ECC is a toolkit that organizes and improves how coding agents work through skills, memory, security checks, research practices, and related extensions. It is for developers using agents such as Claude Code, Codex, OpenCode, and Cursor.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/affaan-m/ecc/python-testingnpx skills add affaan-m/ECC --skill python-testinggit clone --depth 1 https://github.com/affaan-m/ECCWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/affaan-m/ecc/python-testing)<a href="https://agentmods.dev/skills/affaan-m/ecc/python-testing"><img src="https://agentmods.dev/badge/skills/affaan-m/ecc/python-testing.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.02527 |
| Opus 5 | $0.00017 | $0.01264 |
| Sonnet 5 | $0.00007 | $0.00505 |
| Haiku 4.5 | $0.00003 | $0.00253 |
Grade A, and why
python-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- python-testing — 100% identical, 2 lines differ
- python-testing — 100% identical, 0 lines differ
- python-testing — 100% identical, 0 lines differ
- python-testing — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 498 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Testing
This skill provides comprehensive Python testing patterns using pytest as the primary testing framework.
Testing Framework
Use pytest as the testing framework for its powerful features and clean syntax.
Basic Test Structure
def test_user_creation():
"""Test that a user can be created with valid data"""
user = User(name="Alice", email="[email protected]")
assert user.name == "Alice"
assert user.email == "[email protected]"
assert user.is_active is True
Test Discovery
pytest automatically discovers tests following these conventions:
- Files:
test_*.pyor*_test.py - Functions:
test_* - Classes:
Test*(without__init__) - Methods:
test_*
Fixtures
Fixtures provide reusable test setup and teardown:
import pytest
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
@pytest.fixture
def db_session():
"""Provide a database session for tests"""
engine = create_engine("sqlite:///:memory:")
Session = sessionmaker(bind=engine)
session = Session()
# Setup
Base.metadata.create_all(engine)
yield session
# Teardown
session.close()
def test_user_repository(db_session):
"""Test using the db_session fixture"""
repo = UserRepository(db_session)
user = repo.create(name="Alice", email="[email protected]")
assert user.id is not None
Fixture Scopes
@pytest.fixture(scope="function") # Default: per test
def user():
return User(name="Alice")
@pytest.fixture(scope="class") # Per test class
def database():
db = Database()
db.connect()
yield db
db.disconnect()
@pytest.fixture(scope="module") # Per module
def app():
return create_app()
@pytest.fixture(scope="session") # Once per test session
def config():
return load_config()
Fixture Dependencies
@pytest.fixture
def database():
db = Database()
db.connect()
yield db
db.disconnect()
@pytest.fixture
def user_repository(database):
"""Fixture that depends on database fixture"""
return UserRepository(database)
def test_create_user(user_repository):
user = user_repository.create(name="Alice")
assert user.id is not None
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 498 lines · 35 tokens per session scan A 9961c5536f09
python-testing is a skill published in the GitHub repository affaan-m/ECC (250,130 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 2,527 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ci-tests
Run the test suite for the current repo, auto-detecting Python (pytest/uv), Node (vitest/pnpm), or Rust (cargo test).
pypi-release
This skill should be used when releasing tunacode-cli to PyPI. It keeps the existing local release checks, then hands the actual PyPI upload to a GitHub Actions workflow that uses the repository's PYPIAPITOKEN secret.
bugfix-protocol
Systematic 6-phase debugging protocol. Structured approach to bugs with quick checks, isolated testing, 20-minute rule, and bug report template.
bugfix-protocol
Systematic 6-phase debugging protocol. Structured approach to bugs with quick checks, isolated testing, 20-minute rule, and bug report template.
python-testing
Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.
accelerate
Run PyTorch training across GPUs with minimal changes.