Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern.
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.
Perform a broad Python production audit, or reconcile specialist evidence and remaining integration risks.
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…
Read-only first troubleshooting workflow for Python project deployments, including systemd startup failures, dependency issues, encoding problems, permissions, proxy setup, and…
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.
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Restructure tangled code into a clear equivalent, preserving behavior. TRIGGER WHEN: the user asks for "readable", "maintainable" or "clean" code, a review flags comprehension…
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.
Maps Python list, dict, and set operations onto Scala List, Map, and Set: typed empty creation, map/filter/foldLeft, lift/getOrElse, groupBy, zipWithIndex field order, and local…
Guidelines for translating Python functional patterns and simulations to idiomatic Scala.
Rewrites Python-shaped Scala toward host idioms: val over var, expression-bodied match, camelCase, Option/Either instead of null or sentinels, smart constructors, and companion…
Ports Python classes, frozen dataclasses, ABCs, mixins, properties, static and class methods, dunders, enums, factories, and singletons onto Scala 3 class, case class, trait,…
Maps everyday Python syntax to idiomatic Scala 3 (val/var, Option vs None, for/yield, interpolation, operators, typed errors).
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).
Use when: writing Python code, designing software architecture in Python, learning best practices, or fixing Python bugs.
Python patterns for CLI tools, async concurrency, and backend services. Use when working with Python code, building CLI apps, FastAPI services, async with asyncio, backgr — from…
Analyzes a Python project and configures 9-dimension quality methodology including hooks, CI, and tool configs.
How a Vis sandbox Python shim is built and linted: one lazy shim_*.clj per shim, Python in resources/vis-shims, the runtime handle registry every shim that hands out a host id…
Use when you have a Python webservice codebase (e.g., a Flask, Django, or FastAPI application) and need to document its HTTP API surface (endpoints, methods, parameters, schemas,…
Python language expert -- debugging, packaging (PyInstaller/Nuitka/cx_Freeze), testing (pytest/unittest), type checking (mypy/pyright), async/concurrency patterns, performance…