927 boosters — open source, verified from GitHub, ready to install
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.
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Use this skill when users want to: When assisting with jobs:
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.
Trackio is an ML experiment tracking library that integrates with Hugging Face to log metrics, visualize training progress, and trigger alerts during model development. It's useful for ML engineers and researchers who need real-time monitoring and experiment management.
A skill that generates reusable command-line scripts for automating Hugging Face API interactions, useful for developers who need to repeatedly fetch, process, or chain API calls.
A skill for querying and exploring Hugging Face datasets through the Dataset Viewer API, enabling developers to fetch metadata, paginate rows, search, filter, and download parquet files. Useful for data scientists and engineers working with public datasets in their AI/ML workflows.
This skill enables users to run Python workloads, Docker jobs, and GPU-intensive tasks on Hugging Face's managed infrastructure without local setup. It's valuable for ML engineers, data scientists, and developers needing cloud compute for training, inference, and batch processing.
This skill enables AI assistants to create, configure, and manage datasets on Hugging Face Hub with SQL-based querying and transformation capabilities. It's valuable for developers building data workflows and ML projects that require programmatic dataset management.
A skill booster for building interactive web UIs and ML demos using Gradio in Python. Developers creating data applications, ML interfaces, and chatbots benefit from guided assistance with Gradio components and patterns.
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.
Generates per-category catalog JSON files from the npm package's public API. Each catalog lists all UseCase and EventHandler abstractions in a category with their resolved source file paths. LLMs read source files on demand for exact, up-to-date types — no enrichment phase needed. 1. Discovers all
A CLI tool for managing and installing reusable skills across multiple AI coding agents (Claude Code, Cursor, etc.), enabling developers to extend agent capabilities with pre-built tools and workflows from repositories like GitHub and GitLab.
Automates GitHub pull request analysis by gathering diffs, comments, related issues, and local code context to provide comprehensive reviews. Developers and code reviewers benefit from faster, more thorough PR evaluations.
Do NOT check or review pull requests. Do NOT call commands. Run CodeRabbit locally against the working repository only. From the output, extract for each finding:
A Bash CLI wrapper for the EODHD financial data API designed for OpenClaw agents, with built-in secrets management and secure deployment practices. Useful for developers building open-source integrations that need stateless API access without credential exposure.
Filter by smart money category with :
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:
Local GraphRAG knowledge base backed by SQLite + MNN embeddings. Fully compatible with Android OfflineAI RAG database format. On first use, (~400 MB) is auto-downloaded into .
name: openclaw-self-healing-elvatis description: OpenClaw plugin that applies guardrails and auto-fixes reversible failures (rate limits, disconnects, stuck session pins). Self-healing extension for OpenClaw.
A PESTEL analysis skill that systematically evaluates macro-environmental factors (political, economic, social, technological, environmental, legal) impacting product strategy and roadmaps. Ideal for product managers, strategists, and AI agents needing to assess external market risks and opportunities.
Comprehensive quality audit system for Claude Code agents, skills, and commands. Provides quantitative scoring, comparative analysis, and production readiness grading based on industry best practices. The 16-criteria framework is derived from: 1. Claude Code Best Practices (Ultimate Guide line 4921:
Real-time monocular depth estimation using Depth Anything v2. Transforms camera feeds with colorized depth maps — near objects appear warm, far objects appear cool. When used for privacy mode, the blend mode fully anonymizes the scene while preserving spatial layout and activity, enabling security
