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Found 1,378 Skills
Decompose high-level objectives into atomic implementation tasks for Python/React projects. Use when breaking down large features, multi-file changes, or tasks requiring more than 3 steps. Produces independently-verifiable tasks with done-conditions, file paths, complexity estimates, and explicit ordering. Creates persistent task files (task_plan.md, progress.md) to track state across context windows. Does NOT cover high-level planning (use project-planner) or architecture decisions (use system-architecture).
Expert guidance for integrating ViewComfy API into web applications using Python and FastAPI
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
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.
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.
Flask - Lightweight Python web framework for microservices, REST APIs, and flexible web applications with extensive extension ecosystem
Debugging techniques for Python, JavaScript, and distributed systems. Activate for troubleshooting, error analysis, log investigation, and performance debugging. Includes extended thinking integration for complex debugging scenarios.
Build Python-native web apps with Mesop. Triggers when users want to build, debug, or deploy Mesop applications, including AI chat interfaces, internal tools, and ML demos.