440 boosters for "mod" — open source, verified from GitHub, ready to install
Generates repository-grounded threat models that identify trust boundaries, assets, attack paths, and mitigations in application code. Ideal for security engineers and developers performing AppSec threat modeling on specific codebases.
A skill for bootstrapping, developing, and designing modern WinUI 3 desktop applications with C# using Microsoft's official guidance and patterns. Useful for Windows developers building new apps or troubleshooting WinUI-related setup and implementation challenges.
Provides developers with authoritative, up-to-date OpenAI API documentation and guidance by prioritizing official MCP tools for questions about building with OpenAI products. Ideal for developers integrating OpenAI APIs who need reliable, cited references.
Build professional-grade design systems in Figma that match code. This skill orchestrates multi-phase workflows across 20–100+ calls, enforcing quality patterns from real-world design systems (Material 3, Polaris, Figma UI3, Simple DS). Every design system build follows this phase order. Skipping o
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 :
This skill provides comprehensive tools for AI engineers and researchers to publish, manage, and link research papers on the Hugging Face Hub. It streamlines the workflow from paper creation to publication, including integration with arXiv, model/dataset linking, and authorship management. The inclu
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
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
"name": "huggingface-skills", "description": "Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub", "name": "Hugging Face"
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.
Hugging Face Paper pages (hf.co/papers) is a platform built on top of arXiv (arxiv.org), specifically for research papers in the field of artificial intelligence (AI) and computer science. Hugging Face users can submit their paper at hf.co/papers/submit, which features it on the Daily Papers feed (h
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.
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.
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
A skill that enables researchers and AI engineers to publish, manage, and link research papers on Hugging Face Hub with arXiv integration and professional markdown generation. Useful for academics and ML practitioners looking to streamline paper publication workflows.
Enables developers to interact with Hugging Face Hub directly from Claude Code using the `hf` CLI—downloading models/datasets, uploading files, creating repositories, and managing cache without leaving the coding environment.
"name": "context-mode", "version": "1.0.75", "description": "MCP server that saves 98% of your context window with session continuity. Sandboxed code execution in 11 languages, FTS5 knowledge base with BM25 ranking, and automatic state restore across compactions.",
Context Mode is an MCP server that dramatically reduces Claude's context window consumption through sandboxed code execution and intelligent full-text search, enabling developers to work with larger codebases and knowledge bases without hitting token limits.
Heuristic scoring (no AI key configured).
"name": "xcodebuildmcp", "mcpName": "com.xcodebuildmcp/XcodeBuildMCP", "iOSTemplateVersion": "v1.0.8",
"name": "spec-workflow-mcp", "description": "MCP server for structured spec-driven development with real-time web dashboard and VSCode extension.", "homepage": "https://github.com/Pimzino/spec-workflow-mcp",
Yielding Bear provides a single unified API that routes every LLM request to the cheapest capable model across 16+ providers — saving 60-80% vs calling OpenAI, Anthropic, or Google directly. 1. Get an API key at https://yieldingbear.com/api 2. Set environment variable:
