Skill UI

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 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 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.

nano-pdf is a robust command-line interface (CLI) tool designed to modify PDF documents. Instead of requiring complex PDF editing syntax, users can provide natural language instructions (e.g., 'Change the title to X and fix Y') to make precise edits to specific pages of a PDF. This drastically simplifies the content modification process for technical users and automates complex document handling tasks.

This meta-skill orchestrates the entire academic writing workflow, from initial requirement extraction (topic, length, audience) to drafting, citation management, and final LaTeX/PDF compilation. It supports various modes, including compact skeletons for quick drafts and full manuscripts for publication-ready work. Ideal for users needing structured help with research papers or academic manuscripts.

Drafts a complete, publication-ready research paper section (e.g., Introduction, Method, Results) as a robust LaTeX fragment. This tool ensures the section adheres strictly to a predefined writing plan, maintaining narrative coherence, managing citation budgets, and slotting seamlessly into a larger academic manuscript.

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 tool parses a structured, multi-shot video script (following the ai-video-script format) and automatically generates a time-coded SRT subtitle file. It calculates cumulative timestamps based on the duration of each shot, ensuring accurate subtitle cues that sync with the voiceover content across the entire sequence. Ideal for the post-production stage of short-form video creation.

An internal, language-agnostic probe designed for investigating complex stack traces when the source language is unknown. It analyzes the provided trace to output structured, language-neutral elements, including failure contract checks, minimal reproduction guidance, and specific patch targets. This tool is vital for robust failure analysis and debugging across diverse codebases.

An internal utility designed for deep investigation of JavaScript or TypeScript stack traces. It analyzes complex runtime errors, providing specific checks for asynchronous boundaries, module resolution issues, JSON parsing failures, and type contract violations. It aids developers by generating minimal reproduction commands and identifying potential patch targets for robust error handling.

A specialized internal helper for deep meta-stack trace investigation in Python environments. This tool guides users through generating Python-specific root-cause checks, creating minimal reproducible test cases using pytest, and identifying precise targets for defensive patches (e.g., validation or guard clauses). It ensures robust debugging and testing practices.

This skill automates the migration of existing frontend code (such as those built with React, Vite, or other frameworks) into the proprietary Stitch Design format. It performs a multi-step workflow: first, extracting a self-contained static HTML file; second, analyzing source components and stylesheets to generate a comprehensive design system documentation (DESIGN.md); and finally, uploading both artifacts to the specified Stitch project for continuous iteration and improvement.