Write, review, and restructure Python tests with behavior-driven naming conventions.
npx skills add https://github.com/aaronsilinskas/ai-skills --skill as-test-dev
Please help me install this Agent Skill. Skill: as-test-dev Source: https://github.com/aaronsilinskas/ai-skills/tree/main/as-test-dev Command: npx skills add https://github.com/aaronsilinskas/ai-skills --skill as-test-dev
Write, review, or restructure Python tests with a product-focused, behavior-driven philosophy. Use this skill whenever writing new tests, auditing existing ones, naming test functions, or planning coverage — even if the user just says 'add tests' or 'check my tests.' Also use when the user asks whether a test is good, what to test next, or whether their test suite is missing something. Covers naming conventions, test structure, and distinguishing behavior-driven tests from code-path-mirroring tests.
Describe the code behavior you want verified and request test stubs or an audit.
High-intent search queries and answers about installing and using this skill.
Use this skill to audit existing pytest suites, identify missing test-coverage areas, and generate behavior-focused test stubs. Describe the module behavior you want verified, and it provides naming conventions and structural guidance to plan test strategy across product features.
A good pytest function name reflects guaranteed product behavior and scenarios rather than mirroring internal code paths. Use naming conventions that document what the feature guarantees, making tests readable and resistant to breaking during implementation refactoring.
Audit Python test-coverage by evaluating whether existing tests verify product guarantees or merely mirror code paths. Restructure test suites by planning coverage across modules, applying behavior-driven principles to identify gaps, and renaming functions to reflect guaranteed behavior.
Distinguish between behavior-driven and code-path-mirroring tests whenever writing new tests or auditing existing ones. Behavior-driven tests codify product guarantees and survive refactoring, while code-path-mirroring tests break when internal implementation changes without altering product behavior.
Yes, you can plan test strategy across multiple Python modules by applying behavior-driven testing principles. Audit modules to establish naming conventions and plan test-coverage in a product-focused way that clarifies what each feature guarantees, ensuring comprehensive suite validation.
kynopticAudit test suites for vanity tests and mock-to-assertion ratios.
haakonbullRefactor Python pytest suites by merging overlaps, parameterizing duplicates, and pruning dead coverage.
clostaunauCodify pytest conventions, AAA structure, fixtures, mocking, and coverage standards.
lekmanWrite TypeScript tests with Bun and enforce RED-GREEN-REFACTOR workflows.
