python-sdk-specialist

Python SDK development with async support and type hints

509 stars

Best use case

python-sdk-specialist is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Python SDK development with async support and type hints

Teams using python-sdk-specialist should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/python-sdk-specialist/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/sdk-platform-development/skills/python-sdk-specialist/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/python-sdk-specialist/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How python-sdk-specialist Compares

Feature / Agentpython-sdk-specialistStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Python SDK development with async support and type hints

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Python SDK Specialist Skill

## Overview

This skill specializes in developing Pythonic SDKs with full type hint support, async/await capabilities, and modern Python best practices for Python 3.8+ compatibility.

## Capabilities

- Design Pythonic SDK architecture following PEP guidelines
- Implement async/await with aiohttp, httpx, or asyncio
- Configure comprehensive type hints with mypy validation
- Support Python 3.8+ with proper compatibility handling
- Implement context managers for resource management
- Design intuitive API surfaces following Python conventions
- Configure packaging for PyPI distribution
- Implement proper logging and debugging support

## Target Processes

- Multi-Language SDK Strategy
- SDK Architecture Design
- SDK Testing Strategy

## Integration Points

- PyPI package registry
- pytest for testing
- mypy for type checking
- httpx/aiohttp for HTTP clients
- pydantic for data validation
- poetry/setuptools for packaging

## Input Requirements

- API specification (OpenAPI, GraphQL, or custom)
- Target Python version range
- Async requirements
- Type strictness level
- Packaging preferences (poetry vs setuptools)

## Output Artifacts

- Python SDK package source code
- Type stub files (.pyi) if needed
- pytest test suite
- pyproject.toml configuration
- Documentation (Sphinx-ready)
- Example scripts

## Usage Example

```yaml
skill:
  name: python-sdk-specialist
  context:
    apiSpec: ./openapi.yaml
    pythonVersion: ">=3.8"
    asyncSupport: true
    typeHints: strict
    httpClient: httpx
    packageManager: poetry
```

## Best Practices

1. Follow PEP 8 style guide
2. Use type hints throughout (PEP 484, 585)
3. Implement both sync and async interfaces
4. Use context managers for connections
5. Provide comprehensive docstrings (Google style)
6. Support optional dependencies properly

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