Generate Snapshot

Generate Codebase Snapshot

This skill generates a comprehensive code health snapshot using the analysis modules in analysis/.

When to Use

• During planning phase to identify work items
• To find refactoring hotspots (high churn + high complexity)
• To track technical debt over time
• Before major releases to assess code health
• To identify knowledge silos (bus factor risks)

How to Generate

# From repository root
python -m analysis.snapshot --output analysis-snapshot.json

Add --pretty flag to also print the JSON to stdout.

Note: The snapshot is written to the repository root (analysis-snapshot.json). This path is ignored by .gitignore.

Snapshot Structure

The snapshot contains these sections:

summary - High-level metrics dashboard

• files_with_changes: Number of files with git changes
• total_churn: Total lines added + deleted
• high_complexity_files: Count of files with high complexity
• todo_count, fixme_count: Debt marker counts
• circular_dependency_count: Architectural issues
• single_author_file_ratio: Bus factor indicator

priority_hotspots - Top 20 refactoring candidates

Files sorted by priority_score = change_count × max_complexity

{
    "path": "src/features/terminal/terminalManager.ts",
    "change_count": 45,
    "churn": 1200,
    "max_complexity": 18,
    "priority_score": 810
}

High priority_score = frequently changed AND complex = prime refactoring target.

git_analysis - Change patterns

• hotspots: Most frequently changed files
• temporal_coupling: Files that change together (hidden dependencies)
• bus_factor: Knowledge concentration risks

complexity - Code complexity metrics

• by_language.typescript: TypeScript file metrics (max_complexity, avg_complexity, function_count)
• high_complexity_functions: Functions with cyclomatic complexity > 10

debt_indicators - Technical debt markers

• debt_markers.by_type: TODO, FIXME, HACK comments by type
• large_files: Files exceeding 500 lines of code
• long_functions: Functions exceeding 50 lines

dependencies - Module coupling analysis

• circular_dependencies: Cycles in import graph
• highly_coupled_modules: Modules with fan-out > 10
• hub_modules: Modules with fan-in > 10
• layer_violations: Lower layers importing from higher layers

Interpreting Results

Priority Hotspots

1. Sort by priority_score descending
2. Top items = files that change often AND are complex
3. These are prime candidates for:
   • Breaking into smaller modules
   • Adding tests before changes
   • Simplifying complex functions

Temporal Coupling

Files with coupling_ratio > 0.8 changing together indicate:

• Hidden dependencies not visible in imports
• Copy-paste code that should be shared
• Features spread across unrelated files

Knowledge Silos

Files with author_count = 1 and change_count >= 3:

• Single point of failure for knowledge
• Consider documentation or pair programming
• Higher risk for bugs during that author's absence

Circular Dependencies

Any cycles in the import graph:

• Indicates tight coupling
• Makes testing difficult
• Consider introducing interfaces or restructuring

Example Usage in Planning

User: What should we work on next?

Agent: Let me generate a snapshot and analyze it...
[generates snapshot]

Based on the snapshot:

Top 3 priority items:
1. **src/features/terminal/terminalManager.ts** (priority: 810)
   - 45 changes, complexity 18
   - High churn indicates active development area
   - Recommend: Split terminal concerns into separate modules

2. **src/managers/common/nativePythonFinder.ts** (priority: 540)
   - 30 changes, complexity 18
   - Multiple FIXME markers found
   - Recommend: Address type guards and cache issues

3. **src/features/interpreterSelection.ts** (priority: 360)
   - 24 changes, complexity 15
   - Temporal coupling with settings files
   - Recommend: Reduce coupling with settings module