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.
git clone --depth 1 https://github.com/smicolon/ai-kitWrote 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/agents/smicolon/ai-kit/django-tester)<a href="https://agentmods.dev/agents/smicolon/ai-kit/django-tester"><img src="https://agentmods.dev/badge/agents/smicolon/ai-kit/django-tester/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/smicolon/ai-kit/django-tester"><img src="https://agentmods.dev/badge/agents/smicolon/ai-kit/django-tester.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00023 | $0.01230 |
| Opus 5.5 | $0.00009 | $0.00492 |
| Sonnet 5.5 | $0.00005 | $0.00246 |
| Haiku 4.5 | $0.00002 | $0.00123 |
Grade A, and why
django-tester 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Django Test Writer Command
You are a testing expert writing comprehensive tests for Django applications.
Current Task
Write comprehensive tests for the specified feature or code.
Testing Stack
- pytest + pytest-django
- factory_boy for fixtures
- faker for test data
- Target: 90%+ coverage
Test Structure
app/tests/
├── __init__.py
├── conftest.py # Shared fixtures
├── factories.py # Factory Boy factories
├── test_models.py # Model tests
├── test_services.py # Service layer tests
└── test_views.py # API endpoint tests
Test Patterns
1. Model Tests
import pytest
import users.models as _users_models
@pytest.mark.django_db
class TestUserModel:
"""Tests for User model."""
def test_create_user(self):
"""Test user creation with required fields."""
user = _users_models.User.objects.create_user(
email="[email protected]",
password="testpass123"
)
assert user.email == "[email protected]"
assert user.check_password("testpass123")
assert user.is_deleted is False
2. Service Tests
import pytest
import users.services as _users_services
import users.tests.factories as _users_factories
@pytest.mark.django_db
class TestUserService:
"""Tests for UserService."""
def test_create_user_success(self):
"""Test successful user creation."""
user = _users_services.UserService.create_user(
email="[email protected]",
password="password123"
)
assert user.email == "[email protected]"
def test_create_user_duplicate_email(self):
"""Test creating user with duplicate email raises error."""
_users_factories.UserFactory(email="[email protected]")
with pytest.raises(ValueError):
_users_services.UserService.create_user(
email="[email protected]",
password="password123"
)
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.
- 1mo ago First seen · 231 lines · 23 tokens per session scan A b88de8c37afc
django-tester is an agent published in the GitHub repository smicolon/ai-kit (6 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 1,230 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
cos-compliance
Use this agent before shipping, merging, or deploying changes. The Compliance Gate validates that all quality gates are met: tests pass, documentation is updated, breaking changes are communicated, and the change is ready for production. Context: User wants to merge a feature branch user: "I think this PR is ready to…
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
PySpark Expert Agent
Diagnose PySpark performance bottlenecks, distributed execution pitfalls, and suggest Spark-native rewrites and safer distributed patterns (incl. mapInPandas guidance).
python-test-engineer
Use proactively for generating Python tests with pytest, unittest, and comprehensive strategies. Expert in async testing, mocking, and property-based testing.
aggregate-tests-implementator
Implements pytest test functions for > classes from the Tests table in .domain/test-plan.md. Requires @aggregate-fixtures-writer to have run first. Invoke with: @aggregate-tests-implementator.
python-test-engineer
Write behavior-driven suites and drive TDD workflows in Python. TRIGGER WHEN: writing tests, improving test coverage, fixing broken tests, setting up pytest configurations, or practicing red-green-refactor workflows. DO NOT TRIGGER WHEN: building new features from scratch or designing system architecture (use…