Skill UI

A lightweight tool designed to fetch data from a single URL via HTTP/HTTPS using various methods (GET, POST, PUT, DELETE). It serves as a fast, direct shell wrapper, ideal for replacing complex agent steps that only require simple data retrieval. Supports configurable timeouts and response body capping, making it perfect for quick data fetching in meta-skill workflows.

This CLI tool automates the entire issue triage lifecycle for Linear and Jira. Users can pull backlogs, cluster duplicates, apply labels, reassign owners, and post detailed summaries—all directly from the shell. It is ideal for weekly backlog cleanup, post-release bug sweeps, and synchronizing data between multiple tools (e.g., Sentry to Linear).

This meta-skill runs three independent code reviewers—safety, test coverage, and style—in parallel over the current uncommitted diff. It then arbitrates a single, authoritative verdict (BLOCK, BLOCK_WITH_OVERRIDE, or PASS_WITH_NOTES) based on strict priority rules, offering a comprehensive second opinion before committing code.

This meta-skill scans a target codebase path to extract modules, dependencies, and hotspots. It then intelligently classifies the most appropriate architectural diagram type (class, sequence, component, etc.) and renders the diagram simultaneously as both PlantUML source code and editable draw.io XML. Finally, it composes a structured Word document containing both deliverables for comprehensive documentation.

This meta-skill automatically inspects open GitHub Pull Requests, failing CI runs, and new issues. It utilizes the GitHub API to gather critical updates and summarizes the findings into three actionable buckets (To Review, Awaiting Me, CI Red). Finally, it persists follow-up tasks to long-term memory, helping developers efficiently prioritize their daily workflow.

This meta-skill automates the process of handling a GitHub issue by fetching context, delegating the fix to a sub-agent, summarizing the resolution, and finally opening a pull request (PR). It is best suited for small, well-scoped issues with clear acceptance criteria. This pipeline simulates a complete development workflow.

This meta-skill provides a comprehensive, step-by-step plan for complex software migrations. It intelligently coordinates multiple steps, including gathering migration requirements, classifying the source and target stacks (e.g., CJS to ESM, Py2 to Py3), searching authoritative guides, and analyzing repository diffs to ensure a guided, reliable upgrade path for large-scale projects.

This meta-skill automates the complex process of modernizing legacy PDF documents (e.g., historical contracts, scanned manuals, legal documents). It follows a structured four-step pipeline: structural extraction, natural-language rewriting of problem pages, generating an audit summary, and finally re-merging the content into a single, refined PDF. This ensures content is modernized while maintaining fidelity to the original meaning, providing a human review summary before finalization.

This meta-skill implements a combinator pattern for deep security analysis. It runs three independent gates—policy governance, secret scanning, and audit logging—over a candidate operation. It enforces a strict arbitration rule (Policy DENY > Scanner WARN > ALLOW) to provide a robust, multi-layered verdict, making it ideal for reviewing critical code changes or environment modifications for safety compliance.

This meta-skill is designed exclusively to orchestrate and formalize complex workflows composed of multiple existing skills. It acts as a comprehensive governance layer, guiding the user through intent clarification, trigger collision checks, linting, and proposal persistence. Use it when proposing a multi-step, integrated solution, but do not use it for simple standalone skill requests.

This meta-skill is designed to automate the diagnosis of complex programming errors. When presented with a stack trace, traceback, or failing log, it orchestrates a multi-step investigation. It parses the trace, searches the repository history, checks relevant issues, and analyzes code diffs to provide a comprehensive brief for root cause analysis and debugging.

This comprehensive skill provides expert guidance for integrating with Google NotebookLM programmatically, covering both the `nlm` Command Line Interface (CLI) and Measurement/Control Plane (MCP) tools. Use it to automate workflows, manage notebooks, add diverse sources (URLs, YouTube, Drive), and generate various complex content types, such as reports, quizzes, mind maps, and videos. Essential for developers building upon NotebookLM's advanced research capabilities.