Fallback method for executing Python when execute_code_sandbox fails with unknown errors
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium — from…
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium — from…
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium — from…
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium — from…
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium — from…
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium — from…
Python expert for stdlib, packaging, type hints, async/await, and performance optimization
Expert guidance for Python testing that analyzes your existing setup and provides evidence-based recommendations.
Python 3.12+ coding guidelines for APIs, data processing, scripting. Apply when editing `.py` files. Use for async code, type hints, dataclasses, file operations, resource…
Python patterns for system reliability — background jobs and task queues (Celery, async), resilience and recovery (retries, backoff, timeouts, circuit breakers via tenacity), and…
Use when building or reviewing external API integrations in Python — designing client boundaries, defining outbound reliability policy, or structuring contract tests.
Generates searchable Python library references using ast module for source parsing and Sphinx autodoc integration.
Expert guidance for Python logging libraries including structlog, standard logging, and log analysis.
Use when choosing or configuring Python logging, especially deciding between stdlib logging and loguru for apps or CLIs.
Python logging with loguru, structlog, and orjson. TRIGGERS - loguru, structlog, structured logging, JSONL logs, log rotation, secret redaction, OTel logging, lightweight logging,…
Use HSP's ty-backed Python route for semantic navigation, diagnostics, references, rename, call hierarchy, formatting, and code actions.
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…
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.
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.
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,…
Validate Python package surfaces with pyproject metadata, uv-managed builds, dependency boundaries, local smoke checks, semantic versioning, and release-boundary guidance.
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.
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…
Use when working with Peewee ORM patterns, especially DatabaseProxy setup, scoped connection/transaction handling, and SQLite-based tests.
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,…
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).
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…
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.
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…
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.
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).
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…
Python language expert -- debugging, packaging (PyInstaller/Nuitka/cx_Freeze), testing (pytest/unittest), type checking (mypy/pyright), async/concurrency patterns, performance…
Systematic Python debugging workflow for spreadsheet tasks to isolate environment issues from script logic