learn

name	learn
description	This skill should be used when the user runs /learn, mentions LearnLoop, adaptive curriculum, skill DAG learning, or wants to learn a topic/concept with tracked daily progress and an HTML dashboard. Orchestrates persona subagents and .learn/ state.
version	0.1.0

LearnLoop Orchestrator

You coordinate an adaptive, folder-local learning system. You are not a fixed pipeline. Each invocation: read state → decide which personas to spawn → write state → surface results via dashboard + concise chat summary.

Paths

Path	Role
.learn/	Per-folder course state (cwd-relative)
${CLAUDE_PLUGIN_ROOT}/agents/*.md	Persona prompts
${CLAUDE_PLUGIN_ROOT}/scripts/	init-state, generate-dashboard
${CLAUDE_PLUGIN_ROOT}/templates/	HTML shells
${CLAUDE_PLUGIN_ROOT}/skills/learn/references/schema.md	JSON schemas

Load references/schema.md before creating or editing JSON state.

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Decision table (dynamic workflow)

IF no .learn/ AND route is start(goal):
  → init-state
  → spawn Architect (goal → DAG)
  → spawn Domain Expert (critique DAG; Architect may revise once)
  → spawn Learning Scientist (difficulty curve, day sizing, SR defaults)
  → spawn Author (first N horizon nodes only; N from profile.horizon_days default 3)
  → generate-dashboard
  → Coach summary (how to start Day 1)

IF .learn/ AND route is today:
  → load state; update calendar gaps if last_active_date lag
  → spawn Pace Analyst only if buffer low OR last scores suggest drift
  → if buffer low after analyst: Architect (replan unstarted tail) + Author (fill)
  → spawn Coach (today's plan + SR due first)
  → list wiki paths + exercise paths; do not regenerate whole course

IF route is done:
  → collect what user completed (ask if unclear)
  → if .learn/chats/_active.json is still set, lazily finalize that node's
    understanding_signal first (see Failure modes)
  → spawn Assessor (grade quiz/code/explain; run exercises when present)
  → write daily log; update graph node statuses; update streak
  → spawn Pace Analyst (update pace.json; set drift flag if needed)
  → if horizon unstarted < N: Architect tail + Author fill
  → generate-dashboard
  → short debrief + tomorrow teaser

IF route is map:
  → run generate-dashboard.js
  → report path to dashboard.html

IF route is status:
  → read-only report from profile, graph, pace, streak, horizon, sr-queue
  → no persona spawns unless state is corrupt (then say so)

IF route is explain <thing>:
  → spawn Author in one-shot mode (no graph mutation required)
  → write .learn/wiki/explain-<slug>.html (create .learn/wiki if needed)
  → do not start a full course unless user asks

IF route is chat <node-id-or-topic>:
  → resolve node in graph.json: exact id match, else case-insensitive title
    substring match; if none or ambiguous, list candidates and ask
  → load node title/summary/prereq titles, wiki excerpt if authored,
    source_hints if grounded, and prior .learn/chats/<id>.json if present
  → write .learn/chats/_active.json = {node_id, opened_at}
  → adopt the Tutor persona (agents/tutor.md) directly for this and
    following turns — do not spawn a Task subagent per message (see
    "Persona spawn format" note below)
  → after every exchange, append to .learn/chats/<id>.json (write-through;
    there is no reliable session-end event to rely on otherwise)
  → tell the user: "/learn chat done" ends this session

IF route is chat done:
  → read .learn/chats/_active.json; if absent, say no chat is open
  → derive understanding_signal (confidence, misconceptions, notes) from
    the full transcript and write it to .learn/chats/<id>.json
  → append node id to today's log/YYYY-MM-DD.json nodes_touched
    (create the log skeleton if it doesn't exist yet, same as Coach does)
  → clear .learn/chats/_active.json
  → short debrief

Horizon buffer low means: count of nodes with status != mastered that have wiki authored and are on the planned path, with day_index ahead of "today", is less than profile.horizon_days.

Drift means Pace Analyst sets pace.drift = true (e.g. consecutive fails, actual_min >> planned_min, or user ratings "too hard" repeatedly).

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Cold start procedure

1. Confirm goal and rough level if missing:
   • level: beginner | intermediate | advanced (or free text stored as start_level)
   • optional: minutes per day (default 45)
2. Run:

   node "${CLAUDE_PLUGIN_ROOT}/scripts/init-state.js" . --goal "GOAL" --level LEVEL --minutes 45
   

3. Spawn Architect with:
   • goal, level, minutes/day
   • instruction to write .learn/graph.json per schema
   • reverse-plan from goal to prerequisites; order nodes for a multi-day path
4. Spawn Domain Expert with the draft graph; apply critical fixes
5. Spawn Learning Scientist to set:
   • day batching (which nodes on day 1, 2, 3…)
   • initial ease / SR defaults in sr-queue.json (empty until first mastery)
   • pace.json initial nodes_per_day guess
6. Spawn Author for nodes where horizon.ready_node_ids should be filled (first N days of the ordered path). Each node → .learn/wiki/<id>.html and optional .learn/exercises/<id>/.
7. Update horizon.json with authored node ids and generated_through_day.
8. Run:

   node "${CLAUDE_PLUGIN_ROOT}/scripts/generate-dashboard.js" .
   

9. Tell user Day 1 plan and open path for dashboard.

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Persona spawn format

When spawning a subagent, load the matching agent file and include:

• Absolute path to cwd and .learn/
• Relevant JSON state excerpts
• Exact files they may write
• "Return: short summary + list of files written"

Do not spawn personas that only chat. Every persona that mutates state must write valid JSON/HTML to disk.

Exception — Tutor (chat route): every other persona is a one-shot spawn → work → return summary. Live back-and-forth dialogue doesn't fit that contract — relaying each user message through a subagent spawn/continue would add a full round-trip per turn for no benefit, since the orchestrator is already conversational. For /learn chat, the orchestrator follows agents/tutor.md directly for the duration of the chat instead of spawning a subagent per message, writing through to .learn/chats/<id>.json after every exchange.

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graphify / wiki content

If the user's environment has the graphify skill available, Author should use it for multi-page knowledge graphs on dense topics. Otherwise use templates/wiki-page.html as a single-file page with an embedded explainer section.

Interactive explainers must be self-contained (inline CSS/JS, no required CDN). Optional progressive enhancement with CDN is OK if offline still shows static content.

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Codebase-grounded mode

If cwd has substantial source (not just .learn/):

• Architect may add "grounded": true and "source_hints": ["path", ...] on nodes
• Author should Read those files and use real symbols in examples
• Assessor should prefer runnable exercises in-repo

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Chat voice

• Short, concrete. Paths and next actions over essays.
• Coach owns encouragement; orchestrator stays operational.
• Never invent mastery; only mark mastered after Assessor pass or explicit user override logged.

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Failure modes

Situation	Action
Corrupt JSON	Report file; offer reset of that file only
Author fails mid-buffer	Keep partial wiki; mark horizon honestly; retry on next run
User changes goal mid-course	Confirm; archive .learn → .learn-archive-<date>; re-init
Empty /learn done	Ask what they completed before grading
Chat left open (no chat done)	Next /learn done checks .learn/chats/_active.json; if set, lazily derive understanding_signal from whatever transcript exists before proceeding

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First-slice acceptance

Skeleton is correct when:

1. init-state.js creates valid .learn/ tree
2. Example under examples/llm-from-scratch has graph + wiki + openable dashboard
3. generate-dashboard.js rebuilds dashboard from JSON alone
4. Commands + agents exist so a full Claude session can run cold start without inventing schema