hooks/post_tool_use_format.py`
**Trigger Condition**:
```python
def should_trigger_docs_sync(file_path: str, tool_name: str) -> bool:
"""Only trigger on project-management doc edits."""
if tool_name not in ["Write", "Edit"]:
return False
project_docs = [
"project-management/TECH-STACK.md",
"project-management/ARCHITECTURE.md",
"project-management/PROJECT-OVERVIEW.md",
"project-management/SPECIFICATIONS.md",
]
return any(doc in file_path for doc in project_docs)
```
**Invocation**:
```python
# In PostToolUse hook
if should_trigger_docs_sync(file_path, tool_name):
# Load skill
skill_result = invoke_skill("project-docs-sync", {
"file_path": file_path,
"change_type": classify_change(file_path, diff),
"last_sync_state": load_last_sync()
})
# Log sync action
log_sync_action(skill_result)
```
---
## Sync Strategies by File Type
### TECH-STACK.md → ARCHITECTURE.md
**What to sync:**
- New databases → Add data layer component
- New frameworks → Add to tech stack diagram
- New APIs → Add integration points
- Version upgrades → Update compatibility notes
### ARCHITECTURE.md → SPECIFICATIONS.md
**What to sync:**
- New services → Add service endpoints
- New integrations → Add API contracts
- Data model changes → Update request/response schemas
- Security layers → Add authentication specs
### PROJECT-OVERVIEW.md → ALL DOCS
**What to validate:**
- Compliance requirements → Check encryption in TECH-STACK
- Performance goals → Check caching in ARCHITECTURE
- Target users → Check API design in SPECIFICATIONS
- Scope changes → Validate alignment across all docs
---
## Conservative Sync Rules
**DO Sync When:**
- ✅ New technology added (database, framework, library)
- ✅ Service/component added or removed
- ✅ API contract changed (new endpoint, schema change)
- ✅ Compliance requirement added (GDPR, HIPAA)
- ✅ Major version upgrade (breaking changes possible)
**DO NOT Sync When:**
- ❌ Typo fixes (1-2 character changes)
- ❌ Formatting changes (whitespace, markdown)
- ❌ Comment/example clarifications
- ❌ Documentation of existing features (no new info)
- ❌ Minor version bumps (patch releases)
---
## Error Handling
**If sync fails:**
1. Log error to `.meta/sync-errors.log`
2. Add to pending syncs in `.meta/last-sync.json`
3. Report to user with clear action items
4. Do NOT block the write operation (non-blocking)
**Example Error Report:**
```
⚠ Documentation Sync Failed
**File**: project-management/TECH-STACK.md
**Error**: Could not parse ARCHITECTURE.md (syntax error)
**Action Required**:
1. Fix ARCHITECTURE.md syntax error (line 45)
2. Re-run: /lazy docs-sync
**Pending Syncs**: 1 (tracked in .meta/last-sync.json)
```
---
## Configuration
```bash
# Disable auto-sync (manual /lazy docs-sync only)
export LAZYDEV_DISABLE_DOCS_SYNC=1
# Sync everything (even minor changes)
export LAZYDEV_DOCS_SYNC_AGGRESSIVE=1
# Sync specific files only
export LAZYDEV_DOCS_SYNC_FILES="TECH-STACK.md,ARCHITECTURE.md"
```
---
## What This Skill Does NOT Do
❌ Sync code files (only project-management docs)
❌ Generate docs from scratch (use `/lazy docs`)
❌ Fix documentation errors (use `/lazy fix`)
❌ Create missing docs (use `/lazy plan`)
✅ **DOES**: Automatically propagate significant changes across project documentation with conservative triggers.
---
**Version**: 1.0.0
**Non-blocking**: Syncs in background, logs errors
**Speed**: <2 seconds for typical sync

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---
name: project-planner
description: Transforms project ideas into structured documentation (overview + specifications). Use when starting new projects or when brief needs project-level planning with vision, features, and technical requirements.
---
# Project Planner Skill
**Purpose**: Generate comprehensive project documentation from high-level descriptions.
**Trigger Words**: new project, project overview, project spec, technical requirements, project planning, architecture, system design
---
## Quick Decision: Use Project Planning?
```python
def needs_project_planning(context: dict) -> bool:
"""Fast evaluation for project-level planning."""
# Indicators of project-level work
project_indicators = [
"new project", "project overview", "system design",
"architecture", "technical requirements", "project spec",
"build a", "create a", "develop a platform",
"microservices", "full stack", "api + frontend"
]
description = context.get("description", "").lower()
return any(indicator in description for indicator in project_indicators)
```
---
## Output Structure
Generates TWO documents in `project-management/`:
### 1. PROJECT-OVERVIEW.md
High-level vision and goals
### 2. SPECIFICATIONS.md
Detailed technical requirements
---
## Document 1: PROJECT-OVERVIEW.md
### Template Structure
```markdown
# {Project Name}
> {Tagline - one compelling sentence}
## Vision
{2-3 sentences describing the ultimate goal and impact}
## Goals
1. {Primary goal}
2. {Secondary goal}
3. {Tertiary goal}
## Key Features
- **{Feature 1}**: {Brief description}
- **{Feature 2}**: {Brief description}
- **{Feature 3}**: {Brief description}
- **{Feature 4}**: {Brief description}
- **{Feature 5}**: {Brief description}
## Success Criteria
1. **{Metric 1}**: {Target}
2. **{Metric 2}**: {Target}
3. **{Metric 3}**: {Target}
## Constraints
- **Budget**: {If specified}
- **Timeline**: {If specified}
- **Technology**: {Required tech stack or limitations}
- **Team**: {Team size/composition if known}
## Out of Scope
- {What this project will NOT do}
- {Features explicitly excluded}
- {Future phases}
```
### Example Output
```markdown
# TaskFlow Pro
> Modern task management with AI-powered prioritization
## Vision
Build a task management platform that helps remote teams stay organized through intelligent prioritization, real-time collaboration, and seamless integrations with existing tools.
