Skill UI

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 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.

Use PaperJSX to automate the creation of professional documents, including PPTX presentations, DOCX reports, XLSX spreadsheets, and PDF files. By providing a structured JSON specification, this tool generates ready-to-use files, eliminating manual formatting and ensuring consistency across large-scale reporting and content generation pipelines.

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.

An internal tool designed to validate candidate meta-skills (SKILL.md) before registration. It performs comprehensive structural linting (G1 rules) and a scheduler dry-run (G2) to ensure the workflow is deterministic, references are correct, and templates resolve properly. It outputs detailed JSON diagnostics.

An internal utility designed to manage the entire lifecycle of meta-skill proposals within the opensquilla ecosystem. It allows developers to atomically write new skill proposals, list existing candidates, and formally accept them after passing rigorous gate checks (linting, smoke testing, E2E). This tool ensures structured and controlled deployment of new skills.

An internal, non-user-invocable agent used by the meta-skill creator process. It executes positive (G3) and negative (G4) smoke tests against a candidate meta-skill definition (SKILL.md). It simulates meta-resolution using LLM-generated fixtures and reports the results in a structured JSON format.

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).

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.