139 boosters for "python" — open source, verified from GitHub, ready to install
The xlsx skill enables Claude to work with spreadsheet files—opening, editing, creating, cleaning, and converting .xlsx, .xlsm, .csv, and .tsv files. It's essential for users who need programmatic spreadsheet manipulation without leaving their AI assistant.
mcp-builder is a guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to integrate with external services and APIs. It helps developers build well-designed tool interfaces in Python or TypeScript for AI-powered applications.
AGENTS.md Version 2 enables AI agents to autonomously interact with websites using browser automation, allowing developers to automate complex web tasks programmatically. It's ideal for developers building AI-powered automation tools and agents that need reliable web interaction capabilities.
A system prompt that enables AI agents to automate browser tasks, navigate websites, and extract information by operating in an iterative loop. Developers and AI automation engineers use this to enhance AI capabilities for web automation across multiple platforms.
Cursor rules that provide AI coding agents with guidance for developing the Streamlit library itself (backend, frontend, protobufs), rather than building Streamlit applications.
Heuristic scoring (no AI key configured).
A PDF skill that guides AI assistants in reading, creating, and validating PDF files using Python tools like reportlab, pdfplumber, and Poppler rendering. Developers working with PDF generation, extraction, and layout validation will find this particularly useful.
Provides language-specific security best-practice reviews and improvement suggestions for Python, JavaScript/TypeScript, and Go code. Developers building secure applications benefit from automated security guidance tailored to their framework and language.
This booster equips AI coding assistants with specialized guidance for reading, creating, and editing Word documents programmatically while maintaining formatting and layout fidelity. Developers working with `.docx` files—especially those requiring professional formatting, tables, or visual validation—will find this booster invaluable.
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:
Gradio is a Python library for building interactive web UIs and ML demos. This skill covers the core API, patterns, and examples. Detailed guides on specific topics (read these when relevant): Creates a textarea for user to enter string input or display string output..
Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards. Use in your training scripts to log metrics: → See references/logging_metrics.md for setup, TRL integration, and configuration options.
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.
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 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.
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.
"name": "vizro-marketplace", "name": "Vizro Team", "email": "vizro@mckinsey.com"
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A deduplication skill that intelligently groups and selects canonical versions of duplicate events across multiple data sources using reputation scoring and hash-based matching. Ideal for developers building data aggregation systems, search engines, or multi-source content platforms.
A Cursor-integrated coding standard guide for the Dingo SDK that enforces PEP 8 conventions, type hints, and proper lazy-import patterns for optional dependencies. Developers building with or contributing to Dingo benefit from consistent, maintainable Python code patterns.
A CLI tool for interacting with Xiaohongshu (小红书). Use it to search notes, read details, browse user profiles, and perform interactions like liking, favoriting, and commenting. All commands require valid cookies to function. Authentication first uses saved local cookies. If unavailable, it auto-dete
Multi-pattern search/replace tool for bulk refactoring with simultaneous replacements, file/directory renaming, and case-preserving transformations. Then execute if output looks correct:
Sub-Agents is a lightweight framework for decomposing complex tasks into specialized child agents that collaborate under a parent orchestrator agent. Developers building multi-agent systems in Python will benefit from this pattern for creating modular, reusable agent hierarchies.
