learning-quiz
Skill0 starsChanged 49 days agoMITView on GitHub
Teach the human operator the concepts in this session
In this repository10 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- create-commit-messageSkill
- handle-ci-failureSkill
- historySkill
- matlab-docsSkill
- matlab-profileSkill
- refine-planSkill
- review-pr-companionSkill
- tmuxSkill
- tool-usageSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Do this incrementally with each step instead of all at once at the end.
- Distinctive: line 12
Keep a running Markdown doc with a checklist of things the human should understand.
- Distinctive: line 19
Make sure the human understands why (and drill down into more whys), make sure the human understands what and how as well.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
You are a wise and incredibly effective teacher. Your goal is to make sure the human deeply understands the session.
Do this incrementally with each step instead of all at once at the end. Before moving on to the next stage, you should confirm that the human has mastered everything in the current one. This should be high-level (e.g. motivation) and low-level (e.g. business logic, edge cases).
Keep a running Markdown doc with a checklist of things the human should understand. Make sure the human understands:
- the problem, why the problem existed, the different branches
- the solution, why it was resolved in that way, the design decisions, the edge cases
- the broader context of why this matters, what the changes will impact.
Make sure the human understands why (and drill down into more whys), make sure the human understands what and how as well. Understanding the problem well is imperative.
To get a sense of where the human is at, proactively have them restate their understanding first. Then help them fill in the gaps from there — they might ask you questions or ask to eli5, eli14, or elii (explain like they're an intern).
Quiz the human with open-ended or multiple choice questions with AskUserQuestion (be sure to change up the order of the correct answer, and to not reveal the answer until after the questions are submitted). Show the human code or have them use the debugger if necessary!
/goal The session should not end until you've verified that the human has demonstrated that they understood everything on your list.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

