Skill UI

This skill guides developers through creating a comprehensive refactoring plan and serving as a Request for Comments (RFC). It orchestrates detailed interviews, codebase analysis, and scope definition. It ensures the refactor is broken down into tiny, safe, incremental commits, culminating in a structured GitHub issue that serves as a definitive architectural and technical decision document.

This skill ingests public complaints from X/Twitter, automatically detecting, classifying, and clustering reported bugs. It cross-references symptoms with repository evidence (commits, deploys) and assigns ownership using defined routing rules. It is essential for product teams needing to monitor product health and gather early bug signals from social media channels.

This tool assists engineers in transforming vague work logs into structured, evidence-backed impact statements for professional documentation. It uniquely mines Copilot CLI session logs, git commits, and GitHub PRs to reconstruct forgotten work. It enforces a mandatory 'Action → Result → Evidence' impact contract, making it indispensable for preparing performance reviews, promotion packets, and self-assessments.

This skill analyzes raw git diffs or change descriptions to generate clear, story-driven commit messages. It strictly adheres to the Conventional Commits specification, ensuring that messages thoroughly explain *why* a change was made, not just *what* changed. It is ideal for writing meaningful commit history, preparing pull requests, or summarizing complex code changes for team collaboration.

This skill generates structured and standardized Git commit messages following the Conventional Commits specification. By analyzing code changes (via diff or status), it helps developers automatically create clear, actionable, and searchable commit history messages, ensuring consistency across the project.

This skill summarizes the current conversation into a structured handoff document, ideal for passing work to a fresh AI agent or starting a new session. It avoids content duplication by referencing existing artifacts (PRDs, plans, issues, commits) using their paths or URLs, ensuring the receiving agent has a concise and actionable overview of the project's state, open decisions, and next steps.

This meta-skill executes three crucial quality gates—ruff (linting), mypy (type checking), and pytest (unit testing)—in parallel over the staged file differences. It aggregates the results to provide a single, definitive BLOCK or APPROVE verdict, ensuring code readiness and mirroring the strict checks enforced by CI/CD pipelines before local commits.

This tool automates the diagnosis of production errors tracked by Sentry. By leveraging the Composio CLI, it retrieves comprehensive issue details, full stack traces, breadcrumbs, and associated commits. It maps stack frames to local source code, allowing agents to propose fixes and triage issues directly, eliminating manual copy-pasting.

This skill reconstructs the detailed workflow behind a specific coding artifact (like a feature or PR). By analyzing local `ax` agent traces, it connects commits, sessions, skills, and tool calls into a concise, evidence-grounded narrative. It is used when needing to understand the step-by-step process and key decisions that led to a result.

A comprehensive, adaptive framework for developing and implementing features, adhering to a strict, multi-phase workflow: Specify, Design, Tasks, and Execute. This skill ensures feature quality through granular task breakdown, atomic Git commits, requirement traceability, and mandatory, independent verification. It automatically adjusts its depth based on feature complexity (Small to Complex), providing a robust mechanism for managing technical debt and capturing project lessons.

This skill automatically rewrites raw commit messages to adhere to professional engineering standards. It strips out common 'AI/marketing slop' phrases (like 'robust implementation' or 'leverage') while strictly maintaining the Conventional Commits format. It ensures subjects are concise, bodies provide necessary context (why, breaking changes), and the tone is direct and actionable, making your project history clean and readable.

Code Health MCP provides structural maintainability feedback and design-level health scores, going beyond standard linting. It is crucial for AI-assisted coding workflows, allowing users to review code quality, verify score deltas after changes, and gate commits or pull requests to prevent codebase regression. Use it when refactoring, updating legacy modules, or ensuring the integrity of large diffs.