AI 工具全集 · 3,320 个
按热度(浏览量)排序,覆盖工具岛全部 11 个标准分类。点击工具卡片查看详情、价格、官网入口。
mend
Mend is an AI agent that repairs Playwright E2E tests broken by selector drift. It investigates the live DOM via tool calling, proposes a fix, and only accepts it after re-executing the test — never on the model's word alone. Low-confidence fixes route to human review instead of auto-merging.
bebok
Desktop GUI Headless Local-first AI coding agent in Rust (axum, tokio, rmcp, portable-pty) behind HTTP + SSE, with an Angular 20 zoneless client, Tauri 2 desktop shell(and mobile in near future). OpenAI/Anthropic/Z.ai/Ollama + more, MCP bridge, permission engine, PTY terminal.
livetest
Ferramenta dev (devDependency) que observa o projeto, usa o grafo de dependências para achar quais testes uma mudança afeta, roda só esse subconjunto e publica o resultado em 3 canais: terminal (agente de IA), log estruturado (qualquer ferramenta) e painel no VSCode (você).
hp-prime-kit
A reference for HP Prime PPL, with an entry for every command and function: syntax, examples and pitfalls. Plus an agent-ready toolkit — AGENTS.md with the PPL rules, a linter, an interpreter that runs the same source you install, and a .hpprgm builder — so an agent can write, check and package a program without touching a calculator.
FreeCADTool
FreeCAD CAD tool for AI agents: parametric 3D modeling (primitives, PartDesign bodies, sketches, pad/pocket/revolve, patterns, fillet/chamfer, booleans) and STEP/STL/3MF/OBJ/IGES import-export — pure C# over a local RPC bridge to a running FreeCAD. Cross-platform Windows/Linux/macOS.
firstdoctor
FirstDoctor AI: A Next-Gen Multilingual Clinical Decision Support System & Smart Hospital ERP. It uses Generative AI (Gemini) to translate native patient voices into expert clinical summaries and auto-generate smart prescriptions, eliminating language barriers and reducing doctor burnout. Built for the future of healthcare!
simple-rag-document-qa
Simple RAG Document QA is a lightweight Retrieval-Augmented Generation (RAG) project that answers user questions using information retrieved from a local document. The project demonstrates the complete RAG pipeline, including text preprocessing, lemmatization, document chunking, Hugging Face embeddings, FAISS vector search, and local Llama 3.1 gene
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