## Goals
1. Reduce task management overhead by 50%
2. Enable real-time team collaboration
3. Integrate with popular dev tools (GitHub, Jira, Slack)
## Key Features
- **AI Prioritization**: ML-based task ranking by urgency and impact
- **Real-time Collaboration**: Live updates, comments, mentions
- **Smart Integrations**: Auto-sync with GitHub issues, Jira tickets
- **Custom Workflows**: Configurable pipelines per team
- **Analytics Dashboard**: Team productivity insights
## Success Criteria
1. **User Adoption**: 1000 active users in 6 months
2. **Performance**: <200ms API response time
3. **Reliability**: 99.9% uptime
## Constraints
- Timeline: 6 months MVP
- Technology: Python backend, React frontend, PostgreSQL
- Team: 2 backend, 2 frontend, 1 ML engineer
## Out of Scope
- Mobile apps (Phase 2)
- Video conferencing
- Time tracking (separate product)
```
---
## Document 2: SPECIFICATIONS.md
### Template Structure
```markdown
# {Project Name} - Technical Specifications
## Functional Requirements
### Core Features
#### {Feature 1}
- **Description**: {What it does}
- **User Story**: As a {role}, I want {action} so that {benefit}
- **Acceptance Criteria**:
- [ ] {Criterion 1}
- [ ] {Criterion 2}
- [ ] {Criterion 3}
#### {Feature 2}
{Repeat structure}
### User Flows
#### {Flow 1}: {Name}
1. User {action}
2. System {response}
3. User {next action}
4. Result: {outcome}
---
## Non-Functional Requirements
### Performance
- API response time: <200ms (p95)
- Page load time: <1s
- Concurrent users: 10,000+
- Database queries: <50ms
### Security
- Authentication: OAuth2 + JWT
- Authorization: Role-based access control (RBAC)
- Data encryption: AES-256 at rest, TLS 1.3 in transit
- Rate limiting: 100 req/min per user
### Reliability
- Uptime: 99.9% SLA
- Backup frequency: Daily
- Recovery time: <1 hour (RTO)
- Data loss: <5 minutes (RPO)
### Scalability
- Horizontal scaling: Auto-scale based on load
- Database: Read replicas for queries
- Cache: Redis for hot data
- CDN: Static assets
---
## API Contracts
### Authentication API
#### POST /api/auth/login
```json
// Request
{
"email": "user@example.com",
"password": "hashed_password"
}
// Response (200 OK)
{
"token": "jwt_token_here",
"user": {
"id": "user_123",
"email": "user@example.com",
"name": "John Doe"
}
}
// Error (401 Unauthorized)
{
"error": "Invalid credentials"
}
```
#### POST /api/auth/logout
{Repeat structure for each endpoint}
### Tasks API
#### GET /api/tasks
```json
// Query params: ?page=1&per_page=50&status=active
// Response (200 OK)
{
"tasks": [
{
"id": "task_123",
"title": "Fix bug in auth",
"status": "active",
"priority": "high",
"assignee": "user_456",
"created_at": "2025-10-30T10:00:00Z"
}
],
"pagination": {
"page": 1,
"per_page": 50,
"total": 150
}
}
```
{Continue for all major endpoints}
---
## Data Models
### User
```python
class User:
id: str (UUID)
email: str (unique, indexed)
password_hash: str
name: str
role: Enum['admin', 'member', 'viewer']
created_at: datetime
updated_at: datetime
last_login: datetime | None
```
### Task
```python
class Task:
id: str (UUID)
title: str (max 200 chars)
description: str | None
status: Enum['backlog', 'active', 'completed']
priority: Enum['low', 'medium', 'high', 'urgent']
assignee_id: str | None (FK -> User.id)
project_id: str (FK -> Project.id)
due_date: datetime | None
created_at: datetime
updated_at: datetime
```
{Continue for all major models}
---
## System Architecture
### Components
- **API Gateway**: Kong/NGINX for routing and rate limiting
- **Backend Services**: FastAPI/Django microservices
- **Database**: PostgreSQL (primary), Redis (cache)
- **Message Queue**: RabbitMQ for async tasks
- **Storage**: S3 for file uploads
- **Monitoring**: Prometheus + Grafana
### Deployment
- **Infrastructure**: AWS/GCP Kubernetes
- **CI/CD**: GitHub Actions
- **Environments**: dev, staging, production
- **Rollback**: Blue-green deployment
---
## Dependencies
### Backend
- Python 3.11+
- FastAPI or Django REST Framework
- SQLAlchemy or Django ORM
- Celery for background tasks
- pytest for testing
### Frontend
- React 18+ or Vue 3+
- TypeScript
- Tailwind CSS or Material-UI
- Axios for API calls
- Vitest or Jest for testing
### Infrastructure
- Docker + Docker Compose
- Kubernetes (production)
- PostgreSQL 15+
- Redis 7+
- NGINX or Caddy
---
## Development Phases
### Phase 1: MVP (Months 1-3)
- [ ] User authentication
- [ ] Basic task CRUD
- [ ] Simple prioritization
- [ ] API foundation
### Phase 2: Collaboration (Months 4-5)
- [ ] Real-time updates (WebSocket)
- [ ] Comments and mentions
- [ ] Team management
### Phase 3: Integrations (Month 6)
- [ ] GitHub integration
- [ ] Jira sync
- [ ] Slack notifications
---
## Testing Strategy
### Unit Tests
- Coverage: >80%
- All business logic functions
- Mock external dependencies
### Integration Tests
- API endpoint testing
- Database transactions
- Authentication flows
### E2E Tests
- Critical user flows
- Payment processing (if applicable)
- Admin workflows
---
## Security Considerations
### OWASP Top 10 Coverage
1. **Injection**: Parameterized queries, input validation
2. **Broken Auth**: JWT with refresh tokens, secure session management
3. **Sensitive Data**: Encryption at rest and in transit
4. **XXE**: Disable XML external entities
5. **Broken Access Control**: RBAC enforcement
6. **Security Misconfiguration**: Secure defaults, regular audits
7. **XSS**: Output escaping, CSP headers
8. **Insecure Deserialization**: Validate all input
9. **Known Vulnerabilities**: Dependency scanning (Snyk, Dependabot)
10. **Insufficient Logging**: Audit logs for sensitive actions
---
## Monitoring & Observability
### Metrics
- Request rate, error rate, latency (RED method)
- Database connection pool usage
- Cache hit/miss ratio
- Background job queue length
### Logging
- Structured JSON logs
- Centralized logging (ELK stack or CloudWatch)
- Log levels: DEBUG (dev), INFO (staging), WARN/ERROR (prod)
### Alerting
- Error rate >5% (P1)
- API latency >500ms (P2)
- Database connections >80% (P2)
- Disk usage >90% (P1)
---
## Documentation Requirements
- [ ] API documentation (OpenAPI/Swagger)
- [ ] Setup guide (README.md)
- [ ] Architecture diagrams
- [ ] Deployment runbook
- [ ] Troubleshooting guide
```
---
## Generation Process
### Step 1: Extract Project Context
```python
def extract_project_info(prompt: str) -> dict:
"""Parse project description for key details."""
