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Found 216 Skills
Detect test smells, overmocking, flaky tests, and coverage issues. Analyze test effectiveness, maintainability, and reliability. Use when reviewing tests or improving test quality.
Shell script testing expertise using bash test framework patterns from unix-goto, covering test structure (arrange-act-assert), 4 test categories, assertion patterns, 100% coverage requirements, and performance testing
Staff-level codebase health review. Finds monolithic modules, silent failures, type safety gaps, test coverage holes, and LLM-friendliness issues.
This skill should be used when performing AI-powered mutation testing to evaluate and improve unit test quality. It generates targeted code mutants, runs tests to identify surviving mutants, and strengthens or creates tests to kill them. Accepts a file path, directory, or defaults to git diff changed files.
Use when the user wants to review a pull request, understand what a PR changes, assess risk of merging, or check for missing test coverage. Examples: "Review this PR", "What does PR #42 change?", "Is this PR safe to merge?"
Apply Test-Driven Development workflow for new features and bugfixes.
Orchestrates a continuous journey-builder → refine → restart loop. Runs journey-builder and refine-journey sequentially, improving the skill each iteration. Loops until all spec requirements are covered by journeys and the score reaches 95%.
Mutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.
Audit a test suite to find tests that give false confidence — tests that encode bugs, duplicate coverage, or are so heavily mocked they can't catch real regressions. Use to improve robustness, audit coverage, or harden a risky area.
Automates unit test creation for Go projects using the standard testing package with consistent software testing patterns including In-Got-Want, Table-Driven Testing, and AAA patterns. Use when creating, modifying, or reviewing unit tests, or when the user mentions unit tests, test coverage, or Go testing.
Guidelines for writing effective tests in this project
Analyzes code to identify untested functions, low coverage areas, and missing edge cases. Use when reviewing test coverage or planning test improvements. Generates specific test suggestions with example templates following amplihack's testing pyramid (60% unit, 30% integration, 10% E2E). Can use coverage.py for Python projects.