Borrowing it
Nothing to install: this file belongs to TakaGoto/rag-learning-academy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TakaGoto/rag-learning-academy/main/.claude/skills/quiz/SKILL.mdgit clone --depth 1 https://github.com/TakaGoto/rag-learning-academyWrote 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/takagoto/rag-learning-academy/quiz)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/quiz"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/quiz/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/takagoto/rag-learning-academy/quiz"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/quiz.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 | $0.00010 | $0.00864 |
| Opus 5 | $0.00005 | $0.00432 |
| Sonnet 5 | $0.00002 | $0.00173 |
| Haiku 4.5 | $0.00001 | $0.00086 |
Grade A, and why
quiz 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quiz: Test Your RAG Knowledge
Scope: This skill tests your understanding with questions and scoring. For guided teaching of new concepts, use
/lesson.
Generate and administer a quiz on a specific RAG topic to help learners assess their understanding. Quizzes reinforce learning and surface gaps.
Step 1: Determine the Topic
- If the user specifies a topic (e.g.,
/quiz chunking), use that topic. - If no topic is given, read
progress/module-tracker.mdto find the most recently completed lesson and quiz on that. - If no progress exists, suggest running
/startor/lessonfirst.
Step 2: Generate Questions
Create 5-10 questions that mix the following types:
Conceptual Questions (2-3)
Test understanding of "why" and "how" things work. Example: "Why might fixed-size chunking lose important context at chunk boundaries?"
Code Reading Questions (2-3)
Show a code snippet and ask what it does, what is wrong with it, or what the output would be. Example: Show a retrieval function and ask why it might return irrelevant results.
Scenario-Based Questions (2-3)
Present a real-world situation and ask the learner to choose the best approach. Example: "You have a corpus of legal contracts averaging 50 pages each. Which chunking strategy would you start with and why?"
True/False with Justification (1-2)
A statement the learner must evaluate and explain. Example: "Larger chunk sizes always improve retrieval quality. True or false? Explain."
Step 3: Administer the Quiz
Present questions one at a time. Wait for the learner's answer before moving to the next question. Do not reveal the correct answer until the learner has responded.
For each question:
- Present the question clearly
- Wait for the learner's response
- Evaluate the response — is it correct, partially correct, or incorrect?
- Provide the correct answer with a clear explanation
- If incorrect, explain the misconception and link it back to the relevant concept
Step 4: Score and 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.
- 10d ago First seen · 85 lines · 10 tokens per session scan A 26ca673aea1a
quiz is a skill published in the GitHub repository TakaGoto/rag-learning-academy (19 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 864 once invoked, about $0.0001 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 2026-08-30.
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