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Found 114 Skills
Multi-language code quality gate with auto-detection and language-specific linters. Use when user asks to "run quality checks", "quality gate", "lint all", "check everything", "pre-commit checks", or "is this code ready to commit". Use for verifying code quality across polyglot repos. Do NOT use for single-language linting (use code-linting) or comprehensive code review (use systematic-code-review).
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptools_scm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.
Creates context-aware git commits with smart pre-commit checks, submodule support, and conventional commit message generation. Use when user requests to commit changes, stage and commit, check in code, save work, save changes, push my code, finalize changes, add to git, create commits, run /commit command, or mentions "git commit", "commit message", "conventional commits", "stage files", "git add", or needs help with commits.
Expert quality gate decisions for iOS/tvOS: which gates matter for your project size, threshold calibration that catches bugs without blocking velocity, SwiftLint rule selection, and CI integration patterns. Use when setting up linting, configuring CI pipelines, or calibrating coverage thresholds. Trigger keywords: SwiftLint, SwiftFormat, coverage, CI, quality gate, lint, static analysis, pre-commit, threshold, warning
Guides architects on when and how to use goal-seeking agents as a design pattern. This skill helps evaluate whether autonomous agents are appropriate for a given problem, how to structure their objectives, integrate with goal_agent_generator, and reference real amplihack examples like AKS SRE automation, CI diagnostics, pre-commit workflows, and fix-agent pattern matching.
Use when prettier integration with editors, pre-commit hooks, ESLint, and CI/CD pipelines.
Master Git hooks setup with Husky, lint-staged, pre-commit framework, and commitlint. Automate code quality gates, formatting, linting, and commit message enforcement before code reaches CI.
This skill should be used when setting up code quality tooling with ESLint v9 flat config, Prettier formatting, Husky git hooks, lint-staged pre-commit checks, and GitHub Actions CI lint workflow. Apply when initializing linting, adding code formatting, configuring pre-commit hooks, setting up quality gates, or establishing lint CI checks for Next.js or React projects.
Set up and optimize repositories for AI coding agents. Creates minimal AGENTS.md, CLAUDE.md symlink, docs/REQUIREMENTS.md, docs/BUSINESS-RULES.md, feedback loops, and deterministic enforcement (Claude Code hooks, OpenCode plugins). Use when user wants to make a repo AI-friendly, set up AGENTS.md/CLAUDE.md, document requirements/business rules for AI, add pre-commit hooks for AI workflows, or optimize codebase structure for coding agents.
Build, tune, and operate Ruff for Python linting, formatting, and editor/CI integration. Use when adding or updating Ruff configuration, migrating from Black/Flake8/isort, selecting rule families, enforcing fix safety, or debugging lint/format behavior in local development, pre-commit, and CI.
Run Python quality checks with ruff, pytest, mypy, and bandit in deterministic order. Use WHEN user requests "quality gate", "lint", "verify code quality", "check python", or "pre-commit check". Use for pre-merge validation, CI/CD gating, or comprehensive code quality reports. Do NOT use for single-tool runs (run tool directly), debugging runtime bugs (use systematic-debugging), refactoring (use systematic-refactoring), or architecture review.
Code quality gatekeeper and auditor. Enforces strict quality gates, resolves the AI verification gap, and evaluates codebases across 12 critical dimensions with evidence-based scoring. Use when auditing code quality, reviewing AI-generated code, scoring codebases against industry standards, or enforcing pre-commit quality gates. Use for quality audit, code review, codebase evaluation, security assessment, technical debt analysis.