Level Up
Run a 7-question adaptive assessment to map what the user knows and doesn't, relevant to the current project. The output is two files future agents rely on.
Files (repo-relative)
notes/learning/david-knowledge.md — verbatim Q&A pairs + ratings, one section per question, rounds appended.notes/learning/LEARNING-PLAN.md — one concise bullet per genuine gap found.
State-check first: read both files in full if they exist. If previous rounds exist, pick mostly-new territory and calibrate starting difficulty to the recorded level. If missing, create the folder and both files (plan starts as just a header).
Question rules
- 7 questions, strictly one at a time, plain text — never the questions UI.
- Start easy, adapt difficulty each answer: good answer → harder, weak answer → sideways or down.
- Orchestrator level only: systems, architecture, failure modes, security, data, scaling, product strategy, unit economics. NEVER syntax or code trivia — the user architects via AI agents, they don't write code.
- Anchor questions in the current project's real stack and features. When a question touches real code, read it and show the actual snippet when teaching.
- Cover different territory across rounds (e.g. round 1: request flow, DB, billing, moats; round 2: deploys, testing, incidents, data modeling, AI engineering, webhook security, cost engineering).
After every single answer
- Rate honestly 1-10. No flattery — the user wants calibration, not comfort.
- Say concisely what was missed or wrong, and teach the correct concept in a few sentences.
- Immediately save the verbatim answer + rating + gap notes to
david-knowledge.md.
- If a genuine gap surfaced, append one concise bullet to
LEARNING-PLAN.md. Skip minor misses.
- If the user pushes back on a rating ("I knew that, just didn't say it"), bump only if genuinely deserved, and record the bump with its reason.
- When the user says he has since learned a plan item, mark its bullet: strikethrough +
✓ learned YYYY-MM-DD.
After question 7
Append a final summary to david-knowledge.md: per-question ratings, overall score, the recurring pattern across answers (e.g. "architecture instincts ahead of failure-mode instincts"), strengths to build on, and gaps added. Give the user the same summary in chat, concise.
1---2name: level-up3description: Gauge the user's technical + product knowledge through 7 adaptive questions, log verbatim answers with honest ratings, and grow a learning plan from the gaps found. Use when the user says "level up", "level-up session", "quiz me", "gauge my knowledge", or wants a new assessment round. Differentiator: this finds and maps gaps; the `teach` skill delivers lessons on them.4---5
6# Level Up
7
8Run a 7-question adaptive assessment to map what the user knows and doesn't, relevant to the current project. The output is two files future agents rely on.
9
10## Files (repo-relative)
11
12- `notes/learning/david-knowledge.md` — verbatim Q&A pairs + ratings, one section per question, rounds appended.
13- `notes/learning/LEARNING-PLAN.md` — one concise bullet per genuine gap found.
14
15State-check first: read both files in full if they exist. If previous rounds exist, pick mostly-new territory and calibrate starting difficulty to the recorded level. If missing, create the folder and both files (plan starts as just a header).
16
17## Question rules
18
19- 7 questions, strictly one at a time, plain text — never the questions UI.
20- Start easy, adapt difficulty each answer: good answer → harder, weak answer → sideways or down.
21- Orchestrator level only: systems, architecture, failure modes, security, data, scaling, product strategy, unit economics. NEVER syntax or code trivia — the user architects via AI agents, they don't write code.
22- Anchor questions in the current project's real stack and features. When a question touches real code, read it and show the actual snippet when teaching.
23- Cover different territory across rounds (e.g. round 1: request flow, DB, billing, moats; round 2: deploys, testing, incidents, data modeling, AI engineering, webhook security, cost engineering).
24
25## After every single answer
26
271. Rate honestly 1-10. No flattery — the user wants calibration, not comfort.
282. Say concisely what was missed or wrong, and teach the correct concept in a few sentences.
293. Immediately save the verbatim answer + rating + gap notes to `david-knowledge.md`.
304. If a genuine gap surfaced, append one concise bullet to `LEARNING-PLAN.md`. Skip minor misses.
315. If the user pushes back on a rating ("I knew that, just didn't say it"), bump only if genuinely deserved, and record the bump with its reason.
326. When the user says he has since learned a plan item, mark its bullet: strikethrough + `✓ learned YYYY-MM-DD`.
33
34## After question 7
35
36Append a final summary to `david-knowledge.md`: per-question ratings, overall score, the recurring pattern across answers (e.g. "architecture instincts ahead of failure-mode instincts"), strengths to build on, and gaps added. Give the user the same summary in chat, concise.
Solizardking