Skill UI

This skill automatically collects raw activity data (merged PRs, issues, commits, and optional Slack updates) from repositories. It formats this data using predefined templates, runs quality checks, and prepares a structured changelog draft. It can automatically commit the draft to a new branch and open a Pull Request (PR) for review, making it ideal for generating weekly or monthly release notes.

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.

This guide demonstrates how to build a resilient, event-driven architecture for Gamma API integrations when native webhooks are unavailable. It utilizes a poll-based pattern, creating synthetic application-level events (start, completed, failed) by wrapping polling logic within an EventEmitter or message queue. Ideal for building background workers that reliably track and react to the full lifecycle of content generation.

This skill generates complete gRPC service definitions, client/server stubs, and robust implementations directly from Protocol Buffer (.proto) files. It supports all streaming patterns (unary, server, client, bidirectional) and includes advanced features like authentication interceptors, health checks, and automatic REST-to-gRPC gateway generation, making it ideal for building high-performance, enterprise-grade microservices.

This skill guides developers on building scalable, event-driven pipelines around Ideogram's synchronous API. It demonstrates implementing robust async patterns, including using job queues (like BullMQ) for batch generation, handling webhook callbacks for post-processing, and setting up full asset modification pipelines. Ideal for production-grade marketing and content workflows.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.

A comprehensive guide for developers on utilizing the Kling AI Text-to-Video REST API. This skill provides minimal working examples in Python and Node.js, demonstrating the complete workflow: submitting a text prompt, polling the task status, and retrieving the final generated video URL. Ideal for integrating advanced AI video generation capabilities into development projects.

This skill provides comprehensive monitoring and tracking for asynchronous video generation tasks using the Kling AI API. It supports polling single tasks, managing large batches of jobs, and detecting stalled or failed processes across multiple video endpoints. Ideal for developers building robust workflow dashboards, implementing reliable batch processing, or debugging long-running media generation pipelines.

Automatically generates robust, schema-compliant mock API servers directly from OpenAPI specifications. This tool supports advanced testing features, including stateful CRUD operations, Faker-driven dynamic data generation, configurable latency simulation, and detailed request recording for contract testing. Ideal for frontend parallel development and isolated integration testing.

This skill provides comprehensive strategies for implementing robust caching mechanisms for OpenRouter API calls. It covers in-memory caching using Python, persistent caching with Redis for scalability, and utilizing provider-specific features like Anthropic prompt caching. By implementing deterministic key generation and managing Time-To-Live (TTL), developers can significantly reduce API costs and improve application latency when handling repetitive or similar LLM queries, making it ideal for RAG systems and high-volume applications.

Configure a comprehensive local development environment for integrating the TwinMind API. This guide walks you through setting up a project structure, configuring environment variables, and building a reusable client class using TypeScript and Axios. It is ideal for developers building applications that require local testing of transcription, summary generation, or meeting automation workflows.

Orchestrates a comprehensive, end-to-end pipeline for academic paper generation. It guides the user from a raw narrative report or research idea to a polished, submission-ready PDF. The process includes structured planning, figure generation (via SVG, Gemini, or Mermaid), LaTeX writing, compilation, and an automated multi-round improvement loop, ensuring the final output adheres to specified venue styles (e.g., ICLR, NeurIPS).