info = {
"name": None,
"description": prompt,
"features": [],
"tech_stack": [],
"constraints": {},
"goals": []
}
# Extract from prompt:
# - Project name (if mentioned)
# - Desired features
# - Technology preferences
# - Timeline/budget constraints
# - Success metrics
return info
```
### Step 2: Apply Output Style
Use `output-style-selector` to determine:
- **PROJECT-OVERVIEW.md**: Bullet-points, concise
- **SPECIFICATIONS.md**: Table-based for API contracts, YAML-structured for models
### Step 3: Generate Documents
1. Create `project-management/` directory if needed
2. Write PROJECT-OVERVIEW.md (vision-focused)
3. Write SPECIFICATIONS.md (technical details)
4. Validate completeness
### Step 4: Validation Checklist
```markdown
## Generated Documents Validation
PROJECT-OVERVIEW.md:
- [ ] Project name and tagline present
- [ ] Vision statement (2-3 sentences)
- [ ] 3+ goals defined
- [ ] 5-10 key features listed
- [ ] Success criteria measurable
- [ ] Constraints documented
- [ ] Out-of-scope items listed
SPECIFICATIONS.md:
- [ ] Functional requirements detailed
- [ ] Non-functional requirements (perf, security, reliability)
- [ ] API contracts with examples (if applicable)
- [ ] Data models defined
- [ ] Architecture overview
- [ ] Dependencies listed
- [ ] Development phases outlined
- [ ] Testing strategy included
```
---
## Integration with Commands
### With `/lazy plan`
```bash
# Generate project docs first
/lazy plan --project "Build AI-powered task manager"
→ project-planner skill triggers
→ Generates PROJECT-OVERVIEW.md + SPECIFICATIONS.md
→ Then creates first user story from specifications
# Or start from enhanced prompt
/lazy plan --file enhanced_prompt.md
→ Detects project-level scope
→ Runs project-planner
→ Creates foundational docs
→ Proceeds with story creation
```
### With `/lazy code`
```bash
# Reference specifications during implementation
/lazy code @US-3.4.md
→ context-packer loads SPECIFICATIONS.md
→ API contracts and data models available
→ Implementation follows spec
```
---
## What This Skill Does NOT Do
❌ Generate actual code (that's for `coder` agent)
❌ Create user stories (that's for `project-manager` agent)
❌ Make architectural decisions (provides template, you decide)
❌ Replace technical design documents (TDDs)
✅ **DOES**: Create structured foundation documents for new projects.
---
## Configuration
```bash
# Minimal specs (faster, less detail)
export LAZYDEV_PROJECT_SPEC_MINIMAL=1
# Skip API contracts (non-API projects)
export LAZYDEV_PROJECT_NO_API=1
# Focus on specific aspects
export LAZYDEV_PROJECT_FOCUS="security,performance"
```
---
## Tips for Effective Project Planning
### For PROJECT-OVERVIEW.md
1. **Vision**: Think big picture - why does this exist?
2. **Goals**: Limit to 3-5 measurable outcomes
3. **Features**: High-level only (not task-level details)
4. **Success Criteria**: Must be measurable (numbers, percentages)
### For SPECIFICATIONS.md
1. **API Contracts**: Start with authentication and core resources
2. **Data Models**: Include relationships and constraints
3. **Non-Functional**: Don't skip - these prevent tech debt
4. **Security**: Reference OWASP Top 10 coverage
5. **Phases**: Break into 2-3 month chunks maximum
### Best Practices
- **Keep PROJECT-OVERVIEW under 2 pages**: Executive summary only
- **SPECIFICATIONS can be longer**: This is the source of truth
- **Update specs as you learn**: Living documents
- **Version control both**: Track changes over time
---
## Example Trigger Scenarios
### Scenario 1: New Greenfield Project
```
User: "I want to build a real-time chat platform with video calls"
→ project-planner triggers
→ Generates:
- PROJECT-OVERVIEW.md (vision: modern communication platform)
- SPECIFICATIONS.md (WebSocket APIs, video streaming, etc.)
→ Ready for user story creation
```
### Scenario 2: From Enhanced Prompt
```
User: /lazy plan --file enhanced_prompt.md
# enhanced_prompt contains: detailed project requirements, tech stack, timeline
→ project-planner parses prompt
→ Extracts structured information
→ Generates both documents
→ Proceeds to first user story
```
### Scenario 3: Partial Information
```
User: "Build a task manager, not sure about details yet"
→ project-planner generates template
→ Marks sections as [TODO: Specify...]
→ User fills in gaps incrementally
→ Re-generate or update manually
```
---
## Output Format (Completion)
```markdown
## Project Planning Complete
**Documents Generated**:
1. **PROJECT-OVERVIEW.md** (2.4KB)
- Project: TaskFlow Pro
- Vision: Modern task management with AI
- Features: 5 key features defined
- Success criteria: 3 measurable metrics
2. **SPECIFICATIONS.md** (8.1KB)
- Functional requirements: 5 core features detailed
- API contracts: 12 endpoints documented
- Data models: 6 models defined
- Architecture: Microservices with Kubernetes
- Development phases: 3 phases over 6 months
**Location**: `./project-management/`
**Next Steps**:
1. Review and refine generated documents
2. Run: `/lazy plan "First user story description"`
3. Begin implementation with `/lazy code`
**Estimated Setup Time**: 15-20 minutes to review/customize
```
---
**Version**: 1.0.0
**Output Size**: 10-15KB total (both documents)
**Generation Time**: ~30 seconds

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---
name: regression-testing
description: Evaluates and implements regression tests after bug fixes based on severity, code complexity, and coverage. Use when bugs are fixed to prevent future regressions.
---
# Regression Testing Skill
**Purpose**: Automatically evaluate and implement regression tests after bug fixes to prevent future regressions.
**When to Trigger**: This skill activates after bug fixes are implemented, allowing Claude (the orchestrator) to decide if regression tests would be valuable based on context.
