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 lab3git 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/lab3)<a href="https://agentmods.dev/skills/bruce1986/vibe-to-prod-lab/lab3"><img src="https://agentmods.dev/badge/skills/bruce1986/vibe-to-prod-lab/lab3/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/lab3"><img src="https://agentmods.dev/badge/skills/bruce1986/vibe-to-prod-lab/lab3.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 | $0.00050 | $0.00388 |
| Opus 5 | $0.00025 | $0.00194 |
| Sonnet 5 | $0.00010 | $0.00078 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
lab3 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 3 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/lab3-production/README.md，按其步驟帶學員走： 解剖quality.yml三關 → 看 Step Summary → 跑log_demo.py讀 JSONL → （講師 demo）Phoenix。 - 解剖 workflow 時用提問法：「
deploy-gate的needs拿掉會發生什麼事？」 「lab2 弄紅的那次 run，deploy-gate 有跑嗎？」 - 讀 log 欄位時，重點帶三個觀念：trace_id 串起多步驟呼叫、
cost 是平台自己算的（OTel
gen_ai.*沒有 cost 欄位）、 prompt／completion 內容預設不記（隱私與 PII 治理）。 - 學員想多做一步時，建議他們在
log_demo.py加一個欄位（例如retry_count），並同步更新 README 欄位表——體驗「log schema 也要維護」。 - 結尾思考題：如果幻覺分數連續三天上升，你的告警要長什麼樣子？ （帶到「監控不是收集數據，是設計反應」）
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 · 50 tokens per session scan A fe6250f456b0
lab3 is a skill published in the GitHub repository Bruce1986/vibe-to-prod-lab (2 stars, last pushed 19d ago), licensed MIT. It adds 50 tokens to every session and 388 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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