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Found 2,039 Skills
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
Reddit API with PRAW (Python) and Snoowrap (Node.js)
Python asyncio - Modern concurrent programming with async/await, event loops, tasks, coroutines, primitives, aiohttp, and FastAPI async patterns
Validate test effectiveness with mutation testing using Stryker (TypeScript/JavaScript) and mutmut (Python). Find weak tests that pass despite code mutations. Use to improve test quality.
Complete syntax reference for Frappe Server Scripts. Use this skill when Claude needs to write Python code for Server Scripts in ERPNext/Frappe, including Document Events, API endpoints, Scheduler Events, and Permission Queries. Covers sandbox limitations, available frappe.* methods, event name mapping, and correct syntax for v14/v15/v16.
Comprehensive pre-merge validation checklist for Python/React pull requests. Use before approving or merging any PR. Covers code quality checks (linting, formatting, type checking), test coverage requirements, documentation updates, migration safety, API contract compatibility, accessibility compliance, bundle size impact, and deployment readiness. Provides a systematic checklist that ensures nothing is missed before merge. Does NOT cover security review depth (use code-review-security).
Upgrades Python pip/poetry/pipenv dependencies with breaking change handling
A fast, extensible progress bar for Python and CLI. Instantly makes your loops show a smart progress meter with ETA, iterations per second, and customizable statistics. Minimal overhead. Use for monitoring long-running loops, simulations, data processing, ML training, file downloads, I/O operations, command-line tools, pandas operations, parallel tasks, and nested progress bars.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
Python package for working with DICOM files. It allows you to read, modify, and write DICOM data in a Pythonic way. Essential for medical imaging processing, clinical data extraction, and AI in radiology.
Best practices for managing development environments including Python venv and conda. Always check environment status before installations and confirm with user before proceeding.
Use `uv` instead of pip/python/venv. Run scripts with `uv run script.py`, add deps with `uv add`, use inline script metadata for standalone scripts.