name: generate-snapshot description: Generate a codebase health snapshot for technical debt tracking and planning. Analyzes git history, code complexity, debt markers, and dependencies to identify hotspots and refactoring priorities. argument-hint: '--output path --pretty'
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 changestotal_churn: Total lines added + deletedhigh_complexity_files: Count of files with high complexitytodo_count,fixme_count: Debt marker countscircular_dependency_count: Architectural issuessingle_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 filestemporal_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 typelarge_files: Files exceeding 500 lines of codelong_functions: Functions exceeding 50 lines
dependencies - Module coupling analysis
circular_dependencies: Cycles in import graphhighly_coupled_modules: Modules with fan-out > 10hub_modules: Modules with fan-in > 10layer_violations: Lower layers importing from higher layers
Interpreting Results
Priority Hotspots
- Sort by
priority_scoredescending - Top items = files that change often AND are complex
- 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