coverage-reporter
About
The coverage-reporter skill generates and analyzes test coverage reports for Python and TypeScript to identify gaps and track trends. It activates after test runs or before commits to ensure quality thresholds are met. The skill requires git context and can parallelize with testing and code review workflows.
Quick Install
Claude Code
Recommended/plugin add https://github.com/majiayu000/claude-skill-registrygit clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/coverage-reporterCopy and paste this command in Claude Code to install this skill
Documentation
Coverage Reporter Skill
Comprehensive test coverage analysis and reporting for Python and TypeScript code.
When This Skill Activates
- After running test suite
- Before committing changes
- Tracking coverage over time
- Investigating coverage gaps
- Reporting on coverage metrics
Coverage Analysis Methodology
Phase 1: Coverage Collection
Python Coverage
cd backend
pytest --cov=app --cov-report=html --cov-report=term-missing
TypeScript Coverage
cd frontend
npm run test:coverage
Phase 2: Gap Analysis
Step 2.1: Identify Untested Code
For each file:
1. Count lines not covered
2. Identify untested functions
3. Identify untested branches
4. Calculate coverage percentage
Step 2.2: Prioritize by Risk
| Risk Level | Type | Priority |
|---|---|---|
| Critical | Auth, crypto, data access | Fix immediately |
| High | Business logic, validation | Fix within 48h |
| Medium | Utils, helpers | Fix within 1 week |
| Low | Formatting, display | Nice to have |
Phase 3: Coverage Report Generation
## Test Coverage Report
**Date:** [DATE]
**Overall Coverage:** [X]%
### Summary
- Backend: [X]%
- Frontend: [Y]%
- Target: 80%
### Critical Gaps
- [File]: [X]% - [reason]
- [File]: [Y]% - [reason]
### Trends
- Week 1: 75%
- Week 2: 77%
- Week 3: 79%
- Trend: Improving
### Recommendations
1. [Recommendation 1]
2. [Recommendation 2]
Phase 4: Trend Analysis
1. Historical coverage
- Track weekly/monthly trends
- Identify degradation
- Project future coverage
2. Coverage velocity
- How fast is coverage improving?
- Estimate time to target
3. Coverage stability
- Which areas consistently low?
- Which areas consistently high?
Coverage Requirements by Layer
| Layer | Target | Minimum |
|---|---|---|
| Services | 90% | 80% |
| Controllers | 85% | 75% |
| Models | 80% | 70% |
| Utils | 90% | 85% |
| Routes | 75% | 65% |
| Components (Frontend) | 80% | 70% |
Quick Coverage Commands
# Python coverage with details
cd backend
pytest --cov=app --cov-report=html --cov-report=term-missing -v
# Frontend coverage
cd frontend
npm run test:coverage
# Coverage diff against main
# (Identify what new code is untested)
git diff main...HEAD | grep "^+" | wc -l
Gap Remediation Workflow
For Each Untested Component:
1. Understand the code
- What does it do?
- When is it called?
- Why isn't it tested?
2. Determine test strategy
- Unit test?
- Integration test?
- E2E test?
3. Write tests
- Happy path
- Error cases
- Edge cases
4. Verify coverage
- Re-run coverage
- Confirm improved
Integration with test-writer
When coverage gaps identified:
- Report findings to test-writer skill
- Request test generation for gaps
- Re-run coverage after tests added
- Track improvement
Validation Checklist
- Coverage >= target percentage
- No untested critical code
- All public APIs covered
- Error paths tested
- Edge cases covered
- Coverage trend is improving
- No artificial coverage inflation
References
- Coverage requirements in CLAUDE.md
- See test-writer skill for test generation
- Testing patterns in python-testing-patterns skill
GitHub Repository
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