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Found 2,038 Skills
Use when designing or reviewing concurrent Python code — selecting between asyncio, threads, or multiprocessing; structuring cancellation and deadline propagation; bounding fan-out and backpressure. Also use when diagnosing race conditions, deadlocks, slow throughput, or thread/task leaks under load.
Python code quality with ruff (linting & formatting) and mypy (type checking). Covers pyproject.toml configuration, pre-commit hooks, and type hints. Use when user mentions ruff, mypy, linting, formatting, type checking, code style, or Python code quality.
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.
Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
Setup Sentry in Python apps. Use when asked to add Sentry to Python, install sentry-sdk, or configure error monitoring for Python applications, Django, Flask, FastAPI.
Quick reference mapping global architecture concepts to Python/FastAPI/SQLAlchemy syntax. For concepts, see the global skills.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
This skill should be used when the user asks to "query BigQuery with Python", "use the google-cloud-bigquery SDK", "load data into BigQuery", "define a BigQuery schema", or needs guidance on best practices for the Python BigQuery client library.
This skill should be used when the user asks to "scan Python code for security issues", "set up Bandit", "configure bandit security linting", "fix bandit warnings", or needs guidance on Python static security analysis with Bandit.
Complete reference for the Galileo AI platform Python SDK for evaluating, observing, and protecting GenAI applications. Use when building Python applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.
Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring AI-generated Python code (deslopification), (6) Designing API patterns, or (7) Optimizing backend performance.