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