DeepTutor CLI
HKUDS/DeepTutor
Teaches the agent to set up and run DeepTutor from the command line: chat and capabilities, knowledge bases, partners, memory, sessions, notebooks and the server or Web app.
Verify understanding after implementation with targeted quizzes
$ npx skills add jellydn/my-ai-tools --skill quiz-me -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jellydn/my-ai-tools quiz-me --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/quiz-me .claude/skills/quiz-me && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "quiz-me" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-me into .claude/skills/quiz-me/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quiz-me", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-meType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jellydn/my-ai-tools --skill quiz-me -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jellydn/my-ai-tools quiz-me --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/quiz-me .agents/skills/quiz-me && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "quiz-me" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-me into .agents/skills/quiz-me/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quiz-me", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jellydn/my-ai-tools --skill quiz-me -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jellydn/my-ai-tools quiz-me --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/quiz-me .cursor/skills/quiz-me && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "quiz-me" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-me into .cursor/skills/quiz-me/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quiz-me", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jellydn/my-ai-tools.git --path skills/quiz-me--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jellydn/my-ai-tools --skill quiz-me -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jellydn/my-ai-tools quiz-me --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/quiz-me .gemini/skills/quiz-me && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "quiz-me" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-me into .gemini/skills/quiz-me/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quiz-me", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jellydn/my-ai-tools quiz-meInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jellydn/my-ai-tools --skill quiz-me -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/quiz-me .github/skills/quiz-me && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "quiz-me" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-me into .github/skills/quiz-me/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quiz-me", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jellydn/my-ai-tools --skill quiz-me -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jellydn/my-ai-tools quiz-me --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/quiz-me .opencode/skills/quiz-me && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "quiz-me" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/quiz-me into .opencode/skills/quiz-me/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quiz-me", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
quiz-meVerify understanding after implementation with targeted quizzes
Quiz Me is an agent skill from jellydn/my-ai-tools. Verify understanding after implementation with targeted quizzes
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: cline, claude, opencode, amp, codex, gemini, cursor, pi
It sits in Education, covering Quizzes and assessments. The repository describes itself as: Comprehensive configuration management for AI coding tools - Replicate my complete setup for Claude Code, OpenCode, Amp, Li, Codex and Claude Code Switch with custom… The licence is MIT.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 62c9227. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
cline, claude, opencode, amp, codex, gemini, cursor, pi
From compatibility in the SKILL.md frontmatter.
Quiz Me loads about 2.6k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 695 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from jellydn/my-ai-tools at commit 62c9227, republished under its MIT licence (© jellydn). 695 words, ~2,641 tokens.
.claude/skills/quiz-me/SKILL.md (or your agent's skills folder).Use this skill after implementation when:
The agent generates a quiz about the implementation to verify your understanding. This helps you:
Determine what to test:
Create questions across difficulty levels:
Level 1 - Recall (What):
Level 2 - Understanding (Why):
Level 3 - Application (How):
Level 4 - Analysis (Implications):
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_user_question with the question and optionsGuidelines for using ask_user_question:
header to a short label (max 16 chars) like "Architecture", "Trade-offs", "Edge Cases"question with context and any hint referencesoptions — concise label (1-5 words) with descriptive descriptionAfter each question response:
After all questions are answered:
// Ask one at a time
ask_user_question(questions: [{
header: "Architecture",
question: "Why did we use GitHub App Installation flow instead of OAuth for this integration? (Hint: check auth/github/installation.ts for the decision)",
options: [
{
label: "OAuth is deprecated",
description: "GitHub still supports OAuth, so this isn't the reason"
},
{
label: "Org-level access",
description: "Installation flow provides org-level access — OAuth Apps can't access org repos"
},
{
label: "Easier to implement",
description: "Installation flow is actually more complex to set up than basic OAuth"
}
]
}])
// After answer — provide feedback:
// "Correct! OAuth Apps can't access organization repositories, which is a GitHub limitation.
// Installation tokens authenticate as the app installation, giving org-level scope.
// See: auth/github/installation.ts:42-58"ask_user_question(questions: [{
header: "Implementation",
question: "What are the token lifespans in our GitHub auth? Fill in:\n- User OAuth tokens last: ______\n- Installation tokens last: ______\n- We cache installation tokens for: ______\n(Hint: check auth/github/token-cache.ts)",
options: [
{
label: "6mo / 1hr / 55min",
description: "User OAuth=6 months, Installation tokens=1 hour, Cache TTL=55 minutes (5 min buffer)"
},
{
label: "1yr / 8hr / 7hr",
description: "Incorrect — installation tokens only last 1 hour, we need a 5-minute buffer before expiry"
},
{
label: "Permanent / 24hr / 23hr",
description: "Incorrect — GitHub installation tokens have a 1-hour expiry, not 24 hours"
}
]
}])ask_user_question(questions: [{
header: "Trade-offs",
question: "What's the main trade-off of our token caching strategy? We cache installation tokens with a 55-minute TTL.",
options: [
{
label: "Speed vs staleness",
description: "Correct — caching avoids rate limits (5000/hr) but a token could be stale for up to 5 minutes before natural expiry"
},
{
label: "Memory vs latency",
description: "The token cache is small (Redis, key pattern github:install:{id}:token) — memory isn't the constraint here"
},
{
label: "Security vs simplicity",
description: "Tokens are encrypted in Redis — security wasn't the trade-off driver for the TTL decision"
}
]
}])ask_user_question(questions: [{
header: "Edge Cases",
question: "What happens when a user uninstalls the GitHub App? How does our system respond?",
options: [
{
label: "Hard delete record",
description: "We soft delete instead to preserve audit trail and cascading session cleanup"
},
{
label: "Soft delete + audit",
description: "Correct — we soft delete the installation record and cascade to invalidate all user sessions, preserving the audit trail"
},
{
label: "Nothing, tokens work",
description: "Incorrect — when uninstalled, tokens immediately stop working. We must clean up sessions"
}
]
}])ask_user_question(questions: [{
header: "Code Reading",
question: "In the token refresh logic, why do we compare the cached token's expiry against a 55-minute threshold instead of the full 60 minutes?",
options: [
{
label: "5-min safety buffer",
description: "Correct! The 5-minute buffer prevents edge-case race conditions where a token expires between the cache check and the API call"
},
{
label: "Rate limit overhead",
description: "Rate limits are 5000/hr — the buffer isn't about rate limits, it's about preventing stale token usage"
},
{
label: "Clock drift compensation",
description: "While clock drift is a real concern, the primary reason is preventing token expiry during the request window"
}
]
}])For a typical 4-6 question quiz, the flow looks like:
