learn-profile
Interview the learner and write .alvar/LEARNER.md so one teacher can fit one mind — pace, voice, solid ground, and how they want…
Map the edge of the learner's understanding with a short graded quiz. Start broad, then binary-search every strand a lesson will need. Use when the user runs /probe or /skill:probe, or says quiz me, pretest, what do I already know, diagnose my understanding, probe me, or find
$ npx -y skills add vasanthsreeram/alvarmethod --skill probe --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/probeContext preview
The summary Claude sees to decide when to auto-load this skill.
Map the edge of the learner's understanding with a short graded quiz. Start broad, then binary-search every strand a lesson will need. Use when the user runs /probe or /skill:probe, or says quiz me, pretest, what do I already know, diagnose my understanding, probe me, or find
name: probe description: > Map the edge of the learner's understanding with a short graded quiz. Start broad, then binary-search every strand a lesson will need. Use when the user runs /probe or /skill:probe, or says quiz me, pretest, what do I already know, diagnose my understanding, probe me, or find the edge before teaching. license: MIT metadata: author: vasanthsreeram version: "1.0" source: "Eero Alvar — How I Use AI to Learn Things https://youtu.be/kzcI5F4tGiU"
Measure first. Do not teach during a probe except a one-line correction after they answer.
**Quiz UI is mandatory.** Read [../teach/references/quiz-ui.md](../teach/references/quiz-ui.md) and call that tool. Never print A/B/C/D in the chat.
Produce `.alvar/maps/<slug>.md` that labels every dependency strand:
`known` | `edge` | `unknown` | `blocked`
`blocked` = they said they don't know, or the question needs a tool you don't have.
One right answer. No trick options that hinge on wording. Put **I don't know** as an option. Do not mark the correct choice recommended.
| result | status move | |--------|-------------| | correct + sound reason | `known` | | correct, thin reason | `edge` | | wrong, near-miss | `edge` | | wrong, foundation missing | `unknown` | | I don't know | `blocked` or `unknown` |
After each batch, update the map. Stop when the upcoming lesson's dependencies are all labeled — not when you have asked "enough questions."
# Map — <goal> Updated: <ISO date> Goal: <one sentence> ## Strands | strand | status | evidence | |--------|--------|----------| | … | known | Q2 + reason | ## Quiz log - Q1 [strand] <letter> — correct|wrong|idk — <five words>
Show the table. Say which strands a teacher should start from. Do not start the lesson unless they ask.
One-to-one AI teaching skills (Alvar method) for Codex, Claude Code, Grok, Pi, and OpenCode
Interview the learner and write .alvar/LEARNER.md so one teacher can fit one mind — pace, voice, solid ground, and how they want…
Fact-check a teaching claim against sources so the learner can trust the single interface. Use when the user runs /learn-verify…
Make a teaching diagram (usually SVG) for one idea, look at it, and fix it until the picture actually shows the claim. Use when…
One-to-one tutor using the Alvar method — probe the edge of this mind, plan a mermaid DAG, then teach one reasoning step at a…