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Validate commit messages against your project's commitlint configurations for Conventional Commits compliance. Detect, extract, and explain rules from .commitlintrc or commitlint.config.js to set up CI/CD enforcement, pre-commit hooks, generate LLM prompts, and debug rejection errors.
npx claudepluginhub jamie-bitflight/claude_skills --plugin commitlintShare bugs, ideas, or general feedback.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit history.
Creates git commits using conventional commit format with appropriate emojis, following project standards and creating descriptive messages that explain the purpose of changes.
AI-powered conventional commit message generator with smart analysis
Auto-configure code quality tools and generate custom /fix command for parallel agent-based fixing
Git commit workflow: atomic commits, validation, conventions
LLM-powered guardrails for Claude Code. Turn every AI mistake into a rule AI can't repeat.
Read The Fucking Prompt — finds the strongest user reaction to an AI instruction-following failure in a chosen session, reconstructs the triggering assistant output, and renders a shareable terminal-style PNG.
This skill should be used when the model needs to ensure code quality through comprehensive linting and formatting. It provides automatic linting workflows for orchestrators (format → lint → resolve via concurrent agents) and sub-agents (lint touched files before task completion). Prevents claiming "production ready" code without verification. Includes linting rules knowledge base for ruff, mypy, and bandit, plus the linting-root-cause-resolver agent for systematic issue resolution.
Comprehensive Perl 5.30+ development plugin with modular skills for scripting, CPAN ecosystem, environment setup, testing, linting, and validation. Includes specialized agents for script development, code auditing, and CLI architecture.
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.
Build FastMCP 3.x Python MCP servers — covers provider/transform architecture (including CodeMode, Tool Search, and server-level transforms), component versioning, session state, authorization (MultiAuth, PropelAuth, connection-pooled token verifiers), evaluation creation, Pydantic validation, async patterns, STDIO and HTTP transports, nginx reverse proxy deployment, background tasks, Prefab Apps UI, security patterns, client SDK usage, testing, deployment, and migration from FastMCP v2. TypeScript is a legacy reference only and is not updated for v3.
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