obsidian-learn

Use the obsidian-second-brain skill. Execute /obsidian-learn $ARGUMENTS:

The optional argument is a scope: recent (last 30 days, default), all (entire vault), or a topic name. Scope limits the review LIST only - the Stale and Superseded classifications always scan the entire vault, since a 6-months-old learning cannot appear inside a 30-day window.

1. Read _CLAUDE.md first if it exists in the vault root

2. Read index.md and recent operation log for vault context (if Logs/ exists: read the last 2-3 Logs/YYYY-MM-DD.md files; otherwise read log.md)

3. Spawn parallel subagents to gather learnings:

   • Lessons agent: scan all daily notes for "Lesson learned" sections, "What didn't" sections, evening review insights
   • Decisions agent: read all ADRs in the decisions folder (resolved per references/folder-map.md - wiki-style wiki/decisions/, Obsidian-style Knowledge/ with ADR- filename prefix) - extract the rationale and outcome of each
   • Reports agent: read recent emerge/synthesize/connect/challenge reports in the concepts folder (resolved per references/folder-map.md) - the auto-generated pattern reports
   • Mistakes agent: scan dev logs and daily notes for "what didn't work", "wasted time on", "next time", "lesson", phrases indicating learning from failure
   • Wins agent: scan for patterns that worked - "this saved time", "this approach worked", recurring success patterns

4. For each learning found, classify:

   • Active: still relevant, recurring, reinforced by recent activity
   • Stale: 6+ months old with no recent reinforcement, or contradicted by newer evidence
   • Superseded: explicitly replaced by a newer ADR or pattern
   • Promoted: appeared 3+ times - should become a permanent rule in _CLAUDE.md

5. Generate the Learnings Report:

   Active Learnings (still applies)

   • List learnings reinforced in the last 90 days
   • Cite the original source and most recent reinforcement

   Stale Learnings (consider archiving)

   • List learnings with no recent reinforcement
   • Suggest: keep, archive, or convert to history note

   Superseded Learnings (already replaced)

   • Old position → New position with ADR reference

   Promotion Candidates (appeared 3+ times)

   • Learnings strong enough to become permanent rules in _CLAUDE.md
   • Suggest exact wording for the operating manual

   Top 5 Lessons of the Period

   • Most impactful learnings ranked by frequency × recency × consequence

6. Save the report to the concepts folder (resolved per references/folder-map.md) as YYYY-MM-DD - Learnings Review.md (type: learnings-review, schema in references/ai-first-rules.md)

7. Append to the operation log: if Logs/ exists write **HH:MM** - learn | X active, Y stale, Z superseded, N promotion candidates to Logs/YYYY-MM-DD.md; otherwise append ## [YYYY-MM-DD] learn | X active, Y stale, Z superseded, N promotion candidates to log.md

8. Update today's daily note with a brief summary

9. Offer to:

   • Promote candidates to _CLAUDE.md (with user confirmation)
   • Archive stale learnings (with user confirmation)
   • Export top 5 as a shareable markdown for content/journaling

Lessons that aren't reviewed don't compound. This command turns scattered notes into a living rulebook.

AI-first rule: Every note created or updated by this command MUST follow references/ai-first-rules.md - ## For future agent preamble, rich frontmatter (type, date, tags, ai-first: true, plus type-specific fields), recency markers per external claim, mandatory [[wikilinks]] for every person/project/concept referenced, sources preserved verbatim with URLs inline, and confidence levels where applicable. If that path does not resolve from your working directory, search upward for it; if you still cannot read it, say so before writing rather than producing a note that silently skips the rule. The vault is for future agent retrieval - not human reading.