memory-consolidate

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
## Key Facts
- Fact 1
- Fact 2

## Decisions
- 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.

cat <<'PERSONA_EOF' | tmem write-persona
# User Persona

## Identity
- Role, background, expertise

## Preferences
- Tools, styles, communication preferences

## Working Style
- Patterns, habits, workflow characteristics

## Standing Instructions
- 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.