Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
222 skills found
Build RAG systems to ground LLMs in proprietary data. Includes vector database integration, embedding strategies, hybrid search, and advanced retrieval patterns for FastAPI backends.
A universal skill for automating GitHub Project V2 Kanban boards, supporting status transitions, sprint management, and interactive workflows via CLI.
Fetch, index, and search developer documentation from GitHub and websites to provide AI agents with accurate, grounded, and version-specific code context.
A decision-support tool for Claude Code users to select the optimal extension mechanism—slash commands, skills, subagents, or hooks—based on project requirements.
A toolkit for writing high-quality agent skills (SKILL.md files) for ClawdHub/MoltHub, covering structure, frontmatter schemas, content patterns, and agent-consumable documentation best practices.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
A collection of design patterns for the Langroid multi-agent framework, covering agent configuration, tool handling, task orchestration, and external integrations.
A Git-backed memory store for agent skills. Download, version, edit, and share custom agent behaviors and procedural knowledge using a CLI.
Provides targeted, concise English language editing and stylistic improvements for text without performing full rewrites.
Advanced web search, content extraction, and site crawling capabilities using the Tavily API, optimized for AI agent research and data gathering.
Virtual machine development expert focusing on bytecode design, stack-based/register-based VM implementation, memory management, and garbage collection.
Supermemory is a long-term memory infrastructure for AI agents, enabling persistent context, user profiles, and semantic RAG across multi-modal knowledge bases.