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Gravitas Python

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udaysharmadev
gravitas-python

GRAVITAS extension for Python projects. Adds Python-specific recon, type checking with mypy/pyright, ruff/flake8 lint, pytest patterns, and common Python failure modes. Load in addition to the base GRAVITAS skill.

Overview

Publisherudaysharmadev
RepositoryGravitas
Skill namegravitas-python
Stars
312
Forks
29
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by udaysharmadev on GitHub. Read the source before you install it.

Installation

Install the Gravitas Python AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/udaysharmadev/Gravitas/tree/main/skills/gravitas-python
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/udaysharmadev/Gravitas.git /tmp/Gravitas
mkdir -p .claude/skills
cp -r /tmp/Gravitas/skills/gravitas-python .claude/skills/gravitas-python
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gravitas Python in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gravitas Python on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gravitas Python is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GRAVITAS — Python Extension

Extends the base GRAVITAS skill with Python-specific patterns. Load skills/gravitas/SKILL.md first, then this file.


Python Recon Additions

□ pyproject.toml — [tool.mypy], [tool.ruff], [tool.pytest.ini_options]
□ setup.py / setup.cfg — legacy config
□ requirements.txt / requirements-dev.txt — dependency versions
□ conftest.py — pytest fixtures (critical for understanding test patterns)
□ __init__.py — package structure
□ Type stubs — are there py.typed markers? stub packages installed?

Read pyproject.toml for type strictness

bash
cat pyproject.toml | grep -A 10 "\[tool.mypy\]"
cat pyproject.toml | grep -A 10 "\[tool.ruff\]"

Python Verification Chain

bash
# Full chain — in this order
mypy . --strict                          # type correctness
ruff check .                             # lint (fast, Rust-based)
ruff format --check .                    # format check
pytest -v --tb=short                     # all tests

# With coverage
pytest --cov=src --cov-report=term-missing -v

# Type coverage
mypy . --strict 2>&1 | tail -5           # summary line shows error count

Python-Specific Anti-Patterns

Never use bare except

python
BAD:  except:
GOOD: except ValueError as e:
GOOD: except (TypeError, ValueError) as e:

Never shadow builtins

python
BAD:  list = [1, 2, 3]     # shadows list()
BAD:  id = user.id         # shadows id()
GOOD: items = [1, 2, 3]
GOOD: user_id = user.id

Type annotations required (with mypy strict)

python
BAD:  def process(data):
GOOD: def process(data: dict[str, Any]) -> ProcessResult:

Use dataclasses or Pydantic over dicts for structured data

python
BAD:  return {"user_id": 1, "name": "Alice"}   # no type safety
GOOD: return UserResponse(user_id=1, name="Alice")

Common Python Failure Modes

FailureRoot CauseFix
Optional[X] has no attribute YNone not handledAdd if x is not None: guard
Incompatible return value typeWrong return typeCheck function signature
Missing return statementNot all paths returnAdd explicit return
Module has no attribute XWrong importCheck module with dir(module)
Cannot find implementation for XMissing stubInstall types-X package
Circular importImport at module levelMove to function level or restructure

Test Patterns (pytest)

Fixture pattern

python
# conftest.py
import pytest
from myapp.db import get_db

@pytest.fixture
def db():
    conn = get_db(":memory:")
    yield conn
    conn.close()

@pytest.fixture
def client(db):
    from myapp import create_app
    app = create_app({"TESTING": True})
    with app.test_client() as c:
        yield c

Standard test structure

python
class TestUserAuth:
    def test_login_success(self, client, db):
        # Arrange
        db.execute("INSERT INTO users VALUES (1, 'alice', 'hash')")
        # Act
        response = client.post("/login", json={"username": "alice", "password": "pw"})
        # Assert
        assert response.status_code == 200
        assert response.json["token"] is not None

    def test_login_wrong_password(self, client, db):
        response = client.post("/login", json={"username": "alice", "password": "wrong"})
        assert response.status_code == 401

Parametrize for edge cases

python
@pytest.mark.parametrize("input,expected", [
    ("", ValueError),
    (None, TypeError),
    ("valid@email.com", None),
    ("not-an-email", ValueError),
])
def test_validate_email(input, expected):
    if expected:
        with pytest.raises(expected):
            validate_email(input)
    else:
        validate_email(input)  # should not raise

Framework-Specific Notes

FastAPI

python
# Always test with TestClient, not raw function calls
from fastapi.testclient import TestClient
client = TestClient(app)

# Type all path/query params and bodies
@router.post("/users", response_model=UserResponse)
async def create_user(body: CreateUserRequest) -> UserResponse:
    ...

Django

python
# Use pytest-django
# settings for tests
@pytest.mark.django_db
def test_user_creation():
    user = User.objects.create_user(username="test", password="pass")
    assert user.id is not None

Quick Reference

bash
# Run single test file
pytest src/auth/test_session.py -v

# Run single test
pytest src/auth/test_session.py::TestSession::test_timeout -v

# Run with pdb on failure
pytest --pdb

# Show slowest tests
pytest --durations=10

# Type check single file
mypy src/auth/session.py --strict

Frequently asked questions

What does the Gravitas Python AI skill do?

GRAVITAS extension for Python projects. Adds Python-specific recon, type checking with mypy/pyright, ruff/flake8 lint, pytest patterns, and common Python failure modes. Load in addition to the base GRAVITAS skill.

Why use Gravitas Python on TypingMind?

Because you install it once and use it with any model. Gravitas Python is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gravitas Python in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/udaysharmadev/Gravitas/tree/main/skills/gravitas-python. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gravitas Python?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gravitas Python?

As many as you like. As long as a model supports skills, you can use Gravitas Python with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gravitas Python AI skill free?

Yes. It is published on GitHub by udaysharmadev under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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