367 boosters for "llm" — open source, verified from GitHub, ready to install
"description": "Harness-native ECC plugin for engineering teams - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses", "name": "Affaan Mustafa", "url": "https://x.com/affaanmustafa"
  
"name": "everything-claude-code", "version": "1.10.0", "description": "Battle-tested Claude Code plugin for engineering teams — 38 agents, 156 skills, 72 legacy command shims, production-ready hooks, and selective install workflows evolved through continuous real-world use",
  
This skill guides developers in building LLM-powered applications using Claude and Anthropic SDKs, with smart triggering based on API imports and user intent. Ideal for developers looking to integrate Claude into their projects with language-specific best practices.
mcp-builder is a guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to integrate with external services and APIs. It helps developers build well-designed tool interfaces in Python or TypeScript for AI-powered applications.
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation. Scan the target file (or, if no target file, the prompt and project) for non-Anthropic provider markers
AGENTS.md Version 2 enables AI agents to autonomously interact with websites using browser automation, allowing developers to automate complex web tasks programmatically. It's ideal for developers building AI-powered automation tools and agents that need reliable web interaction capabilities.
A system prompt that enables AI agents to automate browser tasks, navigate websites, and extract information by operating in an iterative loop. Developers and AI automation engineers use this to enhance AI capabilities for web automation across multiple platforms.
This booster enables Claude Code to process, convert, and extract data from documents (PDF, DOCX, XLSX, PPTX, HTML, images) using the Nutrient DWS API, including OCR, editing, signing, and form-filling capabilities. Developers building document automation workflows benefit from seamless integration with multiple file formats.
"name": "mempalace", "description": "Give your AI a memory — mine projects and conversations into a searchable palace. 19 MCP tools, auto-save hooks, and guided setup.", "name": "milla-jovovich"
An MCP server that integrates Context7 with Claude, enabling developers to leverage context management and vibe-coding capabilities within Claude Desktop and Claude Code environments.
"description": "Lazy senior dev mode. Forces the simplest, shortest solution that actually works: YAGNI, stdlib first, no unrequested abstractions.", "name": "Dietrich Gebert", "url": "https://github.com/DietrichGebert"
"description": "Ultra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.", "name": "Julius Brussee", "url": "https://github.com/JuliusBrussee"
Oh My Opencode is an AI agent harness that provides multi-model orchestration, parallel background agents, and advanced code analysis tools for Claude Desktop and Claude Code. It benefits developers building sophisticated AI-powered applications who need orchestrated agent coordination and deep code understanding capabilities.
"name": "@mastra/mcp-docs-server", "version": "1.1.25-alpha.4", "description": "MCP server for accessing Mastra.ai documentation, changelogs, and news.",
Heuristic scoring (no AI key configured).
Promptfoo is an LLM evaluation and testing toolkit that helps developers systematically test, benchmark, and validate prompt performance across different models and scenarios. It's essential for teams building LLM applications who need rigorous quality assurance and prompt optimization.
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
Finds the best models for a task by querying official HF benchmark leaderboards, enriching results with model size data, filtering for what fits on the user's device, and returning a comparison table with benchmark scores.
This skill is for running evaluations against models on the Hugging Face Hub on local hardware. It does not cover: If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the skill and pass it one of the local scripts in this skill.
Fast, calibration-free weight quantization supporting 8/4/3/2/1-bit precision with multiple optimized backends. HQQ uses to define quantization parameters: The core quantized layer that replaces :
"name": "understand-anything", "description": "AI-powered codebase understanding — analyze, visualize, and explain any project", "homepage": "https://github.com/Lum1104/Understand-Anything",
This skill automates the process of adding, extracting, and managing evaluation results in Hugging Face model cards, supporting multiple data sources including Artificial Analysis API and custom evaluations with vLLM/lighteval. It's valuable for ML practitioners and model maintainers who need to track and display model performance metrics.
