Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/smileynet/teach-menpx agentmods add skills/smileynet/teach-me/quiz-meWrote 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/smileynet/teach-me/quiz-me)<a href="https://agentmods.dev/skills/smileynet/teach-me/quiz-me"><img src="https://agentmods.dev/badge/skills/smileynet/teach-me/quiz-me/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/smileynet/teach-me/quiz-me"><img src="https://agentmods.dev/badge/skills/smileynet/teach-me/quiz-me.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 | $0.00036 | $0.01508 |
| Opus 5 | $0.00018 | $0.00754 |
| Sonnet 5 | $0.00007 | $0.00302 |
| Haiku 4.5 | $0.00004 | $0.00151 |
Grade A, and why
quiz-me 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The user wants to test their retention. This is not a grilling session (plan-sharpening) — it is knowledge verification.
SR-Powered Review Mode
If spaced repetition cards exist (learning-records/questions/*.jsonl), check what's due first:
python tools/sr-status.py # quick health check
python tools/review.py # list due cards
python tools/review.py <topic> # filter to one topic
When cards are due, prefer surfacing them as conversational questions (the learner explains, you assess quality 0-5 and record via review.py --review ID QUALITY). This integrates naturally with the Socratic dialog below.
If the user asks to "review" or "practice" without specifying a topic, use due cards across all topics (interleaved). If they name a topic, filter to that topic.
Quiz from Source Section
When the user says "quiz me on chapter 3" or "test me on the auth section" and source-chunks/{domain}.json exists:
- Run
python tools/match_section.py source-chunks/{domain}.json "chapter 3"to find matching chunks - Read the matched chunk content — this is the material to quiz from
- Ask 3-5 questions using the patterns below, drawn entirely from the matched content
- Populate
source_sectionandsource_pagewhen recording answers
This skips SR card lookup — it's immediate comprehension checking on material the learner just read. Frame it as "let's check if this landed" not as a test.
How to quiz
- Read the workspace state — check
./learning-records/,./lessons/, and./reference/to understand what the user has been taught. - Pick the scope — if the user named a topic or lesson, quiz on that. Otherwise, quiz across recent learning records — prioritise material that hasn't been tested yet.
- Ask in rounds — 3-5 questions per round. Ask conceptual questions (see below).
- Wait for answers — don't reveal correct answers until the user responds.
- Evaluate against criteria, not exact wording — check whether the learner's response hits the key relationship/mechanism. Multiple valid phrasings are expected. Don't penalize missing details that aren't central.
- Give immediate feedback — acknowledge what they got right, clarify what they missed (one sentence), cite the source.
- Record results — if the user demonstrates solid understanding of something new, write a learning record. If they reveal a gap, note it in
NOTES.mdfor the next lesson to address.
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 · 132 lines · 36 tokens per session scan A 93589b1264f6
quiz-me is a skill published in the GitHub repository smileynet/teach-me (3 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,508 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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