---
## Decision Criteria (Orchestrator Evaluation)
Before implementing regression tests, evaluate these factors:
### High Value Scenarios (Implement Regression Tests)
- **Critical Bugs**: Security, data loss, or production-impacting issues
- **Subtle Bugs**: Edge cases, race conditions, timing issues that are easy to miss
- **Complex Logic**: Multi-step workflows, state machines, intricate business rules
- **Low Coverage Areas**: Bug occurred in under-tested code (<70% coverage)
- **Recurring Patterns**: Similar bugs fixed before in related code
- **Integration Points**: Bugs at module/service boundaries
### Lower Value Scenarios (Skip or Defer)
- **Trivial Fixes**: Typos, obvious logic errors with existing tests
- **Already Well-Tested**: Bug area has >90% coverage with comprehensive tests
- **One-Time Anomalies**: Environmental issues, config errors (not code bugs)
- **Rapid Prototyping**: Early-stage features expected to change significantly
- **UI-Only Changes**: Purely cosmetic fixes with no logic impact
---
## Regression Test Strategy
### 1. Bug Analysis Phase
**Understand the Bug:**
```markdown
## Bug Context
- **What broke**: [Symptom/error]
- **Root cause**: [Why it happened]
- **Fix applied**: [What changed]
- **Failure scenario**: [Steps to reproduce original bug]
```
**Evaluate Test Value:**
```python
def should_add_regression_test(bug_context: dict) -> tuple[bool, str]:
"""
Decide if regression test is valuable.
Returns:
(add_test: bool, reason: str)
"""
severity = bug_context.get("severity") # critical, high, medium, low
complexity = bug_context.get("complexity") # high, medium, low
coverage = bug_context.get("coverage_pct", 0)
# Critical bugs always get regression tests
if severity == "critical":
return True, "Critical bug requires regression test"
# Complex bugs with low coverage
if complexity == "high" and coverage < 70:
return True, "Complex logic with insufficient coverage"
# Already well-tested
if coverage > 90:
return False, "Area already has comprehensive tests"
# Default: add test for medium+ severity
if severity in {"high", "medium"}:
return True, f"Bug severity {severity} warrants regression test"
return False, "Low-value regression test, skipping"
```
### 2. Regression Test Implementation
**Test Structure:**
```python
# test_<module>_regression.py
import pytest
from datetime import datetime
class TestRegressions:
"""Regression tests for fixed bugs."""
def test_regression_issue_123_null_pointer_in_payment(self):
"""
Regression test for GitHub issue #123.
Bug: NullPointerException when processing payment with missing user email.
Fixed: 2025-10-30
Root cause: Missing null check in payment processor
This test ensures the fix remains in place and prevents regression.
"""
# Arrange: Setup scenario that caused original bug
payment = Payment(amount=100.0, user=User(email=None))
processor = PaymentProcessor()
# Act: Execute the previously failing code path
result = processor.process(payment)
# Assert: Verify fix works (no exception, proper error handling)
assert result.status == "failed"
assert "invalid user email" in result.error_message.lower()
def test_regression_pr_456_race_condition_in_cache(self):
"""
Regression test for PR #456.
Bug: Race condition in cache invalidation caused stale reads
Fixed: 2025-10-30
Root cause: Non-atomic read-modify-write operation
This test simulates concurrent cache access to verify thread safety.
"""
# Arrange: Setup concurrent scenario
cache = ThreadSafeCache()
cache.set("key", "value1")
# Act: Simulate race condition with threads
with ThreadPoolExecutor(max_workers=10) as executor:
futures = [
executor.submit(cache.update, "key", f"value{i}")
for i in range(100)
]
wait(futures)
# Assert: Verify no stale reads or corruption
final_value = cache.get("key")
assert final_value.startswith("value")
assert cache.consistency_check() # Internal consistency
```
**Test Naming Convention:**
- `test_regression_<issue_id>_<short_description>`
- Include issue/PR number for traceability
- Short description of what broke
**Test Documentation:**
- **Bug description**: What failed
- **Date fixed**: When fix was applied
- **Root cause**: Why it happened
- **Test purpose**: What regression is prevented
### 3. Regression Test Coverage
**What to Test:**
1. **Exact Failure Scenario**: Reproduce original bug conditions
2. **Edge Cases Around Fix**: Test boundaries near the bug
3. **Integration Impact**: Test how fix affects dependent code
4. **Performance**: If bug was performance-related, add benchmark
**What NOT to Test:**
- Don't duplicate existing unit tests
- Don't test obvious behavior already covered
- Don't over-specify implementation details (brittle tests)
---
## Workflow Integration
### Standard Bug Fix Flow
```bash
# 1. Fix the bug
/lazy code "fix: null pointer in payment processor"
# ✓ Bug fixed and committed
# 2. Regression testing skill evaluates
# (Automatic trigger after bug fix commit)
## Decision: Add regression test?
- Severity: HIGH (production crash)
- Coverage: 65% (medium)
- Complexity: MEDIUM
→ **YES, add regression test**
# 3. Implement regression test
# ✓ test_regression_issue_123_null_pointer_in_payment() added
# ✓ Coverage increased to 78%
# ✓ Test passes (bug is fixed)
# 4. Commit regression test
git add tests/test_payment_regression.py
git commit -m "test: add regression test for issue #123 null pointer"
```
### Quick Bug Fix (Skip Regression)
```bash
# 1. Fix trivial bug
/lazy code "fix: typo in error message"
# ✓ Bug fixed
# 2. Regression testing skill evaluates
## Decision: Add regression test?
- Severity: LOW (cosmetic)
- Coverage: 95% (excellent)
- Complexity: LOW (trivial)
→ **NO, skip regression test** (low value, already well-tested)
# 3. Commit fix only
# No additional test needed
```
---
## Regression Test Suite Management
### Organization
```
tests/
├── test_module.py # Regular unit tests
├── test_module_integration.py # Integration tests
└── test_module_regression.py # Regression tests (this skill)
```
**Separate regression tests** to:
- Track historical bug fixes
- Easy to identify which tests prevent regressions
- Can be run as separate CI job for faster feedback
### CI/CD Integration
```yaml
# .github/workflows/ci.yml
jobs:
regression-tests:
runs-on: ubuntu-latest
steps:
- name: Run regression test suite
run: pytest tests/*_regression.py -v --tb=short
# Fast feedback: regression tests run first
# If they fail, likely a regression occurred
```
### Regression Test Metrics
**Track Over Time:**
- Total regression tests count
- Bug recurrence rate (0% is goal)
- Coverage increase from regression tests
- Time to detect regression (should be in CI, not production)
---
## Examples
### Example 1: Critical Bug (Add Regression Test)
**Bug**: Authentication bypass when session token is malformed
**Fix**: Added token validation
**Decision**: ✅ **Add regression test** (security critical)
```python
def test_regression_issue_789_auth_bypass_malformed_token():
"""
Regression test for security issue #789.
