Skill UI

Automates architecture decision record (ADR) generation and guidance within technical documentation workflows, offering best-practice patterns, production-ready snippets, and validation for API docs, user guides, and automation needs.

Master modern API documentation using OpenAPI 3.1, AsyncAPI, and advanced AI tools. This skill facilitates the creation of world-class, interactive developer portals, robust SDK generation, and comprehensive API specifications. It ensures documentation is always accurate, actionable, and significantly improves the overall developer experience (DX), reducing time-to-integration.

This skill automates the generation of comprehensive changelogs and release notes directly from commit history, Pull Requests (PRs), and release tags. It ensures that documentation adheres to industry-standard formats, such as Keep a Changelog, streamlining release workflows and standardizing version management practices.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

Automates configuration reference generation within technical documentation, offering guidance, best practices, and production-ready code while validating outputs against standards to support API docs, user guides, architecture docs, and automation workflows.

This expert skill specializes in creating comprehensive, maintainable technical documentation directly from existing codebase, configurations, and comments. It can generate various artifacts, including API specifications, system architecture diagrams, detailed user guides, and technical references, ensuring consistency and accuracy using AI-powered analysis and industry best practices. Ideal for building robust documentation pipelines and standardizing documentation across large repositories.

Provides a structured approach for creating, editing, and analyzing .docx files with tooling and best practices for tracked changes, comments, formatting, and text extraction, covering JavaScript document generation and Python OOXML editing plus markdown-based redlining.

This tool generates comprehensive API contracts and OpenAPI specifications (3.0/3.1) from existing codebases, design documents, or schemas. It establishes a single source of truth for APIs by producing machine-readable contracts, supporting documentation, robust testing (including consumer-driven testing via Pact), and code generation. Use it when you need to define, verify, and document complex service interfaces.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.

This skill leverages Google Gemini's advanced image generation model to create high-quality, context-specific visuals. It is essential for developers needing placeholder images for UIs, generating detailed diagrams for technical documentation, or visualizing abstract concepts, logos, and assets across cross-platform projects (Windows, macOS, Linux).

This guide documents common mistakes, gotchas, and anti-patterns encountered during Kling AI API integration. It covers critical best practices regarding data types, token management, polling, URL persistence, and prompt engineering to ensure stable and reliable AI video generation workflows. Essential reading for robust development and troubleshooting.

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.