Stats
Actions
Tags
From learning-loop
Manages a knowledge-management workflow from idea capture through research to permanent notes. Run `/learning-loop:help` or ask what the loop can do.
How this skill is triggered — by the user, by Claude, or both
Slash command
/learning-loop:helpThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Present the guide below when the user runs `/learning-loop:help` or asks what the learning loop can do. Adapt the level of detail to context: if they seem experienced, lean on the quick reference at the end. If they're new, walk them through the narrative.
Present the guide below when the user runs /learning-loop:help or asks what the learning loop can do. Adapt the level of detail to context: if they seem experienced, lean on the quick reference at the end. If they're new, walk them through the narrative.
/learning-loop:help: show all commandsPresent this guide:
The learning loop turns conversations into lasting knowledge. Ideas start rough, get refined through research, and settle into permanent notes in your vault. Every command serves one of three jobs: bringing ideas in, making them stronger, or keeping things tidy.
Curious about something? Run /learning-loop:discovery.
/learning-loop:discovery "spaced repetition"
It searches your vault for what you already know, researches the web for what you don't, and walks you through the topic interactively. You steer: it digs. At the end, key insights land in your inbox as atomic notes.
Want to just browse without saving anything? Add --surf:
/learning-loop:discovery "spaced repetition" --surf
Other options: --style guided|branch|checkpoint, --tone academic|conversational|persona.
Reading something good? Run /learning-loop:literature.
/learning-loop:literature https://example.com/article
It fetches the source, extracts the core ideas, and writes a literature note to 2-literature/. The source's ideas, captured clean: your commentary goes in separate notes that link back.
Need a quick answer? Run /learning-loop:quick.
/learning-loop:quick "how much have jaguar prices dropped recently?"
It searches your vault and the web in parallel, verifies key claims, and gives you a direct answer in 3-10 sentences. If the answer contains something novel and durable, it auto-captures a note to your inbox. No interactive rounds, no steering -- just a fast, sourced answer.
Quick capture mid-conversation? Run /learning-loop:quick-note.
/learning-loop:quick-note "insight as title"
It grabs the insight, finds vault links, and drops an atomic note in 0-inbox/. No preview, no approval: just a one-line confirmation and back to work. Run it with no args and it infers the insight from conversation context.
Finishing a work session? Run /learning-loop:reflect.
It reviews what happened in the conversation, extracts anything worth keeping, and routes it to the right place: behavioral stuff to auto-memory, knowledge to your vault. This is how the loop closes. Without it, insights from the session evaporate.
Notes land in 0-inbox/ as rough captures. Two commands move them forward:
/learning-loop:deepen takes a single note and strengthens it. It reads the note, scores its maturity, researches what's missing, rewrites it in vault voice, and promotes it when ready. Shallow notes get heavy research; deep notes get a light touch.
/learning-loop:deepen "note name"
/learning-loop:verify assesses both quality and source integrity. It scores each note on depth, sourcing, linking, and voice, then checks that cited sources are real and claims match what they say. Use it to find where to invest /deepen effort and catch fabricated references.
/learning-loop:verify inbox
/learning-loop:verify "distributed systems"
/learning-loop:verify permanent
/learning-loop:gaps is the scientific method applied to your vault. It doesn't just find what's missing: it challenges what you believe. For any topic, it extracts your vault's claims, searches for counterarguments and criticisms, and surfaces tensions, absences, and thin ice. Findings are framed as questions, not verdicts. You decide what they mean.
/learning-loop:gaps "theanine"
/learning-loop:gaps
/learning-loop:gaps --sweep
Focused mode analyses a topic. No-argument mode auto-picks your densest unchallenged knowledge cluster. Sweep mode runs across the entire vault. Depth scales to note maturity: permanent notes get deep scrutiny. Counterpoint notes land in your inbox like any other knowledge, tagged #counterpoint and linked back to the challenged note.
/learning-loop:rewrite is cross-store correction. When a belief turns out to be wrong, the old version sits in three places: vault notes, auto-memory, and episodic history. This skill edits all three coherently. Vault and auto-memory are rewritten or archived; episodic history is annotated via a supersession record so future retrievals carry the correction inline.
/learning-loop:rewrite "old pattern" "new pattern"
/learning-loop:rewrite "old pattern" "new pattern" "explicit reason"
/learning-loop:rewrite
No args: infers the change from recent conversation context. With args: searches for every note, preference, and past conversation that encodes the old pattern, presents an impact map for triage, then executes the approved changes and records a supersession.
/learning-loop:inbox is batch triage. It reads every note in your inbox, clusters them by topic, and recommends actions: promote, merge, deepen, or delete. Promotions happen automatically. Merges and deletes wait for your approval.
/learning-loop:health is your vault's status check. It scans for ghost duplicates, near-duplicate pairs, orphan notes, stale inbox entries, embedding gaps, and broken links. Light mode (default) gives you counts in seconds. --deep launches full analysis. --auto fixes the safe stuff without asking.
/learning-loop:health
/learning-loop:health --deep
/learning-loop:health --deep --auto
If you've enabled the librarian (see below), /health also shows pending librarian observations in the dashboard. Run /health --librarian to review and act on them.
The vault librarian runs in the background when ll-watch is active, using Gemma 4 E2B locally via ollama. It wanders the vault autonomously, finding orphan notes that should be linked, suggesting tags for under-tagged notes, flagging topic-style titles, flagging duplicates against near-neighbours, and marking potentially stale claims. It queues observations; you review them with /health --librarian.
