Tools

Extracts text and tables from PDFs; merges, splits and rotates pages, adds watermarks, fills forms and OCRs scanned documents. The official Anthropic skill behind Claude's document features; source-available, not open source.

Self-improving agent from Nous Research with built-in memory and a learning loop that turns experience into skills. Works with any model provider; the hermes command has a terminal UI, and a gateway connects it to Telegram, Slack and more and runs scheduled tasks.

High-throughput LLM inference and serving engine: vllm serve starts an OpenAI- and Anthropic-compatible server in one command. Supports 200+ model architectures from Hugging Face, quantization and distributed inference. Aimed mainly at NVIDIA, AMD and other GPUs.

Generates natively editable PPTX from PDFs, Word files, web pages and other sources, with native shapes, data charts and master layouts. Can also fill your own template and add animations and narration to finished decks. Needs Python 3.10+ and its dependencies.

Ultra-lightweight, self-hosted personal AI agent framework in Python: WebUI, terminal and chat channels (Telegram, Discord, Slack, WeChat, Feishu, email), long-term memory, cron, MCP and multi-agent delegation; reads PDF and Office files. OpenAI-compatible or local models; Python 3.11+.

Open-source personal AI assistant and agent harness, formerly chatgpt-on-wechat: plans tasks, runs tools and skills, self-evolving memory and knowledge, multi-agent teams. Channels: WeChat, WeCom, Feishu, DingTalk, QQ, Telegram, Slack; any major model provider. One-line install and web console.

Adds a searchable, copyable text layer to scanned PDFs, with automatic deskew, rotation and size optimization, outputting PDF/A by default. Built on Tesseract, supports 100+ languages and runs fully locally.

Multi-language git hook manager: declare formatting, lint, secret scanning and other hooks in .pre-commit-config.yaml to run automatically before each commit, or have the agent self-check with pre-commit run --all-files before committing.

Bundles 60+ full-stack specialist skills into one plugin: experts for 12 languages including Python, TypeScript, Go, Rust and Java, and frameworks like React, Django and Spring Boot, activated automatically by request. Also includes Jira and Confluence workflow commands (needs Atlassian MCP).

Scans files and directories for personal data such as names, emails and credit card numbers using Presidio Analyzer, with configurable entity types, confidence thresholds and ignore rules. Needs Python 3.10+ and a spaCy language model.

Converts the output of 100+ commands such as dig, ps, ls and ifconfig, plus common file formats, into JSON so agents can parse it reliably with jq or scripts. Also usable as a Python library.

Lightweight OCR built on PaddleOCR models converted to ONNX, recognizing Chinese and English by default on CPU alone. The rapidocr CLI reads text from screenshots, photos and scanned page images. Needs Python; downloads models on first run, then works offline. Convert PDFs to images first.

Lets agents operate Home Assistant: control devices, query state and history, create and debug automations and edit dashboards, with 80+ tools. Community-maintained (unofficial); requires your own Home Assistant instance, and running via uvx needs a long-lived access token.

Language and toolset for double-entry bookkeeping in text files: bean-check validates the ledger, bean-format normalizes it, and Fava or beanquery handle reports and queries. Plain-text ledgers are easy for agents to generate and keep in version control.

Moves files to the trash instead of deleting them, with commands to list, restore and empty trashed files, so anything an agent deletes by mistake can be recovered. Follows the FreeDesktop.org trash spec and shares the trash with GNOME and KDE.

Converts PDFs to editable Word files, keeping paragraphs, tables, images and layout as closely as possible, and can extract tables alone, handy when a contract or exam only exists as PDF. Needs Python; digital PDFs only, OCR scans first. No longer actively maintained by Artifex.

Works with Gmail, Calendar, Drive, Docs, Sheets, Slides, Tasks, Chat and other Google Workspace services, with multi-user OAuth and read-only mode. You create your own Google OAuth client and run it in HTTP mode.

Searches Google Flights for flights and the cheapest travel dates, filtering by route, date, cabin, stops, airline and alliance, good for trip planning. Open-source Python, no API key needed; built on reverse-engineered Google endpoints, so changes at Google may break it.

Scans codebases and dependencies for open-source licenses, copyright notices and package manifests, detecting licenses by full-text matching, with output in JSON, SPDX, CycloneDX and more, for open-source compliance reviews.

From Free Law Project: finds bad redactions in PDFs, where a black box covers text that can still be selected and copied, and outputs the leaked text and positions per page as JSON. Useful for lawyers checking filings before release. Needs Python 3.10+; may miss cases, so review important files.

The Docling project's official MCP: converts PDF, Word, PowerPoint and more into structured documents with layout and table detection and OCR for scans, and lets agents build new documents paragraph by paragraph and export Markdown. Local mode needs Python and uv, or use your own Docling Serve.

Connects to Odoo ERP so agents can search, create, update and delete customers, products, invoices and other records in natural language, with aggregations and attachment reading. In production, install the companion MCP module in Odoo; for testing, YOLO mode connects directly.

Lets agents create, update and resolve incidents in ServiceNow, manage the service catalog, change requests and knowledge articles, and load only the tools each role needs. Needs a ServiceNow account and Python 3.11+ (from source); community-maintained, last updated Oct 2025.

Local MCP for paralegal work: analyzes clause risk in Word contracts, compares two contract versions, extracts parties, dates and other key terms, outlines pleadings, and optionally searches US case law via CourtListener. US-focused; needs local Python. A lawyer must review results.