5.7 KiB
5.7 KiB
name: analyze:project
description: Autonomously analyze the project with automatic skill selection and pattern learning
delegates-to: autonomous-agent:orchestrator
# Auto-Analyze Command
Analyze the current project autonomously using the orchestrator agent. This will:
- Auto-detect project type and technologies
- Load relevant skills based on project context
- Run code analysis in background
- Generate comprehensive quality report
- Store learned patterns for future use
The orchestrator will make all decisions autonomously without requiring confirmation at each step.
## How It Works
1. Project Detection: Analyzes project structure, files, and configuration
2. Context Analysis: Determines project type, languages, and frameworks
3. Skill Loading: Auto-selects relevant skills based on context
4. Analysis Execution: Runs comprehensive code analysis
5. Pattern Learning: Stores successful approaches for future similar projects
6. Report Generation: Creates detailed analysis report
IMPORTANT: When delegating this command to the orchestrator agent, the agent MUST:
1. Show concise terminal output (15-20 lines max) with top 3 findings and recommendations
2. Save detailed report to
.claude/data/reports/auto-analyze-YYYY-MM-DD.md with ALL findings
3. Include file path in terminal output
4. Never complete silently, never show 50+ lines in terminal
## Usage
bash /analyze:project
## Example Output
The orchestrator MUST use two-tier presentation:
### Terminal Output (Concise)
[PASS] Auto-Analyze Complete - Quality: 88/100 Key Findings: * Python/FastAPI project, 127 files analyzed * 4 failing tests in auth module * 12 functions missing docstrings Top Recommendations: 1. [HIGH] Fix failing auth tests -> +4 quality points 2. [MED] Add docstrings to public APIs 3. [MED] Refactor high-complexity functions 📄 Full report: .claude/data/reports/analyze-project-2025-10-21.md ⏱ Completed in 2.3 minutes
### File Report (Detailed)
Saved to .claude/data/reports/analyze-project-2025-10-21.md:
======================================================= AUTO-ANALYZE DETAILED REPORT ======================================================= Generated: 2025-10-21 14:30:00 +- Project Context ------------------------------------+ | Type: Python project with FastAPI framework | | Languages: Python 3.9+ | | Frameworks: FastAPI, SQLAlchemy, Pydantic | | Total Files: 127 | | Lines of Code: 12,450 | +-------------------------------------------------------+ +- Quality Assessment ---------------------------------+ | Overall Score: 88/100 [PASS] | | Tests: 45 tests, 92% passing (41/45) | | Coverage: 82% | | Standards: 89% compliant | | Documentation: 85% complete | | Pattern Adherence: 95% | +-------------------------------------------------------+ +- Strengths ------------------------------------------+ | * Well-structured API endpoints | | * Good test coverage on core modules | | * Consistent coding style | | * Clear separation of concerns | | * Effective use of Pydantic for validation | +-------------------------------------------------------+ +- Issues Found ---------------------------------------+ | Tests: | | * test_user_login() - AssertionError (auth.py:45) | | * test_token_refresh() - Timeout (auth.py:89) | | * test_logout() - Connection error (auth.py:112) | | * test_password_reset() - Invalid state (auth.py:145)| | | | Documentation: | | * 12 functions missing docstrings | | * API endpoint documentation incomplete | | | | Complexity: | | * get_user_permissions() - Cyclomatic: 18 (auth.py) | | * validate_token() - Cyclomatic: 16 (auth.py) | | * process_payment() - Cyclomatic: 15 (payment.py) | +-------------------------------------------------------+ +- All Recommendations --------------------------------+ | 1. [HIGH] Fix 4 failing tests in auth module | | -> Expected quality impact: +4 points | | -> Run /quality-check for auto-fix | | | | 2. [MED] Add docstrings to 12 public functions | | -> Improves maintainability and API documentation | | -> Expected quality impact: +2 points | | | | 3. [MED] Refactor 3 high-complexity functions | | -> Target: get_user_permissions(), validate_token()| | -> Expected quality impact: +2 points | | | | 4. [LOW] Complete API endpoint documentation | | -> Add OpenAPI descriptions | | -> Expected quality impact: +1 point | +-------------------------------------------------------+ Skills Loaded: code-analysis, quality-standards, pattern-learning Agents Used: autonomous-agent:code-analyzer, autonomous-agent:background-task-manager Patterns Stored: 1 new pattern in .claude-patterns/ Analysis Time: 2.3 minutes =======================================================
## See Also
- /analyze:quality - Comprehensive quality control with auto-fix
- /learn:init - Initialize pattern learning database