LLM & AI Agent Skills

A tool similar to cloc, sloccount and tokei. For counting the lines of code, blank lines, comment lines, and physical lines of source code in many programming languages. Goal is to be the fastest code counter possible, but also perform COCOMO calculation like sloccount, LOCOMO estimation for LLM-based development costs, estimate code complexity similar to cyclomatic complexity calculators and prod

Screenpipe brand style guide. Reference this when designing UI components, writing copy, or making visual decisions.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

MCP Server for the Notion API, enabling LLM to interact with Notion workspaces. Additionally, it employs Markdown conversion to reduce context size when communicating with LLMs, optimizing token usage and making interactions more efficient. Here is a detailed explanation of the steps mentioned above in the following articles: - English Version: https://dev.to/suekou/operating-notion-via-claude-des

The server uses the MCP protocol to share access to a local Home Assistant instance with an LLM application. A powerful bridge between your Home Assistant instance and Language Learning Models (LLMs), enabling natural language control and monitoring of your smart home devices through the Model Context Protocol (MCP). This server provides a comprehensive API for managing your entire Home Assistant

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.

Control Android devices with AI through the Model Context Protocol DroidMind is a powerful bridge between AI assistants and Android devices, enabling control, debugging, and system analysis through natural language. By implementing the Model Context Protocol (MCP), DroidMind allows AI models to directly interact with Android devices via ADB in a secure, structured way. When used as part of an agen

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa

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Apple Health MCP Server Apple Health Data Exploration Connect your Apple Health data with any LLM that supports MCP. Talk to your data and get personalised insights. This demo shows how Claude uses the apple-health-mcp-server to answer questions about your data. Example prompts from the demo: - I would like you to help me analyze my Apple Health data. Let's start by analyzing the data types - chec

🚀 1:1 Optimized Rust Implementation of WCGW (What Could Go Wrong) 🚀 Winx is a specialized Model Context Protocol (MCP) server that provides high-performance tools for LLM code agents. It implements the core functionality of WCGW in pure Rust for maximum efficiency and stability. Benchmarks on i9-13900K + RTX 4090 (WSL2) - Rust 1.75+ - Linux / macOS / WSL2 Add to ~/.config/Claude/claudedesktopcon

Transcribe MCP Automate your transcriptions with AI. Transcribe MCP instantly connects your account to assistants like Claude, Windsurf, Cursor, and more so they can automate tasks on your behalf. The Local Server can add local files for transcription and return result to your Assistant in seconds. - ⚡ Fast, lightweight and LLM-friendly. No special ASR models needed, no setup and fighting python p

An AI coding assistant built in Rust that provides both command-line and graphical interfaces for autonomous code analysis and modification. Multi-Modal Tool Execution: Adapts to different LLM capabilities with pluggable tool invocation modes - native function calling, XML-style tags, and triple-caret blocks - ensuring compatibility across various AI providers. Real-Time Streaming Interface: Advan

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

An MCP (Model Context Protocol) server that provides unified access to multiple Large Language Model APIs for AI coding environments like Cursor and Claude Desktop. - 🌐 8 LLM Providers – ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral - 🎯 Smart Model Selection – Tag-based preferences (coding, business, reasoning, math, creative, general) - 📊 Prompt Logging – Track all prompts

Model Context Protocol (MCP) Server for Langfuse Prompt Management. This server allows you to access and manage your Langfuse prompts through the Model Context Protocol. Quick demo of Langfuse Prompts MCP in Claude Desktop (unmute for voice-over explanations): This server implements the MCP Prompts specification for prompt discovery and retrieval. - prompts/list: List all available prompts - Optio

This project provides Model Context Protocol (MCP) for the Mifos X Ecosystem, enabling AI agents to access financial data and operations. Implementations is available in Java (Quarkus). Use the MCP Inspector to test and debug your server: This starts a local web UI to connect to your MCP server via STDIO or SSE. Prerequisites: JDK 21+, Maven 1. Configure environment variables in your shell or IDE:

An MCP server that provides safe access to your iMessage database through Model Context Protocol (MCP). This server is built with the FastMCP framework and the imessagedb library, enabling LLMs to query and analyze iMessage conversations with proper phone number validation and attachment handling. - macOS (required for iMessage database access) - Python 3.6+ Install all required dependencies: - fa

Overview • Quick Start • Supported Data Providers • FinData is an open-source Model Context Protocol(MCP) Server that provides professional financial data access capabilities for LLM. It supports various data providers such as Tushare, Wind, DataYes, etc. This enables AI applications to quickly retrieve financial data. Fully supports both Stdio and SSE transports, offering flexibility for differen

mcp-ragchat An MCP server that adds RAG-powered AI chat to any website. One command from Claude Code. Tell Claude Code "add AI chat to mysite.com" and it will crawl your content, build a local vector store, spin up a chat server, and hand you an embed snippet. No cloud infra. No database. Just one API key. 1. Clone and build 2. Configure Claude Code (~/.claude/mcp.json) Open Claude Code and say: C

MCP server for the Urlbox Screenshot API. Enables your client to take screenshots, generate PDFs, extract HTML/markdown, and more from websites. Visit Urlbox for more information, and have a read of our docs or chat with your LLM post install to get a good understanding of its options and capabilities. 1. Install dependencies and build: 2. Get Urlbox API credentials: - Sign up at urlbox.com - Get

MCP server that fetches YouTube video transcripts and optionally summarizes them. - Fetch transcripts in multiple formats (text, JSON, SRT, WebVTT, pretty-print) - Summarize videos — returns transcript with instructions for the LLM to produce a summary - List available languages for any video's transcripts - Flexible URL parsing — accepts full YouTube URLs (youtube.com/watch?v=, youtu.be/, youtube

CSL-Core (Chimera Specification Language) is a deterministic safety layer for AI agents. Write rules in .csl files, verify them mathematically with Z3, enforce them at runtime — outside the model. The LLM never sees the rules. It simply cannot violate them. Originally built for Project Chimera, now open-source for any AI system. This doesn't work. LLMs can be prompt-injected, rules are probabilist

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browse