Bug: Malformed session tokens bypassed authentication
Fixed: 2025-10-30
Severity: CRITICAL (security)
Root cause: Missing token format validation
"""
# Arrange: Malformed token that bypassed auth
malformed_token = "invalid||format||token"
# Act: Attempt authentication
result = AuthService.validate_token(malformed_token)
# Assert: Should reject malformed token
assert result.is_valid is False
assert result.error == "invalid_token_format"
```
### Example 2: Complex Bug (Add Regression Test)
**Bug**: Race condition in distributed lock causes duplicate job execution
**Fix**: Atomic compare-and-swap operation
**Decision**: ✅ **Add regression test** (complex concurrency issue)
```python
def test_regression_pr_234_race_condition_duplicate_jobs():
"""
Regression test for PR #234.
Bug: Race condition allowed duplicate job execution
Fixed: 2025-10-30
Complexity: HIGH (concurrency)
Root cause: Non-atomic lock acquisition
"""
# Arrange: Simulate concurrent job submissions
job_queue = DistributedJobQueue()
job_id = "test-job-123"
# Act: 100 threads try to acquire same job
with ThreadPoolExecutor(max_workers=100) as executor:
futures = [
executor.submit(job_queue.try_acquire_job, job_id)
for _ in range(100)
]
results = [f.result() for f in futures]
# Assert: Only ONE thread should acquire the job
acquired = [r for r in results if r.acquired]
assert len(acquired) == 1, "Race condition: multiple threads acquired same job"
```
### Example 3: Trivial Bug (Skip Regression Test)
**Bug**: Typo in log message "Usre authenticated" → "User authenticated"
**Fix**: Corrected spelling
**Decision**: ❌ **Skip regression test** (cosmetic, no logic impact)
```
No test needed. Fix is obvious and has no functional impact.
Existing tests already cover authentication logic.
```
### Example 4: Well-Tested Area (Skip Regression Test)
**Bug**: Off-by-one error in pagination (page 1 showed 0 results)
**Fix**: Changed `offset = page * size` to `offset = (page - 1) * size`
**Coverage**: 95% (pagination thoroughly tested)
**Decision**: ❌ **Skip regression test** (area already has comprehensive tests)
```python
# Existing test already covers this:
def test_pagination_first_page_shows_results():
results = api.get_users(page=1, size=10)
assert len(results) == 10 # This test would have caught the bug
```
---
## Best Practices
### DO:
✅ Add regression tests for **critical and complex bugs**
✅ Include **issue/PR number** in test name for traceability
✅ Document **what broke, why, and when** in test docstring
✅ Test the **exact failure scenario** that caused the bug
✅ Keep regression tests **separate** from unit tests (easier tracking)
✅ Run regression tests in **CI/CD** for early detection
### DON'T:
❌ Add regression tests for **trivial or cosmetic bugs**
❌ Duplicate **existing comprehensive tests**
❌ Write **brittle tests** that test implementation details
❌ Skip **root cause analysis** (understand why it broke)
❌ Forget to **verify test fails** before fix (should reproduce bug)
---
## Output Format
When this skill triggers, provide:
```markdown
## Regression Test Evaluation
**Bug Fixed**: [Brief description]
**Issue/PR**: #[number]
**Severity**: [critical/high/medium/low]
**Complexity**: [high/medium/low]
**Current Coverage**: [X%]
**Decision**: [✅ Add Regression Test | ❌ Skip Regression Test]
**Reason**: [Why regression test is/isn't valuable]
---
[If adding test]
## Regression Test Implementation
**File**: `tests/test_<module>_regression.py`
```python
def test_regression_<issue>_<description>():
"""
[Docstring with bug context]
"""
# Test implementation
```
**Coverage Impact**: +X% (before: Y%, after: Z%)
```
---
## Integration with Other Skills
- **Works with**: `test-driven-development` (adds tests post-fix)
- **Complements**: `code-review-request` (reviewer checks for regression tests)
- **Used by**: `/lazy fix` command (auto-evaluates regression test need)
---
## Configuration
**Environment Variables:**
```bash
# Force regression tests for all bugs (strict mode)
export LAZYDEV_FORCE_REGRESSION_TESTS=1
# Disable regression test skill
export LAZYDEV_DISABLE_REGRESSION_SKILL=1
# Minimum coverage threshold to skip regression test (default: 90)
export LAZYDEV_REGRESSION_SKIP_COVERAGE_THRESHOLD=90
```
---
**Version**: 1.0.0
**Created**: 2025-10-30
**Anthropic Best Practice**: Model-invoked, autonomous trigger after bug fixes

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---
name: security-audit
description: Triggers for authentication, payments, user input, and API endpoints to check OWASP risks. Auto-evaluates security need and provides actionable fixes, not checklists.
---
# Security Audit Skill
**Purpose**: Catch security vulnerabilities early with targeted checks, not generic checklists.
**Trigger Words**: auth, login, password, payment, credit card, token, API endpoint, user input, SQL, database query, session, cookie, upload
---
## Quick Decision: When to Audit?
```python
def needs_security_audit(code_context: dict) -> bool:
"""Fast security risk evaluation."""
# ALWAYS audit these (high risk)
critical_patterns = [
"authentication", "authorization", "login", "password",
"payment", "credit card", "billing", "stripe", "paypal",
"admin", "sudo", "privilege", "role",
"token", "jwt", "session", "cookie",
"sql", "database", "query", "exec", "eval",
"upload", "file", "download", "path traversal"
]
# Check if any critical pattern in code
if any(p in code_context.get("description", "").lower() for p in critical_patterns):
return True
# Skip for: docs, tests, config, low-risk utils
skip_patterns = ["test_", "docs/", "README", "config", "utils"]
if any(p in code_context.get("files", []) for p in skip_patterns):
return False
return False
```
---
## Security Checks (Targeted, Not Exhaustive)
### 1. **Input Validation** (Most Common)
```python
# ❌ BAD - No validation
def get_user(user_id):
return db.query(f"SELECT * FROM users WHERE id = {user_id}")
# ✅ GOOD - Validated + parameterized
def get_user(user_id: int):
if not isinstance(user_id, int) or user_id <= 0:
raise ValueError("Invalid user_id")
return db.query("SELECT * FROM users WHERE id = ?", [user_id])
```
**Quick Fix**: Add type hints + validation at entry points.
---
### 2. **SQL Injection** (Critical)
```python
# ❌ BAD - String interpolation
query = f"SELECT * FROM users WHERE email = '{email}'"
# ✅ GOOD - Parameterized queries
query = "SELECT * FROM users WHERE email = ?"
