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.

16 stars

Best use case

agent-ops-create-python-project is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using agent-ops-create-python-project 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/agent-ops-create-python-project/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/development/agent-ops-create-python-project/SKILL.md"

Manual Installation

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

How agent-ops-create-python-project Compares

Feature / Agentagent-ops-create-python-projectStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

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.

SKILL.md Source

# Skill: agent-ops-create-python-project

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

**Templates**: See [TEMPLATES.md](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

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