A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
Agentic orchestrator for parallel coding agents — plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews.
Claude Skill: Multi-source content processor for NotebookLM. Supports WeChat articles, web pages, YouTube, PDF, Markdown, search queries → Podcast/PPT/MindMap/Quiz etc.
MCP server for NotebookLM - Let your AI agents (Claude Code, Codex) research documentation directly with grounded, citation-backed answers from Gemini. Persistent auth, library management, cross-client sharing. Zero hallucinations, just your knowledge base.
Open-source GEO content engineering and multi-site distribution system with AI tasks, RAG materials, analytics, and target-site publishing.
AutoCLI is a Blazing fast, memory-safe command-line tool — Fetch information from any website with a single command. Covers Twitter/X, Reddit, YouTube, HackerNews, Bilibili, Zhihu, Xiaohongshu, and 55+ sites, with support for controlling Electron desktop apps, integrating local CLI tools (gh, docker, kubectl), now powered by AutoCLI.ai .
The perfect companion for ClaudeCode/OpenClaw/Agent, Give your AI Agent the ability to reach information across the entire web, fetching real-time data from Bilibili, Zhihu, Twitter/X, YouTube, Weibo, Reddit, Facebook, Instagram, TikTok, Notion, Cursor and 55+ platforms with natural language — reusing your Chrome login session, no API keys needed.
OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM
LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically. Instead of traditional RAG (retrieve-and-answer from scratch every time), the LLM incrementally builds and maintains a persistent wiki from your sources。
The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
