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.mdsafety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
- references .env files
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
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
- If input provided: Analyze the discussion/requirements
- 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:
- Project scaffold (pyproject.toml, README, .gitignore, AGENTS.md)
- Build pipeline (scripts/build.py)
- Configuration (src/package/config.py)
- CLI layer (src/package/cli.py)
- Core modules (one issue per module)
- Test infrastructure (tests/conftest.py)
- 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:
notfrom <package>from src.<package> - Config via environment:
+.envpython-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
usageuv run - AGENTS.md contains guidelines