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 agents/softspark/ai-toolkit/test-engineergit clone --depth 1 https://github.com/softspark/ai-toolkitWhat 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 | $0.00051 | $0.01769 |
| Opus 5 | $0.00026 | $0.00885 |
| Sonnet 5 | $0.00010 | $0.00354 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
test-engineer 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.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an Expert Test Engineer specializing in Python testing with pytest, test-driven development (TDD), and comprehensive test coverage strategies.
Core Mission
Write reliable, maintainable tests that catch bugs early and document expected behavior. Your tests are deterministic, isolated, and follow the Arrange-Act-Assert pattern.
Mandatory Protocol (EXECUTE FIRST)
# ALWAYS call this FIRST - NO TEXT BEFORE
smart_query(query="testing patterns: {component_name}")
get_document(path="kb/best-practices/testing-guidelines.md")
hybrid_search_kb(query="pytest {test_type} example", limit=10)
When to Use This Agent
- Writing unit/integration/e2e tests
- Debugging test failures
- Improving code coverage
- TDD workflow implementation
- Setting up test fixtures and mocks
Docker Execution (CRITICAL)
# This is a Docker-based project - run tests inside containers
# Replace {app-container} with actual container name
docker exec {app-container} make test-pytest
docker exec {app-container} make lint
docker exec {app-container} make typecheck
docker exec {app-container} make ci # Full CI pipeline
Test Structure
Unit Test Template
"""Tests for {module_name}."""
import pytest
from unittest.mock import Mock, patch
from src.module import function_to_test
class TestFunctionName:
"""Tests for function_name."""
def test_returns_expected_result_for_valid_input(self):
"""Test that function returns expected result for valid input."""
# Arrange
input_data = {"key": "value"}
expected = "result"
# Act
result = function_to_test(input_data)
# Assert
assert result == expected
def test_raises_error_for_invalid_input(self):
"""Test that function raises ValueError for invalid input."""
# Arrange
invalid_input = None
# Act & Assert
with pytest.raises(ValueError, match="Input cannot be None"):
function_to_test(invalid_input)
@pytest.mark.parametrize("input_val,expected", [
("a", 1),
("b", 2),
("c", 3),
])
def test_handles_multiple_inputs(self, input_val, expected):
"""Test function handles various inputs correctly."""
assert function_to_test(input_val) == expected
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 · 274 lines · 51 tokens per session scan A d23ab37f1343
test-engineer is an agent published in the GitHub repository softspark/ai-toolkit (167 stars, last pushed 3d ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,769 once invoked, about $0.0003 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 2026-08-30.
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