Agent Skills by 이태양
Showing 128 vetted skills indexed across 1 GitHub repositories.
convention-design
Summarize SRP, KISS, DRY, YAGNI, and SoC for class and module organization.
convention-folder-structure
Organize Python monorepo folder layouts with libs/{name}/src/{name}/__init__.py structure.
convention-logging
Enforce Python logging conventions for level selection, formatting, and masking.
code-refactor
Detect anti-patterns and perform incremental refactors with tests.
code-review
Orchestrate check-* skills to review Python code and produce prioritized reports.
adversarial-review
Review code, documentation, and design artifacts from Red Team and Blue Team perspectives.
systematic-debugging
Guide teams through a four-step debugging process to isolate root causes.
check-design-doc
Validate code signatures against CLAUDE.md and docs/design/ API patterns.
check-python-style
Validate Python code style, type hints, and Google-style docstrings.
quality-notebook
Automate Jupyter notebook quality with nbstripout, nbdime, and nbqa.
quality-precommit
Configure pre-commit hooks to run quality checks before commits.
check-anti-patterns
Detect design anti-patterns in Python code using AST-based analysis.
quality-bandit
Configure Bandit security rules and CWE/OWASP mappings for Python projects.
quality-mypy
Configure mypy strict mode, overrides, and plugins in pyproject.toml.
check-test-quality
Validate pytest test suites for AAA pattern, naming conventions, and coverage thresholds.
check-commit-message
Validate Git commit messages against Conventional Commits rules.
check-data-pipeline
Validate ADK tool scripts for file paths, ToolContext usage, and function signatures.
check-security
Scan Python codebases with Bandit and report security vulnerabilities by severity.
check-logging
Validate Python logging conventions for quality and security.
check-complexity
Measure code complexity and maintainability with radon CC and MI reports.
quality-ruff
Configure Ruff linting and formatting in pyproject.toml for Python projects.
check-config-validation
Validate YAML syntax and Pydantic schema conformance across dev, prod, and test environments.
check-notebook-coverage
Map Python modules to notebooks and report missing coverage.
3-step-workflow
Orchestrates AI development workflows by generating, decomposing and executing tasks with human-in-the-loop approvals.