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ia-python-services

Generate production-ready Python CLI tools and backend services with async concurrency, resilience, and testing.

30|3|Updated Feb 22, 2026
Search Tags:#pytest#fastapi#python#uv#ruff#asyncio#service architecture
One-click install
npx skills add https://github.com/iliaal/whetstone --skill ia-python-services
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ia-python-services
Source: https://github.com/iliaal/whetstone/tree/main/plugins/whetstone/skills/ia-python-services
Command: npx skills add https://github.com/iliaal/whetstone --skill ia-python-services

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you produce reliable, production-grade Python CLI tools and backend services by enforcing consistent engineering discipline for async concurrency, resilience, testing, and maintainable architecture.

Core Features & Use Cases

  • Modern Python tooling workflow: Use uv for dependency management, ruff for lint/format, ty for type checking, and pytest for test rigor with a coverage target.
  • Correct async/concurrency decisions: Choose between sync, asyncio patterns, and multiprocessing based on whether work is I/O-bound or CPU-bound, with guidance for structured concurrency and cancellation.
  • Operational readiness for services: Apply resilience patterns (timeouts, retries, idempotency), health/ready endpoints, structured observability (structlog, metrics, tracing), and clean error/envelope design.
  • Practical backend and API design: Contract-first Pydantic/FastAPI schemas, consistent error structures, DI-friendly project structure, and safe evolution practices.

Quick Start

Ask the agent to "design and implement a FastAPI service endpoint with contract-first Pydantic schemas, proper readiness/health checks, async concurrency where appropriate, and a complete pytest + ruff + ty validation workflow."

Frequently Asked Questions about ia-python-services

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure a FastAPI service with proper async concurrency and health checks?▼

Build production-ready FastAPI services by applying contract-first Pydantic schemas, enforcing correct sync vs asyncio boundaries for I/O-bound work, and implementing readiness and health endpoints for operational stability.

When should I use asyncio versus multiprocessing in Python backend services?▼

Choose asyncio for I/O-bound work to enable structured concurrency and safe cancellation, and switch to multiprocessing for CPU-bound workloads to maintain responsiveness in Python backend services.

How do I configure uv, ruff, and pytest workflows for a pyproject.toml-based Python project?▼

Configure uv for dependency management, ruff for linting and formatting, and pytest for test rigor with a coverage target to establish a verify-first validation workflow for pyproject.toml-based projects.

What resilience patterns do I need for production-ready Python background jobs?▼

Production-ready Python background jobs require mandatory timeouts, retry rules, and idempotent job behavior to ensure stable execution and prevent duplicate side effects during operational failures.

Does this approach work for building Python CLI tools or is it only for backend services?▼

This approach works for both Python CLI tools and backend services, enforcing disciplined patterns for async concurrency, structured observability, and a verify-first testing checklist across both application types.

What's the best way to handle error envelopes and structured logging in FastAPI applications?▼

Design clean error envelope structures and implement structured observability using structlog, metrics, and tracing to ensure consistent error reporting and operational visibility in FastAPI applications.

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