db.execute(query, [email])
```
**Quick Fix**: Never use f-strings for SQL. Use ORM or parameterized queries.
---
### 3. **Authentication & Secrets** (Critical)
```python
# ❌ BAD - Hardcoded secrets
API_KEY = "sk_live_abc123"
password = "admin123"
# ✅ GOOD - Environment variables
API_KEY = os.getenv("STRIPE_API_KEY")
# Passwords: bcrypt hashed, never plaintext
# ❌ BAD - Weak session
session["user_id"] = user_id # No expiry, no signing
# ✅ GOOD - Secure session
session.permanent = False
session["user_id"] = user_id
session["expires"] = time.time() + 3600 # 1 hour
```
**Quick Fix**: Extract secrets to .env, hash passwords, add session expiry.
---
### 4. **Authorization** (Often Forgotten)
```python
# ❌ BAD - Missing authorization check
@app.route("/admin/users/<user_id>", methods=["DELETE"])
def delete_user(user_id):
User.delete(user_id) # Anyone can delete!
# ✅ GOOD - Check permissions
@app.route("/admin/users/<user_id>", methods=["DELETE"])
@require_role("admin")
def delete_user(user_id):
if not current_user.can_delete(user_id):
abort(403)
User.delete(user_id)
```
**Quick Fix**: Add permission checks before destructive operations.
---
### 5. **Rate Limiting** (API Endpoints)
```python
# ❌ BAD - No rate limit
@app.route("/api/login", methods=["POST"])
def login():
# Brute force possible
return authenticate(request.json)
# ✅ GOOD - Rate limited
@app.route("/api/login", methods=["POST"])
@rate_limit("5 per minute")
def login():
return authenticate(request.json)
```
**Quick Fix**: Add rate limiting to login, payment, sensitive endpoints.
---
### 6. **XSS Prevention** (Frontend/Templates)
```python
# ❌ BAD - Unescaped user input
return f"<div>Welcome {username}</div>" # XSS if username = "<script>alert('XSS')</script>"
# ✅ GOOD - Escaped output
from html import escape
return f"<div>Welcome {escape(username)}</div>"
# Or use framework escaping (Jinja2, React auto-escapes)
```
**Quick Fix**: Escape user input in HTML. Use framework defaults.
---
### 7. **File Upload Safety**
```python
# ❌ BAD - No validation
@app.route("/upload", methods=["POST"])
def upload():
file = request.files["file"]
file.save(f"uploads/{file.filename}") # Path traversal! Overwrite!
# ✅ GOOD - Validated
import os
from werkzeug.utils import secure_filename
ALLOWED_EXTENSIONS = {"png", "jpg", "pdf"}
@app.route("/upload", methods=["POST"])
def upload():
file = request.files["file"]
if not file or "." not in file.filename:
abort(400, "Invalid file")
ext = file.filename.rsplit(".", 1)[1].lower()
if ext not in ALLOWED_EXTENSIONS:
abort(400, "File type not allowed")
filename = secure_filename(file.filename)
file.save(os.path.join("uploads", filename))
```
**Quick Fix**: Whitelist extensions, sanitize filenames, limit size.
---
## Output Format (Actionable Only)
```markdown
## Security Audit Results
**Risk Level**: [CRITICAL | HIGH | MEDIUM | LOW]
### Issues Found: X
1. **[CRITICAL] SQL Injection in get_user() (auth.py:45)**
- Issue: f-string used for SQL query
- Fix: Use parameterized query
- Code:
```python
# Change this:
query = f"SELECT * FROM users WHERE id = {user_id}"
# To this:
query = "SELECT * FROM users WHERE id = ?"
db.execute(query, [user_id])
```
2. **[HIGH] Missing rate limiting on /api/login**
- Issue: Brute force attacks possible
- Fix: Add @rate_limit("5 per minute") decorator
3. **[MEDIUM] Hardcoded API key in config.py:12**
- Issue: Secret in code
- Fix: Move to environment variable
---
**Next Steps**:
1. Fix CRITICAL issues first (SQL injection)
2. Add rate limiting (5 min fix)
3. Extract secrets to .env
4. Re-run security audit after fixes
```
---
## Integration with Workflow
```bash
# Automatic trigger
/lazy code "add user login endpoint"
→ security-audit triggers
→ Checks: password handling, session, rate limiting
→ Finds: Missing bcrypt hash, no rate limit
→ Suggests fixes with code examples
→ Developer applies fixes
→ Re-audit confirms: ✅ Secure
# Manual trigger
Skill(command="security-audit")
```
---
## What This Skill Does NOT Do
❌ Generate 50-item security checklists (not actionable)
❌ Flag every minor issue (noise)
❌ Require penetration testing (that's a different tool)
❌ Cover infrastructure security (AWS, Docker, etc.)
✅ **DOES**: Catch common code-level vulnerabilities with fast, practical fixes.
---
## Configuration
```bash
# Strict mode: audit everything (slower)
export LAZYDEV_SECURITY_STRICT=1
# Disable security skill
export LAZYDEV_DISABLE_SECURITY=1
# Focus on specific risks only
export LAZYDEV_SECURITY_FOCUS="sql,auth,xss"
```
---
**Version**: 1.0.0
**OWASP Coverage**: SQL Injection, XSS, Broken Auth, Insecure Design, Security Misconfiguration
**Speed**: <5 seconds for typical file

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---
name: story-traceability
description: Ensure Acceptance Criteria map to Tasks and Tests for PR-per-story workflow
version: 0.1.0
tags: [planning, QA]
triggers:
- acceptance criteria
- user story
- traceability
---
# Story Traceability
## Purpose
Create a clear AC → Task → Test mapping to guarantee coverage and reviewability.
## Behavior
1. Build a table: AC | Task(s) | Test(s) | Notes.
2. Insert into `USER-STORY.md`; add brief references into each `TASK-*.md`.
3. Call out missing mappings; propose test names.
## Guardrails
- Every AC must have ≥1 task and ≥1 test.
- Keep table compact; link file paths precisely.
## Integration
- Project Manager agent; `/lazy create-feature` output phase.
## Example Prompt
> Add traceability for US-20251027-001.

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---
name: task-slicer
description: Split features into atomic 2–4h tasks with independent tests and minimal dependencies
version: 0.1.0
tags: [planning, tasks]
triggers:
- break into tasks
- task list
- estimates
---
# Task Slicer
## Purpose
Turn a user story into small, testable tasks with clear inputs/outputs.
