Skill UI

This toolkit provides deterministic, programmatic capabilities for working with PDF documents. Users can extract structured data like text and tables, merge multiple PDFs, split documents by page ranges, fill out PDF form fields using JSON data, or generate entirely new PDFs from scratch using libraries like ReportLab. It is designed for explicit, byte-level operations, making it ideal for automation workflows that require precise document manipulation.

Use the Composio CLI to automate the entire Pull Request review and Continuous Integration (CI) triage process for GitHub and GitLab. This tool fetches code diffs, analyzes failure logs, posts detailed inline review comments, and iteratively patches and re-runs CI checks until the code passes, significantly enhancing code quality and developer efficiency.

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.

A utility for managing and installing specialized AI skills into the Codex environment. It supports fetching curated skills from predefined sources (like openai/skills) or installing custom capabilities directly from any GitHub repository, including private ones. It handles listing available skills, installing specific modules, and provides robust fallback methods (download/git sparse checkout).

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 diagnostic tool designed for analyzing Go language panics and complex stack traces. It provides structured guidance, including Go-specific nil/error checks, minimal reproducible `go test` commands, and precise patch targets, enabling highly efficient root cause analysis and debugging for Go projects.

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.

An internal helper designed for analyzing Rust panics and backtraces. It guides users to perform Rust-specific checks for proper handling of Option and Result types, generates minimal reproducible cargo test commands, and pinpoints exact patch targets for safe code improvements.

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.

Analyzes any frontend codebase (React, Vue, Svelte, Angular, plain CSS, Tailwind) to document the complete design system. It extracts crucial visual elements—including color palettes, typography, spacing scales, and component patterns—directly from source files, without needing to build, render, or run the application. Ideal for design audits, visual language documentation, or migrating a project's identity into a design tool.

Designed to capture a fully functional, standalone static HTML file from any modern web application (React, Next.js, Vue, etc.). It resolves all computed styles and inlines CSS and images (as Base64) into the output, ensuring the captured view can be shared or archived without external dependencies. Supports both automated snapshotting via Puppeteer and interactive capture.