When a Python file exceeds ~300 lines, split it into a package. Use when reading a file and noticing it's too large, or when explicitly asked to split a module.
Use when writing Python that calls a function returning an Optional value (e.g., dict.get, re.search) before accessing attributes on the result.
Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility…
Use when after constructing a peak properties dictionary via csv_to_peak_properties
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or…
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Guidelines for Python and Odoo enterprise application development with ORM, XML views, and module architecture best practices.
Use when you are building or refactoring a scientific Python library and need to decide how to organize and expose utility functions (e.g., adaptive coarse-graining, filtering,…
Maps the public API surface of Python packages using ast module parsing and importlib introspection. Generates comprehensive reference docs with type annotations from mypy stubs.
Builds dependency graphs for Python packages using the PyPI JSON API and pipdeptree library. Visualizes transitive dependency chains and identifies version conflict risks.
Use when you need to enable optional modules in Pyteomics that depend on external libraries not bundled with the core package—such as h5py and hdf5plugin for mzMLb format access,…
Retrieves and indexes Python package documentation from PyPI metadata API and Read the Docs API. Uses ast module parsing and pydoc introspection to extract function signatures,…
Use when when setting up a new computational environment for tandem MS/MS spectrum clustering or other proteomics analysis, and you need to install a tool (like falcon) that…
Use when when you need to validate that a Python package (or update to it) is accessible to end users through official distribution channels, or when you are preparing a release…
Use when when you need to verify a Python package installs successfully from a cloned or local repository, validate that all tests pass after installation, or prepare a…
Use when you have a mature Python package (e.g., Mummichog 2.x) that needs to be relocated to a new GitHub organization (e.
Validate Python package surfaces with pyproject metadata, uv-managed builds, dependency boundaries, local smoke checks, semantic versioning, and release-boundary guidance.
Installing and using common Python packages in SkillBench containers. Covers scientific computing, data analysis, and file format libraries.
Comprehensive guide to creating, structuring, and distributing Python packages using modern packaging tools, pyproject.toml, and publishing to PyPI.
Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI.
Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI.
Structure Python projects for distribution with pyproject.toml, src layouts, dependency management, and publishing workflows.
Write Pandas code to this project's standards — vectorised operations, Pandas-native types, and Pandera schemas.
Use when you have precomputed expected contact frequency tables (TSV format with columns like dist_bp, contact_frequency, n_valid) and need to apply log-binning and smoothing to…
Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying. — from sskim91/dotfiles
Advanced Python patterns — concurrency (threading, multiprocessing, async/await), hexagonal architecture with FastAPI, RFC 7807 error handling, memory optimization, pyproject.toml…
Apply when writing, reviewing, or refactoring Python code that involves interfaces, design patterns, or class hierarchies.
Use when working with Peewee ORM patterns, especially DatabaseProxy setup, scoped connection/transaction handling, and SQLite-based tests.
Detailed PEP 8 guide for Python code — layout, imports, names, whitespace, comments, programming recommendations (None comparisons, exceptions, return, strings).
Python performance profiling and optimization: bottleneck detection, memory tuning, benchmarking
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improvi — from…
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improvi — from…
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improvi — from…
Python data processing pipelines with modular architecture. Use when building content processing workflows, implementing dispatcher patterns, integrating Google Sheets/Drive APIs,…
Instructs the agent on Polars expression-based selection, including column selection by name, regex, type, and wildcard selectors.
Instructs the agent on Polars reshaping methods, including pivot, unpivot, transpose, explode, and partition_by.
Generate PowerPoint decks programmatically from data using the python-pptx library. Best for templated decks — per-customer reports, weekly metrics, batched architecture summaries…
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices.
Expert Python developer specializing in Python 3.11+ features, type annotations, and async programming patterns.
Python-specific idioms, philosophy, and expert-level patterns. Use when working with Python code, including Jupyter notebooks (.ipynb).
Expert-level skill for Python programming (Python 3.12+). Covers type safety, async/await, FastAPI/SQLAlchemy, packaging (uv/Poetry), linting (Ruff), and testing with pytest in…
Modern Python project architecture guide for 2025. Use when creating Python projects (APIs, CLI, data pipelines). Covers uv, Ruff, Pydantic, FastAPI, and async patterns.
Generates a universal `config/` folder (paths.py, files.py, dotenv.py, settings.py, __init__.py, .env.example) that works across any Python project — FastAPI, CLI, scripts, or AI…
Creates Python projects with proper structure, virtual environments, and dependency management. Use when users request to create a new Python project, set up a Python development…
Scaffold a multi-repo Python workspace with models library, core library, Flask backend, and optional sub-projects.
Universal project scaffolding toolkit — drops pre-built, battle-tested layers (config, helpers) into any Python project with a single command.
Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with __all__, or planning…
Resolves Python package dependencies using the PyPI JSON API and pip resolver algorithm. Generates locked requirements files and checks compatibility across Python version markers…
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI.
Indexes Python package documentation using the PyPI JSON API and Read the Docs API. Builds searchable reference catalogs with function signatures, type hints, and usage examples.
Release engineering for Python projects on uv — static versioning in pyproject.toml exposed via importlib.metadata, building the sdist and wheel with uv build, publishing to PyPI…
Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators.
Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming…
Expert Python code reviewer specializing in PEP 8 compliance, Pythonic idioms, type hints, security, and performance. Use for all Python code changes.
Python coding rules from ai-toolkit: coding-style, frameworks, patterns, security, testing. Triggers: .py, .pyi, pyproject.toml, requirements.txt, Pipfile, FastAPI, Django, Flask,…
Use when building or reviewing service, job, or CLI runtime behavior in Python — designing startup validation, shutdown sequences, observability, and structured logging.
Guidelines for translating Python functional patterns and simulations to idiomatic Scala.
Create robust Python automation with full logging and safety checks. Use when tasks need complex data processing, authenticated API work, conditional file operations, or error…
Python SDK for inference.sh - run AI apps, build agents, and integrate with 150+ models. Package: inferencesh (pip install inferencesh).