Memory Organize

You are maintaining a role's persistent memory system. Keep it healthy, accurate, and useful.

Memory Layers

consolidated.md  ← L2: deduplicated learnings + preferences (main)
pending.md       ← Pending: auto-extracted, awaiting verification
daily/*.md       ← L1: raw session logs

Tools Available

Tool	Purpose
role_exec({ op: "list" })	List all memories, detect structural issues
role_exec({ op: "consolidate" })	Rule-based dedup: exact + Jaccard similarity. Safe, never deletes unique entries.
role_exec({ op: "repair" })	Fix markdown structure issues
role_exec({ op: "llm_tidy" })	LLM-guided: rewrites verbose, detects contradictions, suggests deletions
role_search({ query: "..." })	Search with auto-reinforce (≥0.5 → used+1)
role_exec({ op: "reinforce", args: { content: "..." } })	Increment [Nx] usage count
role_exec({ op: "delete_learning", args: { content: "..." } })	Remove stale entry
role_exec({ op: "update_learning", args: { id/query, content } })	Rewrite entry
role_exec({ op: "role_info" })	List role paths only; use memory ops for memory changes

Process

Step 1: Assess

role_exec({ op: "list" })

Look for:

• Parse issues — structural problems
• High [0x] count — many unverified = noise
• Long entries — should be <120 chars

Step 2: Repair (if needed)

role_exec({ op: "repair" })

Fixes: malformed headings, missing sections, stray lines. Safe, rule-based.

Step 3: Consolidate (dedup)

role_exec({ op: "consolidate" })

Rule-based, NOT LLM:

• Exact text match → keep highest [Nx]
• Jaccard token similarity (config threshold) → merge, keep highest
• Rewrites the canonical section layout in consolidated.md
• Updates # Last Consolidated date

consolidate does not magically upgrade [0x] to Normal. Priority still depends on usage (reinforce / auto-reinforce during search).

Step 4: LLM Tidy (deep cleanup)

role_exec({ op: "llm_tidy" })

LLM produces a plan:

• Rewrite verbose entries → concise
• Detect contradictions between entries
• Suggest deletions for stale entries
• Add new entries derived from synthesis

Step 5: Pending Management

Check memory/pending.md: Use the standard file-read tool on the injected role's memory/pending.md path only when pending details are needed.

• [○] pending → if relevant to current work, search to auto-promote (score ≥0.5)
• [✓] already promoted
• [✗] discarded (7-day auto-expiry)

Pending entries that survive 7+ days without use are auto-expired. Manual promotion via search.

Step 6: Reinforce Active Memories

For insights used in this session:

role_exec({ op: "reinforce", args: { content: "<text>" } })

Moves memories toward High Priority [3x]+.

Step 7: Remove Stale Entries

For provably outdated entries:

role_exec({ op: "delete_learning", args: { content: "<text>" } })

Rules:

• Only delete if provably wrong or no longer relevant
• Prefer update_learning over delete when in doubt
• Never delete preferences without user confirmation

Maintenance Report

After organizing:

Memory Organize Report — <role>
- Before: X learnings, Y preferences
- After:  X' learnings, Y' preferences
- Repaired: yes/no
- Consolidate: removed N duplicates
- LLM tidy: rewrote N, deleted N, added N
- Pending: M promoted, K expired

Maintenance Frequency

Frequency	Actions
Per session	auto-extract → pending (automatic)
On request	consolidate (safe dedup)
Weekly	repair + consolidate + reinforce
Monthly / heavy	llm_tidy + manual review + pending cleanup

Operation Rules

• Be conservative: When in doubt, leave entries alone. Better to under-clean than lose useful memories.
• consolidate is safe: only deduplicates, never deletes unique entries.
• llm_tidy is destructive: review the plan before applying.
• Preserve user voice: when updating preferences, keep original phrasing.
• Never fabricate: don't add entries just to make the count look better.