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Found 1,373 Skills
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
Use when designing data ownership, validation boundaries, consistency models, or configuration strategy in Python. Also use when encountering unclear ownership across modules, shared mutable state leaking between layers, validation gaps at ingress, cross-module transactional coupling, or config drift between environments.
Use when building or reviewing service, job, or CLI runtime behavior in Python — designing startup validation, shutdown sequences, observability, and structured logging. Also use when startup crashes from late config, shutdown leaves orphaned processes, terminal states are implicit, or logs lack structure.
Use when designing error handling, retry policies, timeout behavior, or failure classification in Python. Also use when code swallows exceptions, loses error context across boundaries, has unbounded retries, silent failures, or lacks idempotency guarantees on retried writes.
Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility strategies. Also use when hitting RuntimeError: This event loop is already running, asyncio.run() failures in cells, or tasks silently never completing.
Use when building or reviewing external API integrations in Python — designing client boundaries, defining outbound reliability policy, or structuring contract tests. Also use when provider SDK details leak into domain logic, outbound calls lack timeout/retry policy, or failure paths are untested.
Use when designing module boundaries, planning refactors, or reviewing architecture in Python codebases. Also use when facing tangled dependencies, god classes, deep inheritance hierarchies, unclear ownership, or risky structural changes.
Python skill router. Use when planning, implementing, or reviewing Python changes and you need to select focused skills for workflow, design, typing/contracts, reliability, testing, data/state, concurrency, integrations, runtime operations, or notebook async behavior.
Use when writing or reviewing tests for Python behavior, contracts, async lifecycles, or reliability paths. Also use when tests are flaky, coupled to implementation details, missing regression coverage, slow to run, or when unclear what tests a change needs.
Use when defining or evolving public interfaces, schema boundaries, or pydantic usage in Python. Also use when annotations are missing on public APIs, pydantic models appear everywhere instead of at trust boundaries, contract changes lack migration guidance, or Any/object types are overused across module boundaries.
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.
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.