Elite Longterm Memory → ExpertPack

---

name: elite-to-expertpack

description: "Convert Elite Longterm Memory data into a structured ExpertPack. Migrates the 5-layer memory system (SESSION-STATE hot RAM, LanceDB warm store, Git-Notes cold store, MEMORY.md curated archive, and daily journals) into ExpertPack's portable format with multi-layer retrieval, context tiers, and EK measurement. Use when: upgrading from Elite Longterm Memory to ExpertPack, backing up agent knowledge, or migrating to a new platform. Triggers on: 'elite to expertpack', 'convert elite memory', 'export elite memory', 'migrate elite longterm', 'upgrade memory to expertpack', 'elite memory export'."

metadata:

openclaw:

homepage: https://expertpack.ai

requires:

bins:

- python3

---

# Elite Longterm Memory → ExpertPack

Converts an **Elite Longterm Memory** (5-layer system with 32K ClawHub downloads) into a proper structured **ExpertPack**.

**Supported layers:**

Usage

cd /root/.openclaw/workspace/ExpertPack/skills/elite-to-expertpack
python3 scripts/convert.py \
  --workspace /path/to/your/workspace \
  --output ~/expertpacks/my-agent-pack \
  [--name "My Agent's Knowledge"] \
  [--type auto|person|agent]

Flags let you override auto-detected paths for each layer.

What It Produces

A complete ExpertPack conforming to schema 2.3:

**Secrets are automatically stripped** (sk-*, ghp_*, tokens, passwords). Warnings emitted for any found.

Post-Conversion Steps

1. `cd ~/expertpacks/my-agent-pack`

2. Run the ExpertPack chunker: `python3 /path/to/expertpack/scripts/chunk.py --pack . --output ./.chunks`

3. Measure EK ratio: `python3 /path/to/expertpack/scripts/eval-ek.py .`

4. Review `overview.md` and `manifest.yaml`

5. Commit to git and publish to ClawHub

**Learn more:** https://expertpack.ai • ClawHub [expertpack skill](https://clawhub.com/skills/expertpack)

**See also:** Elite Longterm Memory skill on ClawHub.