openai-agents-sdk
Build AI agents with the OpenAI Agents SDK for Python. Supports multi-agent handoffs, function tools, stateful sessions, streaming, and Azure OpenAI integration via LiteLLM.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
376 skills found
Build AI agents with the OpenAI Agents SDK for Python. Supports multi-agent handoffs, function tools, stateful sessions, streaming, and Azure OpenAI integration via LiteLLM.
Automated Vitest management skill: handles test execution, coverage reporting, failure diagnosis, and configuration management for TypeScript/JavaScript projects.
Fetch and parse transcripts from YouTube and Bilibili videos for summarization, QA, and content extraction using yt-dlp.
Create structured, high-quality technical implementation plans via an agent-driven, iterative process. Ideal for complex refactoring, new features, and technical design.
Generate professional Product Requirements Documents (PRD) and structure features for autonomous development cycles.
Expand seed keywords into comprehensive lists and cluster them by intent and topic to optimize your pillar content strategy.
A comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models like SCQA, First Principles, and Systems Thinking.
Implement the 'Engineering as Marketing' growth strategy: build free SEO-driven utility tools to drive organic traffic, capture leads, and convert visitors into customers without ad spend.
A structured PRD generator for vibe-coding MVPs. It guides you through defining product requirements, target audiences, and success metrics, ensuring a clear foundation for your development workflow.
Persistent, Git-friendly memory for Claude. Automatically store and retrieve project decisions, bug fixes, and coding patterns in a local .mv2 file.
A meta-skill for building robust AI agent skills using a TDD approach: define failure (RED), implement the skill (GREEN), and plug rationalization loopholes (REFACTOR).
Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.