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Found 115 Skills
Run the full validation sequence before declaring any task done. Use this skill proactively after any code or infrastructure change — run pre-commit, mypy, and pulumi preview (where applicable) without waiting to be asked. Never declare success without passing checks.
Best practices for creating clean, atomic git commits with good messages. Use when: (1) staging and committing changes, (2) writing commit messages, (3) deciding what to group in a single commit, (4) handling pre-commit hook failures, (5) choosing between amend and new commit. Triggers on "commit", "stage", "git add", "write a commit message", or "commit my changes".
Open a pull request the bklit-ui way: stage and commit with pre-commit hooks, run ultracite from the repo root, run a production test build, fix failures, push, and create a PR with a structured summary. Use when the user asks to commit, push, open a PR, "ship it", or run the full pre-PR checklist.
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.
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.
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.
Run git-workspace-review first to understand which tests need updates. Use when updating existing tests, generating new tests for features, enhancing test quality, ensuring detailed coverage, pre-commit validation. Do not use when auditing test suites - use pensive:test-review. DO NOT use when: writing production code - focus on implementation first.
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
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.
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.
Use when prettier integration with editors, pre-commit hooks, ESLint, and CI/CD pipelines.
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.