Find the perfect capability for your agent.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Build production AI agents with the Claude Agent SDK. Use when working with: (1) Creating autonomous agents with query() or ClaudeSDKClient, (2) Defining custom tools via in-process MCP servers, (3) Implementing hooks for tool interception and permissions, (4) Building multi-agent systems with subagents, (5) Managing sessions and state across queries, (6) Integrating MCP servers for external capabilities. Covers Python and TypeScript SDKs with production patterns.
State-aware cognitive reasoning engine combining pattern detection, critical questioning, ontology validation, and gap analysis through InfraNodus integration. Uses programmatic Python modules for algorithmic precision with Claude interpreting results contextually. Handles writing analysis, cognitive diagnosis, ontology generation, and strategic content development.
Build and deploy Apify actors for web scraping and automation. Use for serverless scraping, data extraction, browser automation, and API integrations with Python.
Knowledge management for AI-assisted development. Use this skill when: - Starting a new session or resuming work - User asks about tasks, bugs, notes, or project status - User mentions: "what's next", "where did we leave off", "TODO", "track this" - Creating, updating, or closing tasks - Capturing notes, ideas, or research findings - Checking project vision or plans - User asks about project context or goals - Coordinating with other AI agents on the same codebase - User mentions: "save this", "remember", "note", "task", "bug", "idea" - User wants to explore or document a Python API - User mentions: "docs", "documentation", "API", "explore package"
Expert guidance for integrating Ollama local AI models with Python applications, including installation, configuration, model management, and production deployment
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Mise configuration and runtime management expert. Use when: - Installing programming language runtimes (node, python, go, rust, etc.) - Configuring tool versions globally or per-project - Questions about mise.toml, global config, or shims - Installing CLI tools (especially AI CLI tools like gemini, codex, opencode) - Troubleshooting "command not found" for mise-managed tools - Comparing mise to nvm, pyenv, rbenv, goenv - Setting up development environments Do NOT use for: - General coding questions unrelated to tooling - Questions about specific programming languages (use language-specific agents)
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts. Triggers: "python", "python development", "fastapi", "django", "async python", "python best practices", "python project setup".
Use this agent when you need to design, implement, or optimize FastAPI backend applications. This includes API endpoint creation, database integration, authentication/authorization implementation, cloud deployment strategies, business logic architecture, performance optimization, and following FastAPI best practices.
Expert guidelines for Python ML and LLM workflows. Covers code quality, experiment tracking, and data handling. Use when working on AI/ML components or data pipelines.
WHEN: Machine Learning/Deep Learning code review, PyTorch/TensorFlow patterns, Model training optimization, MLOps checks WHAT: Model architecture review + Training patterns + Data pipeline checks + GPU optimization + Experiment tracking WHEN NOT: Data analysis only → python-data-reviewer, General Python → python-reviewer
Python best practices with modern frameworks, AI/ML integration, and performance optimization for 2025
Follow these patterns when extending the OptAIC Python SDK with new domain operations. Use for adding client methods for datasets, signals, portfolios, backtests, and other resources. Covers async/sync interfaces, uploads, and long-running operations.
Use when creating database migrations. Prevents data loss, downtime, and performance issues. Supports PostgreSQL, MySQL, SQLite. Python 3.8+
Instructions for updating the Python backend and leaderboard database.
Neo4j driver best practices for Python. Use when working with Neo4j connections, Cypher queries, transactions, or GraphRAG implementations.
Design and implement production-grade database layers using SQLModel with SQLAlchemy 2.0 patterns. This skill should be used when users need to create database models, configure engines/sessions, design schemas, implement relationships, or optimize database performance in Python FastAPI projects.
PostgreSQL development for Python with full-text search (tsvector, tsquery, BM25 via pg_search), vector similarity (pgvector with HNSW/IVFFlat), JSONB and array indexing, and production deployment. Use when creating search features, storing AI embeddings, querying vector similarity, optimizing PostgreSQL indexes, or deploying to AWS RDS/Aurora, GCP Cloud SQL/AlloyDB, or Azure. Covers psycopg2, psycopg3, asyncpg, SQLAlchemy integration, Docker development setup, and index selection strategies. Triggers are Use "PostgreSQL search", "pgvector", "BM25 postgres", "JSONB index", "psycopg", "asyncpg", "PostgreSQL Docker", "AlloyDB vector". Does NOT cover - DBA administration (backup, replication, users), MySQL/MongoDB/Redis, schema design theory, stored procedures.
SQLAlchemy Python SQL toolkit and ORM. Use for database models, queries, sessions, relationships, migrations, async database operations, and working with PostgreSQL, MySQL, SQLite, Oracle, SQL Server.
