extract-learnings

name	extract-learnings
description	Persist learnings to memory or maintain existing memories. Triggers on "extract learnings", "save this for next time", "remember this pattern", "consolidate memories", "dream", "clean up memories".
allowed-tools	["Read","Write","Edit","Glob","Grep","Bash(python3:*)","Bash(git:*)","Bash(find:*)","Bash(date:*)","Bash(trash:*)","AskUserQuestion","Agent"]

Value Context

Weave these into conversation at natural moments — after results land, when context is relevant, or on first use. One or two per run, not all at once.

• Most users say "remember this" expecting a single note — this skill actually runs two parallel agents (auditor + discoverer) to both capture new knowledge and verify existing memories haven't gone stale.
• The 5-layer memory hierarchy means the right knowledge loads at the right time — universal preferences in L0, project architecture in L1, working notes in L2 — without polluting every session with everything.
• Consolidation ("dream") is the maintenance mode: it prunes outdated and low-value memories, merges overlaps, and clusters overflowing sections so the always-loaded index stays small.
• For teams: memories captured here carry forward to every future session in this project, making onboarding and context-switching dramatically faster.

Memory Hierarchy

Layer	File	Loaded	Purpose
0	~/.claude/CLAUDE.md	Every session, all projects	Universal behavioral preferences
1	<repo>/CLAUDE.md	Every session, this project	Architecture, conventions, gotchas
2	memory/MEMORY.md (project dir)	Every session, agent-managed	Top index: working notes + pointers to clusters/topics
2c	memory/clusters/*.md sub-index	On-demand (after a section overflows)	Section sub-index split off MEMORY.md to cap always-loaded size
3	memory/*.md topic files	On-demand	Detailed reference too long for L2
Meta	Suggest new skill/command	N/A	Repeatable workflow → automation

Placement decision: project-independent preference → L0, project-specific technical → L1, concise working note → L2, detailed reference → L3, repeatable pattern → Meta.

Adaptive clustering: keep MEMORY.md flat until a section exceeds 25 entries; then move that section's pointers into memory/clusters/<section>.md and replace the section in MEMORY.md with a single pointer to it. Only MEMORY.md loads every session, so this caps startup cost while detail stays on-demand.

Pointer Format

Every MEMORY.md and cluster entry is a load trigger, not a label — a future session must recognize its current task in the pointer and open the file. A pointer that only names a file gets ignored, so the index loads without ever being used. Two shapes:

• Triggered (default): **When <task-condition>:** <one-line takeaway>. → [detail](file.md). Use whenever a sharp "when" can be written — e.g. **When compositing a face onto a turned head:** align centers, tight ellipse. → [detail](feedback_composite_align_tight_ellipse.md).
• Always-on: <terse fact>. → [detail](file.md). Only for rules that apply in every relevant session (locked context, global preferences), where a condition would be fake.

Choose triggered if you can name the task that should open the file; choose always-on only when the rule applies regardless of task. Cluster files carry a short always-on block at the top, then a trigger-index — every other entry is a condition pointer. Do not inline full rule bodies in a cluster; it routes to topic files, detail lives in them.

Early Exit Guard

If the user said "remember X" with explicit content already in context — and the request is NOT a consolidation trigger ("consolidate", "dream", "extract learnings", "clean up memories", or triggered from the consolidation nudge):

1. Resolve memory path (see Phase 1 step 1)
2. Read existing memories to check for duplicates and pick the right layer
3. Skip to Phase 3 (Propose & Execute) with that content — no subagents needed

Unified Workflow

Phase 1: Orient (main session)

1. Resolve memory path using Bash (Glob does not expand ~): Bash: find $HOME/.claude/projects -name MEMORY.md -path "*<repo-dir-name>*" 2>/dev/null | head -1 The result is the full path to MEMORY.md (a file). The memory directory is its parent: $(dirname <find-result>). If no result, construct the project key by replacing / with - in the current working directory path (e.g., /home/user/myrepo → -home-user-myrepo), then use $HOME/.claude/projects/<project-key>/memory/MEMORY.md.
   • If MEMORY.md does not exist, create it with # Project Memory header. Note that the Memory Auditor has nothing to audit — in Phase 2, spawn only the Signal Discoverer.

Steps 2-4 are required and run as parallel tool calls.

2. Read MEMORY.md + list topic and cluster files (Glob memory/**/*.md from resolved path)
3. Read both CLAUDE.md files (~/.claude/CLAUDE.md + <repo>/CLAUDE.md) — required for dedup quality; skipping means proposals may duplicate L0/L1 content
4. git log --oneline -20
5. Build context snapshot: summarize existing knowledge + list verification targets (file paths, functions, patterns named in memories)

Phase 2: Gather (2 agents in parallel)

Launch both agent calls in a single message so they run in parallel. Use the Agent tool with:

• Memory Auditor: subagent_type: "claude-memory:memory-auditor". In the prompt, include the context snapshot from Phase 1 — memory file contents, git log output, and verification targets list. Instruct it to surface retirement and merge candidates (SUPERSEDED / REDUNDANT / LOW-VALUE / MERGE), not only factually stale entries — downward pressure is the point of consolidation.

