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Found 1,863 Skills
Guide for using Microsoft MarkItDown - a Python utility for converting files to Markdown. Use when converting PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs, Jupyter notebooks, RSS feeds, or Wikipedia pages to Markdown format. Also use for document processing pipelines, LLM preprocessing, or text extraction tasks.
Build applications with the Letta API — a model-agnostic, stateful API for building persistent agents with memory and long-term learning. Covers SDK patterns for Python and TypeScript. Includes 24 working code examples.
Configures Husky, lint-staged, commitlint, and Python pre-commit
Use when preparing branches, commits, or PRs for Python changes — scoping work, running validation gates, and ensuring merge readiness. Also use when debugging CI gate failures, resolving lockfile conflicts, or uncertain what checks to run before opening a PR.
Expert guidance for writing Python code using the official Google GenAI SDK (google-genai) for Gemini API and Vertex AI. Use for text generation, multimodal inputs, reasoning, tools, and media generation.
Test-driven development workflow enforcement for Python and React projects. Use when the user requests TDD, test-first development, or red-green-refactor methodology. Enforces strict cycle: write ONE failing test -> implement minimum code to pass -> refactor while green -> repeat. Applies to both backend (pytest) and frontend (Testing Library). Changes agent behavior to write tests before code. Does NOT provide testing patterns (use pytest-patterns or react-testing-patterns for how to write tests).
Guides building Docker images and composing containers for Python/FastAPI applications. Triggered when users ask to "create a Dockerfile", "dockerize a Python app", "optimize Docker image", "create docker-compose", "set up multi-stage build", "reduce Docker image size", "create development container", or "configure Docker for FastAPI". Covers Docker, Dockerfile, container, image build, docker-compose, and containerization best practices for production and development workflows.
Guidelines for Flask Python development with best practices for blueprints, RESTful APIs, and application factories.
Build LiveKit Agent backends in Python. Use this skill when creating voice AI agents, voice assistants, or any realtime AI application using LiveKit's Python Agents SDK (livekit-agents). Covers AgentSession, Agent class, function tools, STT/LLM/TTS models, turn detection, and multi-agent workflows.
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.
Generate Python FastAPI code following project design patterns. Use when creating models, schemas, repositories, services, controllers, database migrations, authentication, or tests. Enforces layered architecture, async patterns, OWASP security, and Alembic migration naming conventions (yyyymmdd_HHmm_feature).