349 boosters for "data" — open source, verified from GitHub, ready to install
1. Run the scaffold script: 2. Open and replace placeholders. 3. Generate or update the repo marketplace entry when the plugin should appear in Codex UI ordering:
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.
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.
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.
Methodology and best practices for account reconciliation, including GL-to-subledger, bank reconciliations, and intercompany. Covers reconciling item categorization, aging analysis, and escalation. Compare the general ledger control account balance to the detailed subledger balance. 1. Pull GL balan
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):
"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.
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
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
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
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.
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
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 .
"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"
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:
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 :
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.
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.
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.
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.
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.
