/memory-seed

name: memory-seed
description: Extract L1 memory atoms from Claude Code conversation history. Run manually via /memory-seed.
disable-model-invocation: true

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

tmem sessions

If no pending sessions, tell the user and stop.

2. For each pending session

Read the conversation:

tmem read-session SESSION_FILE_PATH

3. Extract memories

Read the extraction guide for detailed rules:

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`.

**Grounding (important):** populate `source_message_ids` with the actual transcript message `uuid`s the memory was drawn from. `tmem write-l1 --session` runs a deterministic grounding check — an atom whose `content` does not overlap its cited source messages is **dropped as confabulation**. Leaving `source_message_ids` empty skips the check (atom kept ungated), so cite real ids to get protection, and never invent facts absent from the source. Note: the check is lexical (shared words), so a heavily paraphrased/normalized atom (e.g. expanding an acronym the source never spelled out) can be dropped even when truthful — keep some of the source's own wording in `content`, or leave `source_message_ids` empty if you must paraphrase far.

**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:

cat <<'ATOMS_EOF' | tmem write-l1 --session SESSION_ID
[{"content": "...", "type": "persona", "priority": 80, "scene_name": "...", "source_message_ids": ["<real-uuid-from-transcript>"], "metadata": {}}]
ATOMS_EOF

5. Verify and hint

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.