Borrowing it
Nothing to install: this file belongs to levi-qiao/obsidian-llm-wiki. 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/levi-qiao/obsidian-llm-wiki/main/.claude/skills/tutor/SKILL.mdgit clone --depth 1 https://github.com/levi-qiao/obsidian-llm-wikiWrote 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/levi-qiao/obsidian-llm-wiki/tutor)<a href="https://agentmods.dev/skills/levi-qiao/obsidian-llm-wiki/tutor"><img src="https://agentmods.dev/badge/skills/levi-qiao/obsidian-llm-wiki/tutor/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/levi-qiao/obsidian-llm-wiki/tutor"><img src="https://agentmods.dev/badge/skills/levi-qiao/obsidian-llm-wiki/tutor.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 | $0.00097 | $0.01403 |
| Opus 5.5 | $0.00039 | $0.00561 |
| Sonnet 5.5 | $0.00019 | $0.00281 |
| Haiku 4.5 | $0.00010 | $0.00140 |
Grade A, and why
tutor 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.
This is a copy
100% identical to tutor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tutor Skill
Quiz-based tutor that tracks what the user knows and doesn't know at the concept level. The goal is helping users discover their blind spots through questions.
File Structure
StudyVault/
├── *dashboard* ← Compact overview: proficiency table + stats
└── concepts/
├── {area-name}.md ← Per-area concept tracking (attempts, status, error notes)
└── ...
- Dashboard: Only aggregated numbers. Links to concept files. Stays small forever.
- Concept files: One per area. Tracks each concept with attempts, correct count, date, status, and error notes. Grows proportionally to unique concepts tested (bounded).
Workflow
Phase 0: Detect Language
Detect user's language from their message → {LANG}. All output and file content in {LANG}.
Phase 1: Discover Vault
- Glob
**/StudyVault/in project - List section directories
- Glob
**/StudyVault/*dashboard*to find dashboard - If found, read it. Preserve existing file path regardless of language.
- If not found, create from template (see Dashboard Template below)
If no StudyVault exists, inform user and stop.
Phase 2: Ask Session Type
MANDATORY: Use AskUserQuestion to let the user choose what to do. Analyze the dashboard to build context-aware options, then present them.
Read the dashboard proficiency table and build options based on current state:
- If unmeasured areas (⬜) exist → include "Diagnostic" option targeting those areas
- If weak areas (🟥/🟨) exist → include "Drill weak areas" option naming the weakest area(s)
- Always include "Choose a section" option so the user can pick any area
- If all areas are 🟩/🟦 → include "Hard-mode review" option
Present these as an AskUserQuestion with header "Session" and concise descriptions showing which areas each option targets. The user MUST select before proceeding.
Phase 3: Build Questions
- Read markdown files in target section(s)
- If drilling weak area: also read
concepts/{area}.mdto find 🔴 unresolved concepts — rephrase these in new contexts (don't repeat the same question) - Craft exactly 4 questions following
references/quiz-rules.md
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 167 lines · 97 tokens per session scan A 94bd0aec3fbb
tutor is a skill published in the GitHub repository levi-qiao/obsidian-llm-wiki (10 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 1,403 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to tutor, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
llm-wiki
Build and maintain a persistent, interlinked Obsidian-compatible markdown wiki using Karpathy's LLM Wiki pattern. Extension-backed with auto-generated metadata, guardrails, and 14 custom tools (+3 opt-in agent-trajectory tools).
lecture-to-notes
A workflow that turns lecture or conference recordings into organised notes using local transcription and slide extraction. It accepts video and audio files, including MOV, MP4, M4A, MP3, and WAV.
Learn
When the principal is in learning mode (reading a repo, watching a talk, processing a new tool), /learn takes a brief input and produces a structured note in vault/concepts/learning/ with category, modern-stack location, relevance verdict, and reconsideration triggers. Captures the value of every learning sweep…
gh-discussions
A guide for answering GitHub Discussions, a question-and-answer area separate from issue reports. In its Q&A categories, a maintainer can mark a reply as the accepted answer.
brain-onboard
An onboarding workflow that helps another person create and use their own private knowledge store, sometimes called a second brain. It gathers their information with consent, saves it in their own workspace, and demonstrates a first useful result.
notebooklm
Drive NotebookLM over a programmatic CLI with no browser: turn a vault slice into an audio overview, study guide or mind map, and fold the artifacts back into the vault. Browser automation is the fallback if CLI auth breaks. Triggers: "/notebooklm", "make an audio digest of ", "podcast from the vault".