2026年9月10日 GitHub 热门项目
- 1–ayghri/i-have-adhd+3,854
A skill to stop your coding agent from burying the answer. ADHD-friendly output. 解读生成中
- 2新上榜
- 3–
38 editorial diagram types for Claude Code, Codex, and Pi. Self-contained HTML + SVG. No shadows. No Mermaid slop. 解读生成中
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- 5▲7
Prompt as Code | GPT Image 2 / 2.5 提示词与案例库,530+ 个案例、20+ 套工业级模板与可复用 Skills,新增 2.5 同提示词对比专区,附完整提示词与生成记录,持续更新。 解读生成中
- 6▲1
Notes of the book System Desgin Interview - An Insider's Guide 解读生成中
- 7▼1
- 8新上榜
Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click 解读生成中
- 9–obra/superpowers+731
An agentic skills framework & software development methodology that works. 解读生成中
- 10▲8
Local-first AI coding agent desktop: Electron + Rust host core + pi Agent Harness + user-installable plugins 解读生成中
- 11新上榜
Never stop coding. Free MIT AI gateway: one endpoint, 352 providers (150+ free), 1200+ models Kimi, Claude, GPT, Gemini, GLM, DeepSeek, MiniMax. Works with Claude Code, Codex, Cursor, OpenCode, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, Desktop/PWA. Built by 550+ contributors 解读生成中
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- 15新上榜
Open Source AI trading agent that operates autonomously across 1000+ markets - Polymarket, Kalshi, Binance, Hyperliquid, Solana DEXs, 5 EVM chains. Scans for edge, executes instantly, manages risk while you sleep. Agent commerce protocol for machine-to-machine payments. Self-hosted. Built on Claude. 解读生成中
- 16新上榜
Hundreds of models & providers. One command to find what runs on your hardware. 解读生成中
- 17新上榜
- 18新上榜JustVugg/colibri+157
Run frontier MoE models on hardware you already own — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 解读生成中
- 19▲5
- 20▼3pascalorg/editor+107
Open-source 3D architectural editor with a local CLI, MCP tools, and practical workflows for humans and AI agents. 解读生成中
- 21新上榜
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。 解读生成中
- 22新上榜