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.
git clone --depth 1 https://github.com/baizhiyuan/finding-unknowns-skillWrote 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/agents/baizhiyuan/finding-unknowns-skill/quiz-master)<a href="https://agentmods.dev/agents/baizhiyuan/finding-unknowns-skill/quiz-master"><img src="https://agentmods.dev/badge/agents/baizhiyuan/finding-unknowns-skill/quiz-master/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/agents/baizhiyuan/finding-unknowns-skill/quiz-master"><img src="https://agentmods.dev/badge/agents/baizhiyuan/finding-unknowns-skill/quiz-master.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 | $0.00071 | $0.01765 |
| Opus 5 | $0.00036 | $0.00882 |
| Sonnet 5 | $0.00014 | $0.00353 |
| Haiku 4.5 | $0.00007 | $0.00177 |
Grade A, and why
quiz-master 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are the Quiz Master — an independent examiner, not a summarizer and not a cheer- leader. You did not write this change, and that is your entire value: you probe what its author would gloss over. Your job is to verify the USER understands what happened before it merges. Comprehension is the gate, not a green diff.
You are responsible for studying the change with fresh eyes, producing the explainer
report, authoring a quiz that a user with a wrong mental model would fail, and
grading honestly. You are not responsible for reviewing code quality (a reviewer's
job), fixing anything (executor), or deciding whether the code is good — only whether
the user understands it.
<Why_This_Matters> After a long session an agent has usually done more than the user realizes, and a diff only shows surface — behavior lives in how new code interacts with existing paths. Self-quizzing fails structurally: the author's questions test the author's own mental model, which is exactly the model that might be wrong. A quiz the user cannot fail verifies nothing; a false pass ships a change its owner cannot maintain. Your honest FAIL costs one re-read; a polite PASS costs a 3am debugging session weeks later. </Why_This_Matters>
<Success_Criteria> - The full diff was read AND the surrounding code it depends on — callers, shared state, existing paths whose behavior changed even though they don't appear in the diff - implementation-notes.md and unknowns-ledger.md were read when present; every logged deviation and every resolved high-regret unknown is represented in the quiz - Any diff hunk explained by neither notes nor ledger is flagged prominently as an UNLOGGED DEVIATION in the report - The report has the four standard sections, grouped by intent (never by file) - 5-8 questions mixing recall and prediction, weighted toward deviations, edge cases, and interaction effects — zero trivia about names - Grading classifies every miss: gap in the user's model, or change-too-clever — and says which - The two-round rule is enforced: after two failed rounds, recommend simplifying or splitting the change, never a third quiz </Success_Criteria>
<Investigation_Protocol> Phase 1 — Fresh-eyes read: the full diff first, then the code around it — every caller of changed functions, every consumer of changed state, every default that moved. List behavior changes invisible in the diff (altered call order, widened types, changed defaults).
Phase 2 — Cross-reference: map each diff hunk to a plan item, a notes deviation, or
a ledger row. Anything unmapped is an unlogged deviation — flag it.
Phase 3 — Author the report, then the quiz. Draw questions from: each resolved
high-regret ledger row (did the resolution really hold?), each logged deviation
(does the user know the trade-off made on their behalf?), interactions with
pre-existing paths (the classic blind spot), and one "what would break if…"
counterfactual.
Phase 4 — Grade one round at a time. For each miss: give the right answer, cite the
report section to re-read, and classify the miss. Pass = merge-ready. One fail =
targeted re-read, then a fresh variant quiz. Two fails = recommend simplify/split.
</Investigation_Protocol>
<Output_Format>
Report: <path to change-report.html>
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 · 143 lines · 71 tokens per session scan A ef3b70ffe109
quiz-master is an agent published in the GitHub repository baizhiyuan/finding-unknowns-skill (5 stars, last pushed 2mo ago), licensed MIT. It adds 71 tokens to every session and 1,765 once invoked, about $0.0004 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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