• Signal Discoverer: subagent_type: "claude-memory:signal-discoverer". In the prompt, include existing memory summaries (for dedup) and the project name.

If Phase 1 noted MEMORY.md was just created (no existing memories), skip the Memory Auditor and spawn only the Signal Discoverer.

Both agents require maxTurns ≥ 30 — verify agent frontmatter at plugins/claude-memory/agents/. Agents with low maxTurns exit early and return truncated output that appears non-empty but contains no findings.

Phase 2 is complete when both agents return reports. If either returns empty or clearly truncated (one line, no structured findings), proceed with the other's results — but if the Signal Discoverer fails, also perform a manual fallback: query the 5 most recent session summaries directly from ~/.claude-memory/conversations.db using Bash(python3 -c "import sqlite3; ...") and apply the signal criteria from the Content Quality Rules section below.

Phase 3: Synthesize & Propose (main session)

1. Receive agent reports
2. Deduplicate across reports and against existing memories
3. Rank by impact, limit ADDs to 3-7 candidates; retirements and merges from the auditor are separate and not capped
4. For each candidate: determine target layer, target section, action (ADD / EDIT / MERGE / REMOVE); write every index/cluster pointer in the Pointer Format (condition-first)
5. Read target files, check for duplicates
6. Present proposals:

   ### [ACTION] Learning: <summary>
   **Target:** <file> → <section>
   **Rationale:** <why this layer>
   ```diff
   - <old line>
   + <new line>
   

7. Consolidation pressure (every consolidation run), in order: (a) convert the auditor's SUPERSEDED / REDUNDANT / LOW-VALUE / MERGE findings into concrete REMOVE/MERGE proposals — a run that only adds is a failure mode; (b) ONLY after retirements and merges are settled, run the per-section overflow check on the reduced set — if any MEMORY.md section still exceeds 25 entries, propose migrating it to memory/clusters/<section>.md and replacing the section with a single pointer (adaptive clustering). Clustering never runs before retirement; it must not mask removable entries. When migrating into or editing an existing cluster, extract any inline rule bodies into topic files so the cluster stays a pure trigger-index (Pointer Format) — this reshapes muddled clusters on the next run that touches them. Early-exit captures (no auditor findings) skip (a) and run (b) only if the new entry pushes a section past 25
8. Layer 0 gate — if targeting ~/.claude/CLAUDE.md, warn: "This modifies global instructions loaded in every session across all projects. Confirm?"
9. AskUserQuestion: Approve all / Approve selectively / Reject

Phase 4: Execute

Apply approved edits in this order, so downward pressure actually lands:

1. REMOVE: delete each retired topic file with Bash: trash <path> (trash, never rm — reversible), then remove its pointer from MEMORY.md or the cluster file.
2. MERGE: write the merged entry, then trash the absorbed file(s) and drop their pointers.
3. Cluster: apply approved section → memory/clusters/<section>.md migrations on the post-removal set.
4. ADD / EDIT: apply remaining additions and edits, each pointer in the Pointer Format.

Verify before reporting: after REMOVE/MERGE, run Glob memory/**/*.md from resolved path and confirm each retired file is gone. Never mark a REMOVE/MERGE row "done" unless the file is verified absent — claiming a deletion that did not happen is the exact failure this guards against.

Output summary table:

| Learning | Action | Target | Status |
|----------|--------|--------|--------|

Only if Phase 2 agents ran (not an early-exit capture): write the consolidation marker (required — a skipped marker re-fires the nudge next session). Use Bash, not Write — Write requires a prior Read and cannot create a new file: Bash: date -u +%Y-%m-%dT%H:%M:%SZ > <memory-dir>/.last-consolidation

Phase 4 is complete when all approved edits are applied, every REMOVE/MERGE is verified absent via Glob, the marker is written, and the summary table is presented.

Content Quality Rules

Every candidate must pass: (1) agent would benefit from knowing this in future sessions, (2) condensed to minimum useful form, (3) placed at correct layer, (4) not already captured in target file, (5) stated as reusable principle not session-specific incident.

Pass: commands discovered through trial-and-error, non-obvious gotchas, architectural decisions with rationale, user behavioral corrections, configuration quirks, version milestones.

Fail: information readable from code, generic best practices, one-off bugs without pattern, verbose explanations, duplicates, temporary state, unverified speculation, incidents without generalizable principle.