## Behavior
1. Create 3–10 tasks, each 2–4 hours.
2. For each task: description, files, test focus, dependencies, estimate.
3. Name files `TASK-US-<id>-<n>.md` and reference the story ID.
## Guardrails
- Prefer independence; minimize cross-task dependencies.
- Split or merge tasks to hit target size.
## Integration
- Project Manager agent; `/lazy create-feature` task generation step.
## Example Prompt
> Slice US-20251027-001 into executable tasks.

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---
name: tech-stack-architect
description: Design complete technology stack and system architecture from project requirements - generates TECH-STACK.md with frontend/backend/database/DevOps choices plus rationale, and ARCHITECTURE.md with components, data flow, and mermaid diagrams
version: 0.1.0
tags: [architecture, planning, tech-stack, design]
triggers:
- tech stack
- architecture
- technology choices
- system design
- architecture diagram
---
# Tech Stack Architect
## Purpose
Generate comprehensive technology stack selection and system architecture design from project requirements. Creates two foundational documents that guide implementation.
## When to Use
- Starting a new project after PROJECT-OVERVIEW.md is created
- Re-architecting existing systems
- Technology evaluation and selection
- Architecture documentation needed
- User mentions "tech stack", "architecture", "system design"
## Behavior
### Phase 1: Technology Stack Selection
1. **Read PROJECT-OVERVIEW.md** for:
- Project goals and constraints
- Scale requirements (users, data, traffic)
- Team skills and preferences
- Budget and timeline
- Compliance requirements
2. **Analyze requirements** across 4 categories:
- Frontend (framework, state management, UI library)
- Backend (language, framework, API style)
- Database (RDBMS, NoSQL, caching, search)
- DevOps (hosting, CI/CD, monitoring, security)
3. **Generate TECH-STACK.md** with:
- **Category tables**: Technology | Rationale | Alternatives Considered
- **Integration notes**: How technologies work together
- **Trade-offs**: What you gain/lose with this stack
- **Migration path**: How to evolve the stack
- **Team considerations**: Learning curve, hiring, support
### Phase 2: System Architecture Design
1. **Design components**:
- Client-side architecture
- API layer and services
- Data storage and caching
- Background jobs and queues
- External integrations
2. **Define data flow**:
- Request/response paths
- Authentication flow
- Data persistence patterns
- Event-driven flows (if applicable)
3. **Generate ARCHITECTURE.md** with:
- **System Overview**: High-level component diagram (C4 Context)
- **Component Details**: Responsibilities, interfaces, dependencies
- **Data Flow Diagrams**: Key user journeys with sequence diagrams
- **Scalability Strategy**: Horizontal scaling, caching, load balancing
- **Security Architecture**: Auth, encryption, OWASP considerations
- **Mermaid Diagrams**: C4, sequence, data flow, deployment
## Output Style
- Use `table-based` for technology comparisons
- Use `markdown-focused` with mermaid diagrams for architecture
- Keep rationales concise (1-2 sentences per choice)
- Include visual diagrams for clarity
## Output Files
### 1. project-management/TECH-STACK.md
```markdown
# Technology Stack
## Summary
[2-3 sentence overview of the stack philosophy]
## Frontend Stack
| Technology | Choice | Rationale | Alternatives Considered |
|------------|--------|-----------|------------------------|
| Framework | React 18 | ... | Vue, Svelte, Angular |
| State | Zustand | ... | Redux, Jotai, Context |
| UI Library | Tailwind + shadcn/ui | ... | MUI, Chakra, custom |
| Build | Vite | ... | Webpack, Turbopack |
## Backend Stack
| Technology | Choice | Rationale | Alternatives Considered |
|------------|--------|-----------|------------------------|
| Language | Python 3.11 | ... | Node.js, Go, Rust |
| Framework | FastAPI | ... | Django, Flask, Express |
| API Style | REST + OpenAPI | ... | GraphQL, gRPC, tRPC |
## Database & Storage
| Technology | Choice | Rationale | Alternatives Considered |
|------------|--------|-----------|------------------------|
| Primary DB | PostgreSQL 15 | ... | MySQL, MongoDB, SQLite |
| Caching | Redis | ... | Memcached, Valkey |
| Search | ElasticSearch | ... | Algolia, Meilisearch |
| Object Storage | S3 | ... | MinIO, CloudFlare R2 |
## DevOps & Infrastructure
| Technology | Choice | Rationale | Alternatives Considered |
|------------|--------|-----------|------------------------|
| Hosting | AWS ECS Fargate | ... | k8s, VM, serverless |
| CI/CD | GitHub Actions | ... | GitLab CI, CircleCI |
| Monitoring | DataDog | ... | Grafana, New Relic |
| Secrets | AWS Secrets Manager | ... | Vault, Doppler |
## Integration Notes
- [How frontend talks to backend]
- [Database connection pooling strategy]
- [Caching layer integration]
- [CI/CD pipeline flow]
## Trade-offs
**Gains**: [What this stack provides]
**Costs**: [Complexity, vendor lock-in, learning curve]
## Migration Path
- Phase 1: [Initial minimal stack]
- Phase 2: [Add caching, search]
- Phase 3: [Scale horizontally]
## Team Considerations
- **Learning Curve**: [Estimate for team]
- **Hiring**: [Availability of talent]
- **Support**: [Community, docs, enterprise support]
```
### 2. project-management/ARCHITECTURE.md
```markdown
# System Architecture
## Overview
[2-3 sentence description of the system]
## C4 Context Diagram
```mermaid
C4Context
title System Context for [Project Name]
Person(user, "User", "End user of the system")
System(app, "Application", "Main system")
System_Ext(auth, "Auth Provider", "OAuth2 provider")
System_Ext(payment, "Payment Gateway", "Stripe")
Rel(user, app, "Uses", "HTTPS")
Rel(app, auth, "Authenticates", "OAuth2")
Rel(app, payment, "Processes payments", "API")
```
## Component Architecture
```mermaid
graph TB
Client[React Client]
API[FastAPI Backend]
DB[(PostgreSQL)]
Cache[(Redis)]
Queue[Job Queue]
Worker[Background Workers]
Client -->|HTTPS/JSON| API
API -->|SQL| DB
API -->|GET/SET| Cache
API -->|Enqueue| Queue
Queue -->|Process| Worker
Worker -->|Update| DB
```
### Component Details
**Client (React)**
- **Responsibilities**: UI rendering, state management, client-side validation
- **Key Libraries**: React Router, Zustand, React Query
- **Interfaces**: REST API via fetch/axios
