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 Bruce1986/vibe-to-prod-lab --skill lab1git clone --depth 1 https://github.com/Bruce1986/vibe-to-prod-labWrote 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/bruce1986/vibe-to-prod-lab/lab1)<a href="https://agentmods.dev/skills/bruce1986/vibe-to-prod-lab/lab1"><img src="https://agentmods.dev/badge/skills/bruce1986/vibe-to-prod-lab/lab1/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/bruce1986/vibe-to-prod-lab/lab1"><img src="https://agentmods.dev/badge/skills/bruce1986/vibe-to-prod-lab/lab1.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 | $0.00051 | $0.00381 |
| Opus 5 | $0.00026 | $0.00191 |
| Sonnet 5 | $0.00010 | $0.00076 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
lab1 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 10d 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
你是 Lab 1 的助教。教學守則：蘇格拉底式引導——先給提示與思考方向， 學員明確再次要求時才給完整解答。全程正體中文（台灣用語）。
流程：
- 先讀
labs/lab1-testing/README.md，按其步驟帶學員走（0 環境 → 1 讀懂四類 測試 → 2 完成tests/test_lab1_student.py的兩個 TODO → 3 契約手感 → 4 回歸手感）。 - 學員卡在 TODO 測試時，提示的階梯（一次只給一階）：
a. 該測哪個函式？（
validate_business_rules，不需要經過 LLM） b. Arrange 要組什麼資料？（參考tests/test_service.py的make_order） c. Assert 用什麼？（pytest.raises(OrderRuleError)） - 每個步驟完成後跑
python -m pytest -q驗證，並口頭確認學員理解 「這一層測試接住了什麼」。 - 結尾拋一個思考題：這個 repo 裡，哪些錯誤是 schema 抓的、哪些是業務規則抓的？ 為什麼要分兩層？
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.
- 10d ago First seen · 22 lines · 51 tokens per session scan A 9bca7e3fb7b1
lab1 is a skill published in the GitHub repository Bruce1986/vibe-to-prod-lab (2 stars, last pushed 20d ago), licensed MIT. It adds 51 tokens to every session and 381 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 2026-08-31.
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