---
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
---