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Found 1,220 Skills
Production Python coding standards with automatic version detection (3.10-3.13). Use when writing, reviewing, or refactoring Python to ensure adherence to modern type syntax, LBYL exception handling, pathlib operations, ABC-based interfaces, and production-tested patterns. Not Dagster-specific - applies to any Python project.
CCXT cryptocurrency exchange library for Python developers. Covers both REST API (standard) and WebSocket API (real-time). Helps install CCXT, connect to exchanges, fetch market data, place orders, stream live tickers/orderbooks, handle authentication, and manage errors in Python. Use when working with crypto exchanges in Python projects, trading bots, data analysis, or portfolio management. Supports both sync and async (asyncio) usage.
Guidance for implementing high-performance portfolio optimization using Python C extensions. This skill applies when tasks require optimizing financial computations (matrix operations, covariance calculations, portfolio risk metrics) by implementing C extensions for Python. Use when performance speedup requirements exist (e.g., 1.2x or greater) and the task involves numerical computations on large datasets (thousands of assets).
Upgrades Python pip/poetry/pipenv dependencies with breaking change handling
Guidelines for Python and Odoo enterprise application development with ORM, XML views, and module architecture best practices.
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.
Expert in Python testing with pytest and test-driven development
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
Meta-skill for pplx-sdk development. Orchestrates code review, testing, scaffolding, SSE streaming, and Python best practices into a unified workflow. Use for any development task on this project.
Source and evaluate candidates from LinkedIn using the linkedin_scraper Python library. Use when the user wants to (1) scrape LinkedIn profiles for candidate data, (2) evaluate candidates against a job description, (3) generate boolean search strings for sourcing, (4) produce candidate scorecards, summaries, or comparison tables, or (5) any recruiting/talent-sourcing task involving LinkedIn data.