Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
424 skills found
Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.
Linter-driven refactoring agent that resolves complexity issues like cyclomatic depth, primitive obsession, and long functions using automated pattern extraction.
Standardized technical documentation templates for ADRs, runbooks, architecture, and knowledge transfer in AI agent workflows.
Generates structured Handoff Pack prompts for delegating scoped coding tasks to Gemini with clear instructions, acceptance criteria, and output requirements.
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.
A framework for creating, testing, and managing autonomous AI subagents within project environments using Test-Driven Development principles.
Standardized detective skill integration for agent roles. Maps agents to code-analysis skills and enforces claudemem usage for memory-indexed code investigation.
Guidance for creating and integrating new ECS components, including class design, JSON serialization, custom editor UI implementation, and DI registration.
A comprehensive guide and reference for building, orchestrating, and deploying AI agents using the Google Agent Development Kit (ADK).
React Native best practices for Expo and bare workflow. Supports project structure, navigation, NativeWind styling, platform-specific code, and TypeScript integration.
A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.
Enforces a strict evidence-based debugging workflow using structured observation, hypothesis testing, and causality validation to eliminate speculation in technical investigations.