# Unicorn Python

> >-

- **Type:** Skill
- **Install:** `agentstack add skill-andrey-learning-machines-swe-harness-unicorn-python`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [andrey-learning-machines](https://agentstack.voostack.com/s/andrey-learning-machines)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [andrey-learning-machines](https://github.com/andrey-learning-machines)
- **Source:** https://github.com/andrey-learning-machines/swe-harness/tree/main/plugins/swe-harness/skills/unicorn-python

## Install

```sh
agentstack add skill-andrey-learning-machines-swe-harness-unicorn-python
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Python Domain Skill

## Modern Type Hints (3.10+)

```python
# Union with | operator
def process(value: int | str | None) -> dict[str, int]: ...

# Type aliases
type UserID = int
type Config = dict[str, str | int | bool]

# See references/type-hints-advanced.md for:
# - Generics and TypeVars
# - Protocols (structural typing)
# - Function overloading
```

## Testing with Pytest

```python
# Fixtures
@pytest.fixture
def sample_user():
    return {"name": "Alice", "email": "alice@example.com"}

# Parametrize
@pytest.mark.parametrize("input,expected", [
    (0, 0), (1, 1), (2, 4),
])
def test_square(input, expected):
    assert input ** 2 == expected

# Mock with pytest-mock
def test_api(mocker):
    mock_get = mocker.patch("requests.get")
    mock_get.return_value.json.return_value = {"status": "ok"}
    result = fetch_data()
    assert result["status"] == "ok"
```

**Coverage target: 80%+**

```bash
pytest --cov=myapp --cov-report=term-missing --cov-fail-under=80
```

**See references/testing-pytest.md for** advanced fixtures, mocking patterns, markers, plugins.

## Tooling

### Ruff (Linting + Formatting)

Replaces Black, isort, flake8, pyupgrade.

```bash
ruff check --fix . && ruff format .
```

```toml
# pyproject.toml
[tool.ruff]
line-length = 88
target-version = "py310"

[tool.ruff.lint]
select = ["E", "W", "F", "I", "N", "UP", "B", "C4", "SIM"]
ignore = ["E501"]

[tool.ruff.format]
quote-style = "double"
```

### Mypy (Type Checking)

```toml
[tool.mypy]
python_version = "3.10"
strict = true
warn_return_any = true
disallow_untyped_defs = true
```

### Poetry (Dependency Management)

```bash
poetry init
poetry add requests
poetry add --group dev pytest pytest-cov ruff mypy
poetry install
poetry run pytest
```

**See references/tooling-config.md for** complete ruff rule sets, mypy options, pre-commit hooks, CI/CD.

## Async Programming

```python
# Concurrent execution
async def fetch_all(urls: list[str]) -> list[dict]:
    return await asyncio.gather(*[fetch_data(url) for url in urls])

# Async context manager
class AsyncDatabase:
    async def __aenter__(self):
        self.connection = await connect()
        return self

    async def __aexit__(self, *args):
        await self.connection.close()
```

**See references/async-patterns.md for** task management, semaphores, queues, error handling, pitfalls.

## Data Modeling

| Use case | Choose | Why |
|----------|--------|-----|
| Internal data, simple containers | `@dataclass` | Stdlib, fast, no deps |
| Performance-critical, immutable | `@dataclass(frozen=True, slots=True)` | Minimal overhead |
| External/untrusted data, APIs | `pydantic.BaseModel` | Auto-validation, serialization |
| Config from environment | `pydantic_settings.BaseSettings` | Env var parsing, type coercion |

```python
# Dataclass: internal data
@dataclass(frozen=True)
class Point:
    x: float
    y: float

# Pydantic: external data with validation
class UserCreate(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    email: EmailStr
    age: int = Field(..., ge=0, le=150)

    @field_validator("email")
    @classmethod
    def normalize_email(cls, v: str) -> str:
        return v.lower().strip()
```

**See references/dataclasses-pydantic.md for** advanced features, nested models, settings, JSON schema.

## Anti-patterns Quick Reference

| Anti-pattern | Fix |
|-------------|-----|
| Mutable default `def f(x=[])` | `def f(x=None)` then `x = x or []` |
| Bare `except:` | `except SpecificError as e:` |
| `== None` / `== True` | `is None` / `is True` |
| Modify list while iterating | List comprehension filter |
| `f = open()` without `with` | `with open() as f:` |
| String concat in loop `+=` | `"".join(...)` |
| Manual index counter | `enumerate()` |
| `type(x) == list` | `isinstance(x, list)` |
| `print()` debugging | `logging.getLogger(__name__)` |
| Star imports `from x import *` | Explicit imports |

## Project Setup Checklist

```bash
# 1. Initialize
poetry init
poetry add 
poetry add --group dev pytest pytest-cov ruff mypy pre-commit

# 2. Configure pyproject.toml
# [tool.ruff], [tool.mypy], [tool.pytest.ini_options]

# 3. Install hooks
pre-commit install

# 4. Full quality check
ruff check --fix . && ruff format . && mypy . && pytest --cov=myapp --cov-fail-under=80
```

## Commands

```bash
ruff format . && ruff check --fix .                              # Format + lint
mypy .                                                           # Type check
pytest --cov=myapp --cov-report=term-missing --cov-fail-under=80 # Test + coverage
```

## Reference Files

- **references/type-hints-advanced.md** - Generics, Protocols, TypeVars, overloads, TypedDict
- **references/async-patterns.md** - Task management, queues, synchronization, performance
- **references/testing-pytest.md** - Fixtures, parametrize, mocking, markers, plugins
- **references/tooling-config.md** - ruff, mypy, poetry, pre-commit, bandit configs
- **references/dataclasses-pydantic.md** - Data modeling, validation, serialization, settings

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [andrey-learning-machines](https://github.com/andrey-learning-machines)
- **Source:** [andrey-learning-machines/swe-harness](https://github.com/andrey-learning-machines/swe-harness)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-andrey-learning-machines-swe-harness-unicorn-python
- Seller: https://agentstack.voostack.com/s/andrey-learning-machines
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.