Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or…
Provides Python patterns for type-first development with dataclasses, discriminated unions, NewType, and Protocol. Must use when reading or writing Python files.
Use when when you have mzPeak files (Parquet-based archives in uncompressed ZIP containers) or other PyArrow-compatible columnar formats containing mass spectrometry spectra, and…
Use when when you have a C++ library (such as OpenMS) with nanobind binding specifications in a designated bindings directory and need to create a Python module that exposes C++…
PyO3 Python bindings for Kailash Rust — maturin, .pyi stubs, Value mapping, register_callback.
Python 生信编程工具 skill。用于 Biopython、pysam、cyvcf2、scikit-bio、Bio.PDB、Entrez、BLAST 解析、FASTA/GenBank/PDB/BAM/VCF 编程处理和自定义 Python 生信脚本设计。
Investigate and propose fixes for Python canary cron failures in the openinference repo. Use when the user mentions Python canary failures, Python cron failures, or when the…
Design and maintain Python CI workflows around uv, pytest, Ruff, mypy, package build checks, dependency caching, Python version matrices, and local-command parity.
Use when when you need to enable bracket notation (e.g., handler[5]) for random access to blocks within a large compressed or remote data source, or when you want to support both…
Remove dead code, fix PEP 8 violations, add type hints, consolidate duplicates, and modernize Python codebases. Use when cleaning up Python projects.
Complete Python Cloudflare deployment system. PROACTIVELY activate for: (1) Python Workers with Pyodide, (2) FastAPI on Workers, (3) Cloudflare Containers for heavy compu — from…
Complete Python Cloudflare deployment system. PROACTIVELY activate for: (1) Python Workers with Pyodide, (2) FastAPI on Workers, (3) Cloudflare Containers for heavy compu — from…
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase.
Reviews Python code for type safety, async patterns, error handling, and common mistakes. Use when reviewing .py files, checking type hints, async/await usage, or exception…
Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or…
Modern Python 3.12+ development with uv, ruff, and production-ready practices. Routes to specialized skills for frameworks and testing.
Choose threads, processes, or asyncio from workload shape, and use concurrent.futures without deadlocks.
Use when designing or reviewing concurrent Python code — selecting between asyncio, threads, or multiprocessing; structuring cancellation and deadline propagation; bounding…
Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing…
Apply Python project conventions — uv for deps and builds, Ruff strict (E, F, I, UP, B, SIM, PTH, PIE, RUF, T201, PLC0415), mypy strict, pytest with pytest-cov and pytest-asyncio,…
Python excellence specialist for type safety, async patterns, packaging, and idiomatic production codeUse when "python development, type hints, async python, pydantic models,…
Best practices for multi-step Python tasks including data analysis, HuggingFace datasets, token counting, and any task requiring state across multiple python() calls.
Data Architecture, Engineering, Science, Analytics, Visualization, และ Governance
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Use when designing data ownership, validation boundaries, consistency models, or configuration strategy in Python.
Use when after executing a spatial analysis function (e.g., squidpy.gr.sepal) that modifies or augments a data object, verify that the expected output fields exist with correct…
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work — from…
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work — from…
Choose between dataclass, NamedTuple, and pydantic, and configure frozen, slots, and validation correctly.
Python アプリケーションのドメインモデル / DTO / API スキーマを Pydantic v2 で設計するとき。BaseModel、field_validator / model_validator、computed_field、model_config (frozen,…
Detect and remove unused Python code using vulture and ruff. Covers unused imports, variables, functions, classes, and unreachable code.
Debug Python scripts with proper error surfacing and working directory verification
Debug Python script execution failures by capturing full tracebacks and verifying working directory
Debug Python script execution failures by capturing full output with exit codes and verifying working directory before file operations
Systematically debug Python code using structured error analysis, logging inspection, and test-driven fixes
Master debugger for Python code with expertise in common errors, performance issues, and debugging tools
Use when debugging Python via pdb or remote debugpy (DAP). Triggers: pdb, debugpy, remote debug python.
负责《Paddle API 对齐 PyTorch 项目》中 Step2：API 代码修改，实施『Python 装饰器』方案。通过 Python 装饰器，在 Python 层为 Paddle API 实现参数名称、参数顺序、参数类型和参数用法的重载，实现 PyTorch 风格的 API 调用，并保持 Paddle API 的向后兼容性。
Write decorators that preserve signatures and compose cleanly, including parameterized and class-applied forms.
Resolves and visualizes Python package dependency graphs using pip, pipdeptree, and the PyPI JSON API.
Use when when setting up a multi-stage bioinformatics workflow (e.g., ENPKG) that calls external tools (MZmine, Sirius, SPARQL engines) and depends on specific Python libraries;…
Use when when setting up a new conda environment for a Python-based bioinformatics pipeline and you need to confirm that all declared dependencies (e.g., pysam >=0.15.4, bx-python…
Mide qué parte del árbol de dependencias Python de un repo es REALMENTE auditable por un scanner de vulnerabilidades, y nombra las que quedan fuera.
Use when designing module boundaries, planning refactors, or reviewing architecture in Python codebases.
Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance.
Opinionated Python development setup with uv + ty + ruff + pytest + just. Use when creating new Python projects, setting up pyproject.toml, configuring linting, type checking,…
Implement or fix small-to-medium Python features without full SDD (FastAPI/Flask, pytest). Use for isolated Python work or when invoking /python-developer.
You are a Python project architecture expert specializing in scaffolding production-ready Python applications.
**UTILITY SKILL** — Python diagram generation: WAF/cost/compliance charts (matplotlib), architecture diagrams (diagrams lib), ERDs, swimlanes, timelines, wireframes (graphviz).
Use when creating or modifying Python files in the src/backend/ directory — Django models, DRF views, serializers, services, selectors, Celery tasks, migrations, or tests.
Python documentation conventions — a Google-style docstring on every public module, class, and function, enforced by ruff's pydocstyle (D) rules with the google convention, and…
Extracts and indexes Python module documentation using ast.parse and docstring_parser library. Supports Google, NumPy, and Sphinx docstring formats with type hint…
Handle noisy git diffs from src/*.egg-info generated by editable installs (uv) and decide whether to commit or ignore.
Build maintainable Python applications and automation with typed boundaries, packaging, environments, tests, and robust subprocess handling.
Apply focused Python engineering judgment after repository gate discovery. Use for Python packages, typing, async behavior, tests, dependencies, compatibility, and builds; defer…
Fast Python environment management with uv (10-100x faster than pip). Triggers on: uv, venv, pip, pyproject, python environment, install package, dependencies.
Use when when you have installed a Python package (e.g., via pip or conda) and need to confirm that the installation succeeded and all expected submodules can be imported.
Use when you are preparing to run Hi-C data normalization or read alignment filtering steps that depend on Python modules (iced, pysam, numpy, scipy) and you need to ensure…
Use when when you have access to a research repository or README documenting a machine learning implementation (e.g., Keras/TensorFlow-based deep learning model) and need to…
Use when you have cloned a scientific repository containing Python code (scripts, Jupyter notebooks, or module imports) and need to execute it locally or on new hardware.