1. Agent: "Let me quiz you on the GitHub OAuth implementation.
I'll ask one question at a time and give feedback after each."
2. ask_user_question → Q1 (Architecture question)
User answers
Agent feedback: "Correct! ... See auth/github/installation.ts:42"
3. ask_user_question → Q2 (Implementation question)
User answers
Agent feedback: "Almost — the cache TTL is 55 minutes, not 50..."
4. ask_user_question → Q3 (Edge case question)
User answers
Agent feedback: "Right!..."
5. ask_user_question → Q4 (Trade-off question)
User answers
Agent feedback: "Good analysis..."
6. Agent: "Here's your summary:
- Strong on architecture decisions
- Review token caching details (auth/github/token-cache.ts)
- PR description material from Q1 and Q4..."ask_user_question with a single-question array. Never batch questions.A good quiz:
ask_user_question for focused, one-at-a-time questioning# Quiz Results: [Feature Name]
## Strong Areas
- [Area 1]: Solid understanding of X
- [Area 2]: Clear grasp of Y
## Areas to Review
- [Area 1]: Unclear on Z, review [file.ts:123]
- [Area 2]: Missing context on W, see [commit abc123]
## Recommended Actions
- [ ] Review [specific code section]
- [ ] Read [specific documentation]
- [ ] Discuss [specific decision] with team
## PR Description Material
Based on your answers, include in PR:
- [Key point 1 from quiz]
- [Key point 2 from quiz]
- [Trade-off explanation from Qx]The quiz becomes your PR description outline.
© jellydn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/quiz-me of jellydn/my-ai-tools.
Open the folder on GitHubat commit 62c9227
Quiz Me next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Quiz Me this skilljellydn/my-ai-tools | 123 | — | ~2.6k | Automated safety check: Pass | MIT | |
| DeepTutor CLIHKUDS/DeepTutor | 41k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch | 67k | — | ~2k | Automated safety check: Pass | MIT | |
| Codebase to Coursezarazhangrui/codebase-to-course | 5.7k | — | ~4.4k | Automated safety check: Pass | None | |
| AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch | 67k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Scholar EvaluationK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~2.9k | Automated safety check: Notes | MIT |
HKUDS/DeepTutor
Teaches the agent to set up and run DeepTutor from the command line: chat and capabilities, knowledge bases, partners, memory, sessions, notebooks and the server or Web app.
rohitg00/ai-engineering-from-scratch
Runs a 10-question quiz across five areas to place a learner in the AI Engineering from Scratch curriculum, so they skip what they already know.
zarazhangrui/codebase-to-course
Turns a codebase into an interactive single-page HTML course for non-technical learners, with scroll modules, animated diagrams, quizzes and plain-English code translations.
rohitg00/ai-engineering-from-scratch
Quizzes you on a completed phase of the AI Engineering from Scratch course, taking a phase number or name and mapping it to that phase's directory.
K-Dense-AI/claude-scientific-writer
Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls.
guanyang/open-agent-hub
This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and…
jellydn/my-ai-tools
A skill your agent uses when monitoring an open GitHub PR for CI failures, review feedback, mergeability, and safe retries or fixes.
jellydn/my-ai-tools
Posts a concise visual outline as a GitHub pull request comment.
jellydn/my-ai-tools
Manage project knowledge with qmd — captures learnings, decisions, and conventions
jellydn/my-ai-tools
Generate Product Requirements Documents from feature ideas — plans specs and requirements
jellydn/my-ai-tools
Build an interactive report or experiment when the user asks to explore model capabilities.
jellydn/my-ai-tools
Fix PR review comments by implementing requested changes. An agent skill from jellydn/my-ai-tools.
Categories
Verify understanding after implementation with targeted quizzes. Quiz Me is an agent skill from jellydn/my-ai-tools.
Quiz Me fits situations like: tasks that involve Quizzes and assessments.
Run `npx skills add jellydn/my-ai-tools --skill quiz-me -a claude-code`. Or copy the skill folder (skills/quiz-me in jellydn/my-ai-tools) into .claude/skills/quiz-me in your project. Claude Code loads it when a task matches its description.
Run `npx skills add jellydn/my-ai-tools --skill quiz-me -a codex`. Or copy the skill folder (skills/quiz-me in jellydn/my-ai-tools) into .agents/skills/quiz-me in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jellydn/my-ai-tools --skill quiz-me -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/quiz-me, .gemini/skills/quiz-me, .github/skills/quiz-me and .opencode/skills/quiz-me in your project.
SKILL.md names no scripts, command-line tools or credentials: Quiz Me is instructions for the agent only. Compatibility (from SKILL.md): cline, claude, opencode, amp, codex, gemini, cursor, pi.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Quiz Me is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Quiz Me: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 67k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
jellydn (a GitHub user) maintains it in jellydn/my-ai-tools, which has 123 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 10, 2026.
Source: jellydn/my-ai-tools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.