A Python framework for modular, self-contained skill management for machines.

MCP server for token-efficient large document analysis via the use of REPL state

Implementation of Podlite markup language

Generate a compact codebase index for AI assistants — saves 50K+ tokens per conversation

Writing is thinking! WIT provides skills for scientific thinking and writing!

Full computer-use for AI agents. Self-learning workflows. Native macOS. No screenshots required.

⚡️ A curated library of Agent Skills focused on Jetpack Compose performance.

ISO 24495 Plain Language skills and Claude Code plugin

Model Context Protocol (MCP) Server for Graphlit Platform

Agent Skills and lightweight tools for clearer, more natural Vietnamese writing, translation cleanup, grammar checks, and style consistency.

🔥 Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.

Academic research skills suite for AI Agent — literature search/download (WoS+Elsevier+Springer), PDF extraction, figure cropping, review writing, Zotero sync, and PPT/Html generation.

Pair Claude Desktop on Anthropic with Claude Code routed through Ollama. Visual walkthrough + copy-paste prompt that cuts your Claude Code bill ~90%.

Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

The OKF toolkit for Claude Code — author, maintain, validate & visualize Open Knowledge Format bundles. Plugin, agent skills, and a GitHub Action.

An agentic memory database that cuts session tokens by 82–99%. One portable SQLite file — your agent's memory, anywhere.

OKF (Open Knowledge Format): Durable, structured memory for AI agents. Author, validate, consume, and maintain portable knowledge bundles through an ecosystem of Skills, MCP, an interactive graph, TUI, CLI, Docker, and a Claude Code plugin. 100% local.

Own your LLM's web search: a local search->fetch->rank pipeline that replaces hosted web-search tools. Measured: matches hosted accuracy at 66% lower cost and up to 88% fewer tokens, plus a precision-tuned semantic caching with query-dependant TTL that no API offers.