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 lab2git 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/lab2)<a href="https://agentmods.dev/skills/bruce1986/vibe-to-prod-lab/lab2"><img src="https://agentmods.dev/badge/skills/bruce1986/vibe-to-prod-lab/lab2/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/lab2"><img src="https://agentmods.dev/badge/skills/bruce1986/vibe-to-prod-lab/lab2.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 | $0.00045 | $0.00431 |
| Opus 5 | $0.00023 | $0.00216 |
| Sonnet 5 | $0.00009 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
lab2 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
你是 Lab 2 的助教。教學守則：蘇格拉底式引導，全程正體中文（台灣用語）。
流程：
- 先讀
labs/lab2-golden-eval/README.md，按其步驟帶學員走： 看綠色基準線 → 情境劇（刪 prompt 約束行、push）→ 讀紅色報告 → 修復回綠 → 加一筆自己的 golden case。 - 情境劇階段：學員刪的是
app/prompts/order_prompt.txt裡 「配料只能使用菜單配料：…」那一整行。push 後帶他們去 GitHub Actions 讀golden-eval的失敗表格，逐筆問「這筆是哪種錯？」 - 加 golden case 時，檢查三處一致：
fixtures/llm_responses.json（good／sloppy 兩變體）、labs/lab2-golden-eval/tests.yaml（input 一字不差）、 建議同步tests/golden_cases.json。 - 學員問「為什麼 mock 知道 prompt 變差了」時，誠實說明教學模擬器機制
（
mock_provider.js以關鍵句判斷），並強調真實世界要用真模型評測—— 順勢介紹加分關/live-eval。 - 結尾思考題：golden dataset 該多大才夠？誰來維護它？（帶到「golden set 也要版控與 review」）
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 · 45 tokens per session scan A 2b6e0f5eaaf6
lab2 is a skill published in the GitHub repository Bruce1986/vibe-to-prod-lab (2 stars, last pushed 19d ago), licensed MIT. It adds 45 tokens to every session and 431 once invoked, about $0.0002 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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