Skill UI

This comprehensive guide details how to set up OAuth 2.0 client credentials for the Navan REST API. It provides complete, runnable code examples in both TypeScript (Node.js) and Python. Users can follow these steps to exchange client secrets for a secure bearer token, enabling programmatic access to Navan's booking and travel data. This is essential for configuring new Navan integrations or rotating API credentials.

This skill provides a comprehensive guide and code examples for creating a complete OneNote structure (Notebook, Section, and Page) using the Microsoft Graph API. It specifically addresses the critical technical requirement that OneNote content must follow strict XHTML standards, detailing common HTML pitfalls (such as unclosed tags, unsupported attributes, and merge cell usage) that can lead to silent content corruption during API calls.

This skill integrates 10 major academic literature APIs (including PubMed, arXiv, OpenAlex, Crossref, etc.). It guides users on converting complex research intents into reproducible, verifiable lookups for papers, preprints, citations, and open-access full texts. It ensures results include verifiable provenance and follows a rigorous workflow for maximum accuracy in scholarly research.

This skill generates a comprehensive, structured meeting notes page in HTML format. It captures all essential meeting elements, including attendees, a detailed agenda checklist, key decisions, and a trackable action items table (with owners and due dates). Ideal for documenting meeting minutes, 1:1 recaps, or all-hands summaries to ensure clear follow-up and accountability.

A comprehensive guide and pattern library for reviewing, troubleshooting, and safely modifying Cisco IOS and IOS-XE configurations. This skill outlines best practices for using read-only 'show' commands, validating ACL wildcard masks, managing configuration hierarchy, and following stringent change control procedures to prevent network disruptions.

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.

This guide provides a systematic, safe workflow for refactoring rapidly developed or AI-generated fullstack applications. It focuses on identifying and removing dead code, fixing broken imports, and consolidating repetitive logic while strictly maintaining existing API contracts, routes, and core functionality. Use this before launch to ensure a stable, maintainable production baseline, following a 'surgery, not demolition' philosophy.

Automates the daily triage of GitHub Pull Requests, CI/CD failures, and new issues. It uses the GitHub API to pull relevant information, summarizes the findings into actionable buckets (e.g., needing review, awaiting action, CI failed), and persists follow-up notes to long-term memory for context retention. Ideal for keeping track of complex development workflows.

This meta-skill converts messy meeting inputs—such as transcripts, rough notes, or recordings—into highly structured, actionable outputs. It automatically extracts key decisions, assigns owners, generates follow-up tasks, and creates drafts for popular collaboration platforms like GitHub, Trello, or Notion, ensuring continuity from discussion to execution.

This comprehensive guide and tool suite transforms web designs created in Stitch (HTML/CSS) into clean, production-ready React Native components. It meticulously maps HTML elements (like <div>, <button>) to native RN primitives (View, Pressable) and converts complex CSS styles (Flexbox, typography, shadows) into React Native's StyleSheet.create(). It guides developers to follow architectural best practices, including Atomic Design and data decoupling, for scalable cross-platform mobile development.

This skill analyzes your Gmail inbox over a specified time window, filtering out noise such as newsletters, receipts, and automated notifications. It surfaces only actionable items—emails requiring a direct reply, a decision, or follow-up—and even drafts suggested replies for efficiency.

This skill implements the LLM Council concept, enabling multiple open-weight language models to collaborate on complex queries. The process follows a three-stage deliberation: 1) Parallel independent responses from selected models; 2) Peer-to-peer ranking of the responses; and 3) Final synthesis by a designated Chairman LLM. Powered by Fireworks AI, it ensures deep, multi-faceted analysis and consensus building for high-stakes decision support.