Extract L1 memory atoms from Claude Code conversation history. Triggers when the user says "seed memories", "extract memories", "backfill memory", "remember my history", "learn from past conversations", "what do you know about me", or after /memory-init when conversation history exists. Also use when asyncRewake pipeline flags pending sessions. This skill is about CREATING new memories from transcripts — for inspecting existing memories use tmem-cli instead.
Scanned 5/27/2026
Install via CLI
openskills install baodq97/tencentdb-agent-memory---
name: memory-seed
description: Extract L1 memory atoms from Claude Code conversation history. Triggers when the user says "seed memories", "extract memories", "backfill memory", "remember my history", "learn from past conversations", "what do you know about me", or after /memory-init when conversation history exists. Also use when asyncRewake pipeline flags pending sessions. This skill is about CREATING new memories from transcripts — for inspecting existing memories use tmem-cli instead.
---
# Memory Seeding
Read conversation transcripts from `~/.claude/projects/` and extract structured L1 memory atoms. You perform all extraction — no external LLM needed.
## Workflow
### 1. Find pending sessions
```bash
tmem sessions
```
If no pending sessions, tell the user and stop.
### 2. For each pending session
Read the conversation:
```bash
tmem read-session SESSION_FILE_PATH
```
### 3. Extract memories
Read the extraction guide for detailed rules:
```bash
cat ${CLAUDE_PLUGIN_ROOT}/skills/memory-seed/references/extraction-guide.md
```
Analyze the conversation and produce a JSON array of memories. Each memory needs `content`, `type`, `priority`, `scene_name`, `source_message_ids`, `metadata`.
**Three types with scope routing:**
- **persona** (priority 50-100) → stored globally. Stable user attributes, preferences.
- **episodic** (priority 60-100) → stored per-project. Events, decisions, plans.
- **instruction** (priority 70-100) → stored globally. AI behavior rules.
**Filtering — skip these:**
- Greetings, filler, one-time requests
- AI tool outputs, error messages
- Anything already covered by existing memories (check with `tmem search <keyword>` if unsure)
If a session has no extractable memories, mark it done and move to the next.
### 4. Write atoms
Write the JSON array to a temp file to avoid shell escaping issues, then pipe it:
```bash
cat <<'ATOMS_EOF' | tmem write-l1 --session SESSION_ID
[{"content": "...", "type": "persona", "priority": 80, "scene_name": "...", "source_message_ids": [], "metadata": {}}]
ATOMS_EOF
```
### 5. Verify and hint
```bash
tmem status
tmem changelog --last 10
```
After seeding, tell the user: **Next: use the memory-consolidate skill** to group atoms into scenes and synthesize persona.
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