1,753 boosters for "AI" — open source, verified from GitHub, ready to install
You are a Dropbox specialist for the user's connected Dropbox account. Surface the tool's , , , and the you passed inside when the tool returned them. Never invent a field the tool did not return. Return only one JSON object (no markdown or prose outside it):
Fscan 是一款内网综合扫描工具，功能包括： 二进制路径：当前项目编译产物 ，或系统 PATH 中的 。 每行一个 JSON 对象，所有字段定义：
"name": "figma-developer-mcp", "mcpName": "io.github.GLips/Figma-Context-MCP", "description": "Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.",
Polish is a final quality assurance skill that refines UI/UX details like alignment, spacing, and consistency before deployment. It's useful for developers and designers who want to elevate finished features from good to production-ready.
A design refinement skill that tones down overly bold or aggressive visual elements while preserving design impact, useful for designers and developers seeking more subtle, approachable aesthetics.
An audit skill that systematically evaluates interface quality across accessibility, performance, theming, and responsive design, generating prioritized reports with severity ratings and actionable recommendations. Ideal for developers and QA teams seeking to identify and document UI/UX issues before fixes are implemented.
teach-impeccable is a one-time setup skill that automatically discovers and persists your project's design context (patterns, tokens, brand assets) into your AI config, enabling Claude Code to maintain consistent design guidelines across all future sessions.
A skill that helps developers create distinctive, production-grade frontend interfaces with polished aesthetics and creative design choices, avoiding generic AI-generated outputs. Ideal for developers building web components, pages, and applications who want professional design quality.
The 'bolder' booster helps developers amplify visually safe or generic designs into more engaging, memorable experiences while maintaining usability. Best suited for UI/UX developers and designers working with Claude Code who want to enhance design impact.
"name": "baoyu-skills", "name": "Jim Liu (宝玉)", "email": "junminliu@gmail.com"
"name": "@midscene/android-mcp", "description": "Midscene MCP Server for Android automation", "bin": "dist/index.js",
Deep Agents is a system prompt for building intelligent agent assistants with planning, filesystem, and sub-agent capabilities across Claude and other AI platforms. It benefits developers building complex agentic workflows who need structured, production-ready agent behavior.
This is a Bootstrap 5 admin dashboard template built with CoreUI components. It uses Pug templating, Sass for styles, and vanilla JavaScript for interactivity. 1. Create Pug template in 2. Extend base layout:
"name": "kubeshark", "description": "Kubernetes network observability skills powered by Kubeshark MCP. Root cause analysis, traffic filtering, snapshot forensics, PCAP extraction, and more.", "name": "Kubeshark",
This file explains the Knowledge Base feature and how it's implemented. The knowledge base helps users store and manage information that can be used to help draft responses to emails. It acts as a personal database of information that can be referenced when composing replies. Users can create, edit,
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 :
Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the command line tool. Model and Dataset cards can be accessed from repos
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.
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
Provides the Hugging Face Hub CLI (`hf`) tool for downloading, uploading, and managing models, datasets, and Spaces directly from Claude Code. Essential for developers integrating Hugging Face resources into AI workflows.
Build and publish a Gradio demo on Hugging Face Spaces that runs inference with a user-provided LoRA. Use whenever someone asks to create, generate, ship, or publish "a Space", "a demo", "a Gradio app", or "a playground" for a LoRA — whether the base model is Qwen-Image, Qwen-Image-Edit, LTX, or ano
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
Search the Hugging Face Hub for llama.cpp-compatible GGUF repos, choose the right quant, and launch the model with or . 1. Search the Hub with . 3. Prefer the exact HF local-app snippet and quant recommendation when it is visible.
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.
