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 TeamDzX/myllm-skills --skill unit-testsgit clone --depth 1 https://github.com/TeamDzX/myllm-skillsWrote 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/teamdzx/myllm-skills/unit-tests)<a href="https://agentmods.dev/skills/teamdzx/myllm-skills/unit-tests"><img src="https://agentmods.dev/badge/skills/teamdzx/myllm-skills/unit-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/teamdzx/myllm-skills/unit-tests"><img src="https://agentmods.dev/badge/skills/teamdzx/myllm-skills/unit-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 | $0.00024 | $0.00230 |
| Opus 5 | $0.00012 | $0.00115 |
| Sonnet 5 | $0.00005 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
unit-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 9d 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.
What it actually says
Unit tests
Write focused unit tests for the code the user provides.
- Identify the language and test framework. If it's not obvious, pick the idiomatic default (e.g. pytest for Python, Jest for JS/TS, XCTest for Swift, JUnit for Java) and say which you used.
- Cover, at minimum:
- the happy path,
- edge cases (empty/zero/boundary/large inputs),
- error/invalid-input behaviour.
- Output the tests in one runnable code block.
Rules:
- Use Arrange–Act–Assert and descriptive test names that state the expectation.
- Test observable behaviour and the public interface — not private implementation details.
- One logical assertion focus per test; avoid coupling tests to each other.
- Don't hit the network, clock, or filesystem — stub/mock those and note it.
- If a likely bug surfaces while writing tests, add a failing test for it and flag it briefly.
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.
- 9d ago First seen · 24 lines · 24 tokens per session scan A 57607b47daf9
unit-tests is a skill published in the GitHub repository TeamDzX/myllm-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 230 once invoked, about $0.0001 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 2026-08-31.
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