Claude-skill-registry agent-ops-create-python-project

Create a plan and issues for implementation of a production-ready Python project with proper structure, tooling, and best practices.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/agent-ops-create-python-project" ~/.claude/skills/majiayu000-claude-skill-registry-agent-ops-create-python-project && rm -rf "$T"
manifest: skills/data/agent-ops-create-python-project/SKILL.md
safety · automated scan (low risk)
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  • references .env files
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source content

Skill: agent-ops-create-python-project

Create a plan and issues for implementation of a production-ready Python project

Templates: See TEMPLATES.md for pyproject.toml, build.py, README, AGENTS.md, .gitignore


Triggers

  • User asks to create a new Python project
  • User provides project requirements/discussion to convert into implementation
  • User wants to scaffold a CLI tool, library, or application in Python

Procedure

Phase 1: Requirements Gathering

  1. If input provided: Analyze the discussion/requirements
  2. If no input: Interview user about:
    • Project name and purpose
    • Core features/commands
    • External services/APIs needed
    • Data processed (files, APIs, databases)
    • CLI, library, or both?
    • Specific dependencies?

Phase 2: Architecture Design

Extract from requirements:

  • Features: List all behaviors/capabilities
  • Data Models: Entities and relationships
  • Dependencies: Map features to PyPI packages
  • Modules: Cohesive, loosely-coupled units
  • Interfaces: Public APIs per module

Phase 3: Issue Creation

Create issues for:

  1. Project scaffold (pyproject.toml, README, .gitignore, AGENTS.md)
  2. Build pipeline (scripts/build.py)
  3. Configuration (src/package/config.py)
  4. CLI layer (src/package/cli.py)
  5. Core modules (one issue per module)
  6. Test infrastructure (tests/conftest.py)
  7. Unit tests (tests/unit/*)

Phase 4: Plan Generation

Create plan with:

  • Dependency order (scaffold → config → core → CLI → tests)
  • Estimated effort per issue
  • Quality gates between phases

Project Structure

project-name/
├── pyproject.toml          # Config, dependencies, tools
├── README.md               # Overview, install, usage
├── AGENTS.md               # AI agent guidelines
├── .gitignore
├── scripts/
│   └── build.py            # Build pipeline
├── src/<package>/
│   ├── __init__.py
│   ├── cli.py              # Thin CLI (typer)
│   ├── config.py           # Configuration
│   └── <modules>.py        # Core logic
└── tests/
    ├── conftest.py
    ├── unit/
    └── integration/

Code Standards

Design Principles

  • SRP: One responsibility per module/function
  • DRY: No duplicated logic
  • Dependency Injection: Accept dependencies as parameters

Architecture Rules

  • Thin CLI: Parse/format only, delegate to core
  • No src imports:
    from <package>
    not
    from src.<package>
  • Config via environment:
    .env
    +
    python-dotenv

Function Guidelines

  • Max 15 lines per function
  • Max 3 levels of nesting
  • Type annotations on ALL functions

Output Checklist

  • All functions have type annotations
  • Tests cover core logic (≥75% target)
  • CLI is thin orchestration layer
  • pyproject.toml is valid
  • README explains
    uv run
    usage
  • AGENTS.md contains guidelines