python-testing
Pytest-first Python testing with emphasis on fakes over mocks. Covers unit, integration, and async tests; fixture design; coverage setup; and debugging test failures. Use when writing tests, reviewing test quality, designing fixtures, setting up pytest, or debugging failures—e.g., "write unit tests for new feature", "fixture design patterns", "fakes vs mocks comparison", "fix failing tests".
95
Does it follow best practices?
Validation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-crafted skill description that excels across all dimensions. It provides specific capabilities (unit/integration/async tests, fixtures, coverage, debugging), includes natural trigger terms users would actually say, has an explicit 'Use when...' clause with concrete examples, and carves out a distinct niche with its pytest and fakes-over-mocks focus.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'unit, integration, and async tests; fixture design; coverage setup; and debugging test failures'. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what (pytest testing with fakes, unit/integration/async tests, fixtures, coverage, debugging) AND when with explicit 'Use when...' clause listing specific trigger scenarios and example phrases. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'write unit tests', 'fixture design patterns', 'fakes vs mocks', 'fix failing tests', 'pytest', 'coverage'. These match how developers naturally phrase testing requests. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche: Python-specific, pytest-specific, emphasis on 'fakes over mocks' philosophy. The specific tooling (pytest) and methodology (fakes) create clear boundaries unlikely to conflict with generic coding or other language testing skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill that efficiently communicates pytest best practices with strong emphasis on fakes over mocks. The actionable code examples and clear YOU MUST/NEVER constraints provide concrete guidance. The main weakness is the lack of explicit workflow sequences for common testing scenarios like debugging failing tests or the test-write-validate cycle.
Suggestions
Add a brief workflow section for debugging test failures with explicit validation steps (e.g., 1. Run failing test in isolation, 2. Check fixture state, 3. Verify assertions match expected behavior)
Consider adding a quick workflow for writing new tests that includes validation checkpoints (e.g., run test to see it fail first, implement, verify pass)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, assuming Claude's competence with Python and pytest. No unnecessary explanations of basic concepts; every section provides actionable value without padding. | 3 / 3 |
Actionability | Provides fully executable code examples for fakes, factory fixtures, and side effect capture. The patterns are copy-paste ready with clear context for when to use each. | 3 / 3 |
Workflow Clarity | While the skill provides clear patterns and constraints (YOU MUST/NEVER sections), it lacks explicit multi-step workflows with validation checkpoints. For a testing skill, guidance on test-debug-fix cycles or validation steps would strengthen this. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview and well-signaled one-level-deep references to detailed materials (test-doubles.md, anti-patterns.md, etc.). Content is appropriately split between quick patterns and detailed reference files. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
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