290 boosters for "data" — open source, verified from GitHub, ready to install
A skill for building and scaffolding ChatGPT Apps SDK applications that combine MCP servers with widget UIs, using a docs-first workflow. Developers building ChatGPT extensions and integrations benefit from structured guidance on tool design, UI registration, and SDK compliance.
Sentry booster enables developers to inspect production errors, summarize recent issues, and pull health data from Sentry via read-only API queries. Ideal for on-call engineers and DevOps teams needing quick observability access without leaving their coding environment.
Analyzes git repositories to map security ownership, identify bus factors, and detect orphaned sensitive code, exporting results for graph visualization. Essential for security teams and DevOps engineers managing code risk and maintainer dependencies.
Automates real browser interactions from the terminal using Playwright CLI for tasks like navigation, form filling, and data extraction. Useful for developers and AI assistants building UI automation workflows without writing test frameworks.
"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.",
A Cursor rules prompt that standardizes the DataHub development workflow by directing developers to use a centralized shell script (datahub-dev.sh) for all build, test, and flag operations. This benefits DataHub contributors by reducing setup friction and ensuring consistent development practices across the team.
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
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:
"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"
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
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
Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction. 1. Optionally validate dataset availability with . 2. Resolve + with .
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 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 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 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 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.
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.
