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 skills add smicolon/ai-kit --skill test-coverage-advisorgit 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/skills/smicolon/ai-kit/test-coverage-advisor)<a href="https://agentmods.dev/skills/smicolon/ai-kit/test-coverage-advisor"><img src="https://agentmods.dev/badge/skills/smicolon/ai-kit/test-coverage-advisor/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/skills/smicolon/ai-kit/test-coverage-advisor"><img src="https://agentmods.dev/badge/skills/smicolon/ai-kit/test-coverage-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.02458 |
| Opus 5.5 | $0.00024 | $0.00983 |
| Sonnet 5.5 | $0.00012 | $0.00492 |
| Haiku 4.5 | $0.00006 | $0.00246 |
Grade A, and why
test-coverage-advisor 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 — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Coverage Advisor
Auto-suggests comprehensive test cases to achieve the 90%+ test coverage target.
Activation Triggers
This skill activates when:
- Completing feature implementation
- Mentioning "done", "finished", "ready", "complete"
- Saying "review this code"
- Creating models, services, views, or APIs
- Asking about testing
- Before code is marked as complete
Coverage Target (MANDATORY)
90%+ test coverage required for ALL code.
Test categories:
- Unit Tests - Models, services, utilities (80%+ of tests)
- Integration Tests - API endpoints, workflows (15%+ of tests)
- Edge Cases - Error handling, validation (5%+ of tests)
Auto-Analysis Process
Step 1: Identify Untested Code
When feature implementation is complete, scan for:
# models.py - Has tests? ❓
class User(models.Model):
def activate(self): # Needs test
self.is_active = True
self.save()
# services.py - Has tests? ❓
class UserService:
@staticmethod
def create_user(data): # Needs test
# ...business logic
# views.py - Has tests? ❓
class UserViewSet(viewsets.ModelViewSet):
def create(self, request): # Needs test
# ...
Step 2: Calculate Coverage Gap
Current coverage: X%
Target coverage: 90%
Gap: Y%
Missing: Z test cases
Step 3: Generate Missing Test Cases
For Model:
# users/tests/test_models.py
import pytest
import users.models as _users_models
@pytest.mark.django_db
class TestUserModel:
"""User model tests."""
def test_create_user(self):
"""Test user creation with required fields."""
user = _users_models.User.objects.create(
email='[email protected]',
first_name='Test'
)
assert user.id is not None # UUID assigned
assert user.email == '[email protected]'
assert user.created_at is not None
assert user.is_deleted is False
def test_user_str_representation(self):
"""Test __str__ returns email."""
user = _users_models.User.objects.create(email='[email protected]')
assert str(user) == '[email protected]'
def test_activate_user(self):
"""Test activate method sets is_active."""
user = _users_models.User.objects.create(email='[email protected]')
user.activate()
assert user.is_active is True
def test_soft_delete(self):
"""Test soft delete sets is_deleted flag."""
user = _users_models.User.objects.create(email='[email protected]')
user.soft_delete()
assert user.is_deleted is True
# User still in database
assert _users_models.User.objects.filter(id=user.id).exists()
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 Changed 2324abdb069e
- 1mo ago First seen · 395 lines · 60 tokens per session scan A 3a3ac961791d
test-coverage-advisor is a skill published in the GitHub repository smicolon/ai-kit (6 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 2,458 once invoked, about $0.0002 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.
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