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.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-ops-create-python-project/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent-ops-create-python-project Compares
| Feature / Agent | agent-ops-create-python-project | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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 guidelinesRelated Skills
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