**API (FastAPI)**
- **Responsibilities**: Business logic, validation, auth, rate limiting
- **Key Modules**: auth, users, payments, notifications
- **Interfaces**: REST endpoints (OpenAPI), WebSocket (notifications)
**Database (PostgreSQL)**
- **Responsibilities**: Persistent data storage, relational integrity
- **Schema**: Users, sessions, transactions, audit logs
- **Patterns**: Repository pattern, connection pooling
**Cache (Redis)**
- **Responsibilities**: Session storage, rate limiting, job queue
- **TTL Strategy**: Sessions (24h), API cache (5m), rate limits (1h)
**Background Workers**
- **Responsibilities**: Email sending, report generation, cleanup jobs
- **Queue**: Redis-backed Celery/ARQ
- **Monitoring**: Dead letter queue, retry logic
## Authentication Flow
```mermaid
sequenceDiagram
participant User
participant Client
participant API
participant Auth0
participant DB
User->>Client: Click "Login"
Client->>Auth0: Redirect to OAuth2
Auth0->>Client: Return auth code
Client->>API: Exchange code for token
API->>Auth0: Validate code
Auth0->>API: User profile
API->>DB: Create/update user
API->>Client: Return JWT token
Client->>Client: Store token (httpOnly cookie)
```
## Data Flow: User Registration
```mermaid
sequenceDiagram
participant Client
participant API
participant DB
participant Queue
participant Worker
participant Email
Client->>API: POST /api/register
API->>API: Validate input
API->>DB: Create user (inactive)
API->>Queue: Enqueue welcome email
API->>Client: 201 Created
Queue->>Worker: Process email job
Worker->>Email: Send welcome email
Worker->>DB: Log email sent
```
## Scalability Strategy
### Horizontal Scaling
- **API**: Stateless containers (2-10 instances behind ALB)
- **Database**: Read replicas for reporting queries
- **Cache**: Redis Cluster (3+ nodes)
- **Workers**: Auto-scale based on queue depth
### Caching Strategy
- **API Responses**: Cache GET endpoints (5m TTL)
- **Database Queries**: Query result cache in Redis
- **Static Assets**: CDN (CloudFront) with edge caching
### Load Balancing
- **Application**: AWS ALB with health checks
- **Database**: pgpool for read/write splitting
- **Geographic**: Multi-region deployment (future)
## Security Architecture
### Authentication & Authorization
- **Strategy**: OAuth2 + JWT tokens (15m access, 7d refresh)
- **Storage**: httpOnly cookies for web, secure storage for mobile
- **Rotation**: Automatic token refresh
### Data Protection
- **At Rest**: PostgreSQL encryption (AWS RDS)
- **In Transit**: TLS 1.3 for all connections
- **Secrets**: AWS Secrets Manager, rotated monthly
### OWASP Top 10 Mitigations
- **Injection**: Parameterized queries (SQLAlchemy ORM)
- **Auth**: JWT validation, session management
- **XSS**: Content Security Policy, input sanitization
- **CSRF**: SameSite cookies, CSRF tokens
- **Rate Limiting**: Redis-backed (100 req/min per IP)
### Network Security
- **VPC**: Private subnets for DB/workers
- **Security Groups**: Least privilege access
- **WAF**: CloudFront WAF rules
## Deployment Architecture
```mermaid
graph TB
subgraph "Public Subnet"
ALB[Application Load Balancer]
end
subgraph "Private Subnet - App Tier"
API1[API Container 1]
API2[API Container 2]
Worker1[Worker Container]
end
subgraph "Private Subnet - Data Tier"
DB[(RDS PostgreSQL)]
Cache[(ElastiCache Redis)]
end
Internet((Internet)) --> ALB
ALB --> API1
ALB --> API2
API1 --> DB
API2 --> DB
API1 --> Cache
API2 --> Cache
Worker1 --> DB
Worker1 --> Cache
```
## Monitoring & Observability
**Metrics**:
- API latency (p50, p95, p99)
- Error rates by endpoint
- Database connection pool usage
- Cache hit/miss ratios
**Logging**:
- Structured JSON logs (ECS logs to CloudWatch)
- Request ID tracing across services
- Error tracking (Sentry)
**Alerting**:
- API error rate >1%
- Database connections >80%
- Job queue depth >1000
## Future Considerations
**Phase 2 Enhancements**:
- GraphQL API option
- WebSocket real-time updates
- ElasticSearch for full-text search
**Phase 3 Scale**:
- Multi-region deployment
- Event-driven microservices
- CQRS for read-heavy workloads
```
## Guardrails
- Keep technology choices pragmatic (avoid hype-driven development)
- Consider team skills when selecting stack
- Prefer managed services over self-hosted for DevOps
- Include alternatives to show deliberate choice
- Use mermaid for all diagrams (portable, version-controllable)
- Keep each document under 400 lines
- Link to official docs for each technology
## Integration
- Run after PROJECT-OVERVIEW.md is created
- Feed into `/lazy plan` for user story creation
- Reference during `/lazy code` for implementation consistency
- Update during `/lazy review` if architecture evolves
## Example Prompt
> Design the tech stack and architecture for this project
## Validation Checklist
- [ ] TECH-STACK.md has all 4 categories (Frontend, Backend, Database, DevOps)
- [ ] Each technology has rationale and alternatives
- [ ] ARCHITECTURE.md has system overview + 3+ mermaid diagrams
- [ ] Authentication and data flow are documented
- [ ] Scalability and security sections are complete
- [ ] Trade-offs and migration path are clear

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---
name: test-driven-development
description: Enforce RED→GREEN→REFACTOR micro-cycles and keep diffs minimal
version: 0.1.0
tags: [testing, quality]
triggers:
- tdd
- tests first
- failing test
---
# Test-Driven Development
## Purpose
Bias implementation to tests-first and small, verifiable changes.
## Behavior
1. RED: scaffold 1–3 failing tests targeting the smallest slice.
2. GREEN: implement the minimum code to pass.
3. REFACTOR: improve names/structure with tests green.
4. Repeat in tiny increments until task acceptance criteria are met.
## Guardrails
- Block large edits unless a failing test exists.
- Prefer small diffs spanning ≤3 files.
- Keep test names explicit and deterministic.
## Output Style
- `bullet-points` for steps; `markdown-focused` for code blocks.
## Integration
- `/lazy task-exec` implementation phase; Coder/Tester agents.
## Example Prompt
> Apply TDD to implement input validation for prices.

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# lazy
Pragmatic, flexible AI framework for Claude Code with optional TDD, smart reviews, and inline task management

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