| name | memory-consolidate |
| description | Consolidate L1 memory atoms into L2 scene blocks and L3 persona. Triggers when the user says "consolidate memories", "build persona", "update persona", "update scenes", "organize memories", or after memory-seed completes. Also triggers via asyncRewake pipeline after N conversation turns. This skill is about ORGANIZING existing memories into higher structures — for creating memories from transcripts use memory-seed instead. |
Memory Consolidation
Analyze L1 atoms and produce L2 scene blocks + L3 persona. You perform all reasoning — no external LLM needed.
Workflow
1. Check current state
tmem status
If zero records exist, tell the user to run memory-seed first and stop.
2. List existing scenes
tmem scenes list
Note existing scene names — you will reuse them when topics match to avoid duplicates.
3. Load L1 atoms
tmem atoms project
If output is very large (200+ records), focus on records since last consolidation by checking tmem changelog --last 50 for recent writes.
For global atoms (persona/instruction types):
tmem atoms global
4. Generate L2 scene blocks
Group project-scoped atoms by topic into narrative scenes.
Important: If a scene with the same topic already exists from step 2, reuse that exact name so the file gets updated instead of duplicated.
Write each scene using a heredoc to handle multiline content:
cat <<'SCENE_EOF' | tmem write-scene --name "Scene Name" --summary "One-line summary" --heat 3
- Fact 1
- Fact 2
- What was decided and why
SCENE_EOF
Guidelines:
- Group by topic, not by session
- Aim for 5-15 scenes per project — fewer if topics are narrow, more if diverse
- Heat 4-5: active this week. Heat 2-3: recent but not current. Heat 1: historical.
- Each scene should be understandable on its own
5. Generate L3 persona
Read existing persona:
tmem persona
Merge new insights from persona-type and instruction-type atoms. Don't replace — evolve.
Priority cap (don't amplify on merge): merging combines evidence; it must NOT inflate importance beyond the strongest source. When you fold several atoms into one persona point or standing instruction, the merged item's weight (priority/prominence) MUST be ≤ max(priority) of the contributing atoms — never higher just because it was repeated or merged. A single scene-local instruction must not be promoted into a dominant global rule unless the source atoms' own priority already justifies it. Likewise, scene --heat reflects recency, not merge count: repetition across sessions is not evidence of higher priority.
cat <<'PERSONA_EOF' | tmem write-persona
- Role, background, expertise
- Tools, styles, communication preferences
- Patterns, habits, workflow characteristics
- Long-term rules for AI behavior
PERSONA_EOF
Keep under 500 words — this gets injected into every turn's recall context.
6. Mark complete
tmem mark-done
After consolidation, tell the user: Memory pipeline complete. Hybrid recall is now active.
