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.
Scanned 5/27/2026
Install via CLI
openskills install baodq97/tencentdb-agent-memory---
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
```bash
tmem status
```
If zero records exist, tell the user to run memory-seed first and stop.
### 2. List existing scenes
```bash
tmem scenes list
```
Note existing scene names — you will reuse them when topics match to avoid duplicates.
### 3. Load L1 atoms
```bash
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):
```bash
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:
```bash
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:
```bash
tmem persona
```
Merge new insights from persona-type and instruction-type atoms. Don't replace — evolve.
```bash
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
```bash
tmem mark-done
```
After consolidation, tell the user: **Memory pipeline complete.** Hybrid recall is now active.
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