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 jellydn/my-ai-tools --skill quiz-megit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/quiz-me)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/quiz-me"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/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/jellydn/my-ai-tools/quiz-me"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/quiz-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.02455 |
| Opus 5.5 | $0.00004 | $0.00982 |
| Sonnet 5.5 | $0.00002 | $0.00491 |
| Haiku 4.5 | $0.00001 | $0.00246 |
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 1mo 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quiz Me
When to Use
Use this skill after implementation when:
- You've completed a complex feature
- Need to write a PR description
- Want to verify understanding of changes
- About to present work to team
- Ensuring you stay "in the loop" with agent work
What It Does
The agent generates a quiz about the implementation to verify your understanding. This helps you:
- Identify gaps in your knowledge
- Prepare for code review discussions
- Write better PR descriptions
- Stay engaged with increasingly capable agents
How to Execute
Step 1: Scope the Quiz
Determine what to test:
- Core architectural decisions
- Key implementation details
- Edge cases and error handling
- Integration points
- Trade-offs made
Step 2: Generate Questions
Create questions across difficulty levels:
Level 1 - Recall (What):
- What did we implement?
- What files were changed?
- What are the main components?
Level 2 - Understanding (Why):
- Why did we choose this approach?
- Why not use [alternative]?
- What problem does this solve?
Level 3 - Application (How):
- How would you explain this to a reviewer?
- How does this integrate with existing code?
- How would you debug an issue here?
Level 4 - Analysis (Implications):
- What are the trade-offs?
- What could go wrong?
- What would you change if requirements changed?
Step 3: Conduct Quiz (One Question at a Time)
Use the ask_user_question tool for each quiz question. Ask one question at a time — present it, wait for the answer, provide feedback, then move to the next. This makes the quiz feel like a conversation, not a test.
Flow for each question:
- Ask using
ask_user_questionwith the question and options - Read the answer the user selected or typed
- Provide feedback: tell them the correct answer, explain why, link to code
- Track correctness mentally (or note it)
- Proceed to the next question
Guidelines for using ask_user_question:
- Set
headerto a short label (max 16 chars) like"Architecture","Trade-offs","Edge Cases" - Write a clear
questionwith context and any hint references - Provide 2-4 concrete
options— conciselabel(1-5 words) with descriptivedescription - After the user answers, give the correct answer with explanation and code references
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.
- 1mo ago First seen · 318 lines · 10 tokens per session scan A ecdb824fd0f7
quiz-me is a skill published in the GitHub repository jellydn/my-ai-tools (124 stars, last pushed today), licensed MIT. It adds 10 tokens to every session and 2,455 once invoked, about $0.0000 per session on Opus 5.5. 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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