/learning-loop:health --librarian
Phase 1 presents link suggestions, tag suggestions, voice flags, and duplicate flags for approval. Phase 2 hands staleness suspects to Claude for deep investigation. Enable via /init Phase 7 (requires ollama + 16GB+ RAM). No API calls, completely local and free.
/learning-loop:refresh is pure recall: what does your vault already hold on a topic? No research, no new notes. Just surfaces what's there, organized by knowledge depth, and suggests what to do next.
/learning-loop:refresh "authentication patterns"
/learning-loop:ingest pulls bulk context from external systems into your vault and auto-memory. Three sources supported:
/learning-loop:ingest linear # my assigned tickets
/learning-loop:ingest linear "AI Assistant" # tickets from a specific project
/learning-loop:ingest repo ~/dev/kinso/monorepo # scan a repo
/learning-loop:ingest repo ~/dev/foo --deep # force the parallel deep fan-out
/learning-loop:ingest context # paste text to extract
It fetches the data, extracts atomic insights, previews them for your confirmation, then routes project-state to auto-memory and durable insights to 0-inbox/. Run it when starting a new project, onboarding to a codebase, or pulling in work context from Linear.
For repo, a Haiku gate decides between a single-pass scan and a 5-wide parallel fan-out (4 deep mappers covering stack/architecture/conventions/domain + 1 state sidecar) that stages structured docs at <vault>/_ingested-repos/<slug>/ and synthesizes them into atomic notes. Pass --deep to skip the gate and force the parallel path.
first time → /learning-loop:init → vault path, persona, folder structure
federation → /learning-loop:federation → identity, token redeem, visibility, sync test
external → /learning-loop:ingest → auto-memory + inbox notes
curiosity → /learning-loop:discovery → inbox notes → /learning-loop:deepen → permanent notes
question → /learning-loop:quick → answer + auto-capture if novel
reading → /learning-loop:literature → literature notes
mid-work → /learning-loop:quick-note → inbox note (don't break flow)
sessions → /learning-loop:reflect → inbox + auto-memory
cleanup → /learning-loop:inbox → promote, merge, or deepen
recall → /learning-loop:refresh → see what you know
quality → /learning-loop:verify → score quality + check sources → /learning-loop:deepen
challenge → /learning-loop:gaps → counterpoints + rewrites + /deepen queue
correction → /learning-loop:rewrite → vault + auto-memory + episodic supersession
hygiene → /learning-loop:health → diagnose → route to /inbox, /verify, /deepen
librarian → /learning-loop:health --librarian → approve links + tags, review duplicates and staleness
If you've configured federation via /learning-loop:federation (also reachable from /learning-loop:init), search results automatically include notes from connected peers. Peer results appear with a peer:<name>/ prefix in their path.
node vault-search.mjs sync refreshes peer indexes from the hubguide/federation.md| Command | What it does |
|---|---|
/learning-loop:init | First-time setup: vault path, persona, folder structure |
/learning-loop:federation | Configure federation: identity, token redeem, peers, visibility, sync |
/learning-loop:discovery "topic" | Interactive research journey: explore something new or go deeper |
/learning-loop:quick "question" | Fast verified answer: vault + web, auto-captures if novel |
/learning-loop:literature <URL> | Capture an external source as a literature note |
/learning-loop:quick-note [title] [body] | Quick capture to inbox: no args infers from context |
/learning-loop:reflect | End-of-session: extract and persist learnings |
/learning-loop:deepen <note> | Strengthen a single note with research |
/learning-loop:verify [scope] | Score quality + verify sources, find what needs work |
/learning-loop:inbox | Batch triage inbox notes |
/learning-loop:refresh "topic" | Surface what you already know: no research |
/learning-loop:gaps "topic" | Challenge vault knowledge: find tensions, thin ice, and missing perspectives |
/learning-loop:rewrite "old" "new" [reason] | Cross-store correction: vault + auto-memory + episodic supersession |
/learning-loop:ingest [linear|repo|context] [--deep] | Pull external context into vault + auto-memory; --deep forces parallel deep mappers on repo |
/learning-loop:seed [--for-job] [--types a,b] [--out <dir>] | Build a portable starter slice for a fresh instance (ramp up at a new job / second machine) |
/learning-loop:harvest [--all] [--out <dir>] | Collect opt-in portable: true, IP-scrubbed insights from this instance to carry back home |
/learning-loop:health [--deep] [--auto] | Vault hygiene dashboard: ghost dupes, orphans, stale notes, broken links |
/learning-loop:health --librarian | Review librarian queue: approve link/tag suggestions, acknowledge voice flags, resolve duplicate flags, investigate staleness |
/learning-loop:dream | Consolidate auto-memory between sessions |
/learning-loop:diagram "concept" | Generate Excalidraw diagram for vault |
/learning-loop:help | This guide |
npx claudepluginhub robinslange/learning-loop --plugin learning-loopDelivers contextual guidance on commands, active skills, and vault-state suggestions in narrative, contextual, or compact modes. Supports /help with args for compact or skill-specific help.
Captures insights as markdown files, searches prior learnings, and promotes patterns to CLAUDE.md using tiered backends (local, qmd, agent-fs) for knowledge across projects.
Searches vault notes, episodic memory, and literature to surface what you already know about a topic. Use before discovery or when returning to a project.