Инструменты

Plugin for building LLM apps: LangGraph agents with memory and tool use, hybrid-search RAG, vector index tuning, prompt optimization and LLM evaluation. Includes 3 subagents and 8 skills.

MLOps plugin from the wshobson/agents marketplace: data scientist, ML engineer and MLOps engineer subagents and a /ml-pipeline command that runs the pipeline in phases from data analysis and training to deployment and monitoring, plus ML pipeline and recommender system skills.

Cross-platform shell built around structured data: pipelines pass tables instead of plain text, and you can open and query JSON, CSV, TOML and more directly. Default builds include nu --mcp, which serves it to agents as an MCP server.

Converts PDFs, images, PPTX, DOCX, XLSX, EPUB and more into Markdown, JSON or HTML, preserving tables, formulas and layout, with optional LLM help for higher accuracy. Needs Python 3.10+ and PyTorch; large-scale commercial use of the model weights needs a paid license.

Headless browser written from scratch in Zig for AI agents and automation, using less memory and running faster than headless Chrome in official benchmarks. fetch turns pages into Markdown, and it can serve CDP or MCP. Telemetry is on by default (an env var disables it); Windows needs WSL.

ByteDance's open-source multimodal agent CLI: operates browsers via vision or the DOM and calls terminal, file and MCP tools to complete tasks, with a Web UI and headless server mode. Needs Node.js 22+ and a model API key.

Browser automation MCP server open-sourced by ByteDance in the UI-TARS-desktop repo: drives pages through Puppeteer accessibility data with an optional vision mode, running locally over stdio or remotely over SSE or Streamable HTTP.

Detects your CPU, memory and GPU and scores open models on whether they run, speed, quality and context, then works with local runtimes like Ollama, llama.cpp and MLX to download and benchmark them. recommend --json gives agent-friendly output.

Translates full PDF papers while preserving formulas, charts and layout (command pdf2zh), outputting monolingual and side-by-side bilingual PDFs. Uses Google Translate by default and can switch to DeepL, OpenAI and others; also runs as an MCP server.

Local model gateway that routes requests from Claude Code, Codex and other coding agents to providers like DeepSeek, Kimi, Gemini and OpenRouter, with failover, rule-based routing and request logs. Bring your own API keys.

Package manager for the open Agent Skills ecosystem: one command installs skills from GitHub and other sources into Claude Code, Codex, Cursor and dozens more agents, with search, update and removal. From Vercel Labs.

When a user asks whether a skill exists for some task, searches the skills.sh ecosystem with the skills CLI, then recommends and installs a fit. Maintained by Vercel Labs and shipped in the skills CLI repo.

Trading research agent from HKU's HKUDS whose MCP server offers 70+ tools for quotes, financials, backtesting and research across A-shares, Hong Kong and US stocks and crypto. Needs local Python; HK, US and crypto research need no API key. For research only; not investment advice.

Give it a person's name or a vague need and it researches and distills their mental models, decision style and voice into a callable persona skill (e.g. Munger, Feynman, Jobs) to aid analysis and decisions. Works with Claude Code, Codex and other agents.

Distills technical books, doc folders or paper collections (PDF, EPUB, DOCX, Markdown and more) into an agent skill with a core framework, on-demand chapter files, glossary and cheat sheet for lookup while working. Extraction needs local Python; poppler or docling optional for PDFs.

Official CLI for Tencent's open-source knowledge base platform WeKnora: upload documents, manage knowledge bases, run hybrid search and answer questions from them, or serve as a read-only MCP. Needs your own WeKnora deployment and a source build with Go 1.26+.

Lightweight command-line JSON processor that slices, filters, maps and transforms JSON with concise expressions and no runtime dependencies. A staple for agents parsing API responses and logs.

Anthropic's official data skill that guides agents to write, translate and optimize SQL across Snowflake, BigQuery, Databricks, PostgreSQL and other warehouse dialects, including CTEs, window functions and analytical aggregations.

Data analysis plugin: writes queries in each SQL dialect, explores datasets, runs statistical tests, charts with Python and builds interactive HTML dashboards, then reviews methods and conclusions before sharing. Connects to Snowflake, BigQuery and other warehouses. Official from Anthropic.

Runs the cognee memory engine as an MCP server whose remember, recall and forget tools write documents and conversations into a knowledge graph and vector store for cross-session retrieval. Start it with Docker or uv first; needs an LLM API key by default, or connect to Cognee Cloud.

Office command-line tool built for AI agents: a single binary that creates, reads and edits Word, Excel and PowerPoint files without Office installed, and renders them to HTML or PNG so agents can check the layout.

Writes conversations and business data into Graphiti's temporal knowledge graph so agents can retrieve long-term memory by entity, fact and time. Open source from Zep; start FalkorDB or Neo4j and the server with Docker first, and configure an LLM API key.

Google Classroom skill for the gws CLI: lets agents create and manage courses, maintain rosters and send invites, and post assignments, materials and announcements, so teachers can set up classes in bulk. Needs gws with OAuth via a Google Cloud project; not officially supported by Google.

Built-in gws CLI skill: lets agents create Google Forms, add or edit questions in bulk, publish and read responses, handy for sign-ups, surveys and class feedback. Needs gws with a Google account authorized via your own Google Cloud project; not an officially supported Google product.