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 Goldziher/ai-rulez --skill write-testsgit clone --depth 1 https://github.com/Goldziher/ai-rulezWrote 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/goldziher/ai-rulez/write-tests)<a href="https://agentmods.dev/skills/goldziher/ai-rulez/write-tests"><img src="https://agentmods.dev/badge/skills/goldziher/ai-rulez/write-tests/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/goldziher/ai-rulez/write-tests"><img src="https://agentmods.dev/badge/skills/goldziher/ai-rulez/write-tests.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 | $0.00028 | $0.00160 |
| Opus 5.5 | $0.00011 | $0.00064 |
| Sonnet 5.5 | $0.00006 | $0.00032 |
| Haiku 4.5 | $0.00003 | $0.00016 |
Grade A, and why
write-tests 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 today.
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.
What it actually says
Write tests
- Create the test file named
<module>_test.pynext to the module. - Use this shape:
import unittest
from parcel_calc import total
ITEMS = [10, 20]
class TotalTest(unittest.TestCase):
def test_total(self):
self.assertEqual(total(ITEMS), 30)
def test_total_empty(self):
self.assertEqual(total([]), 0)
if __name__ == "__main__":
unittest.main()
- Run
python <module>_test.pyand confirm that both tests pass.
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.
- today First seen · 32 lines · 28 tokens per session scan A 51bdf15588a5
write-tests is a skill published in the GitHub repository Goldziher/ai-rulez (163 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 160 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 2026-10-17.
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