repo-analyzer
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
Configure TDD enforcement via pre-commit hooks and CI coverage gates. Use when setting up test-first development workflow, adding coverage gates, or enforcing TDD practices.
Configure TDD enforcement via pre-commit hooks and CI coverage gates. Use when setting up test-first development workflow, adding coverage gates, or enforcing TDD practices.
Merge multiple documentation sources (docs, GitHub, PDF) with conflict detection. Use when combining docs + code for complete skill coverage.
Merge multiple documentation sources (docs, GitHub, PDF) with conflict detection. Use when combining docs + code for complete skill coverage.
Move Prover formal verification expert for Aptos smart contracts. Write specifications (MSL), preconditions (requires), postconditions (ensures), invariants, abort conditions (aborts_if), quantifiers, schemas, and pragmas. Debug verification failures. Triggers on Move Prover, formal verification, spec, invariant, ensures, requires, aborts_if, precondition, postcondition.
Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.
Identify flaky tests from CI history and test execution patterns. Use when debugging intermittent test failures, auditing test reliability, or improving CI stability.
Auto-generate test data factories from schemas, types, or models. Use when creating test data infrastructure, setting up fixtures, or reducing test setup boilerplate.
Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.
Run mutation testing to validate test quality beyond code coverage. Use when assessing test effectiveness, finding weak tests, or validating test suite quality.
Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.
Identify flaky tests from CI history and test execution patterns. Use when debugging intermittent test failures, auditing test reliability, or improving CI stability.
Verify bidirectional traceability from requirements to code to tests. Use when relevant to the task.
Run mutation testing to validate test quality beyond code coverage. Use when assessing test effectiveness, finding weak tests, or validating test suite quality.
Auto-generate test data factories from schemas, types, or models. Use when creating test data infrastructure, setting up fixtures, or reducing test setup boilerplate.