GB-Power-Market-JJ elite-to-expertpack

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'.

install

source · Clone the upstream repo

git clone https://github.com/GeorgeDoors888/GB-Power-Market-JJ

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/GeorgeDoors888/GB-Power-Market-JJ "$T" && mkdir -p ~/.claude/skills && cp -r "$T/openclaw-skills/skills/brianhearn/elite-to-expertpack" ~/.claude/skills/georgedoors888-gb-power-market-jj-elite-to-expertpack && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/GeorgeDoors888/GB-Power-Market-JJ "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/openclaw-skills/skills/brianhearn/elite-to-expertpack" ~/.openclaw/skills/georgedoors888-gb-power-market-jj-elite-to-expertpack && rm -rf "$T"

manifest: openclaw-skills/skills/brianhearn/elite-to-expertpack/SKILL.md

source content

Elite Longterm Memory → ExpertPack

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

Supported layers:

Hot RAM —
```
SESSION-STATE.md
```
(current task, context, decisions)
Warm Store — LanceDB vectors at
```
~/.openclaw/memory/lancedb/
```
(note: exported or skipped)
Cold Store — Git-Notes JSONL (decisions, learnings, preferences)

Curated Archive —

MEMORY.md

memory/YYYY-MM-DD.md

journals,

memory/topics/*.md

Cloud — SuperMemory/Mem0 (skipped, noted in overview)

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:

```
manifest.yaml
```
(with context tiers, EK stub)
```
overview.md
```
summarizing conversion (layer counts, warnings)

Structured directories:

mind/

facts/

summaries/

operational/

relationships/

, etc.

```
_index.md
```
files, lead summaries,
```
glossary.md
```
(if terms found)
```
relations.yaml
```
(if relationships detected)
Clean deduplication preferring curated > structured > raw sources

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

Post-Conversion Steps

```
cd ~/expertpacks/my-agent-pack
```
Verify content files are 400–800 tokens each (Schema 2.5 — retrieval-ready by design)

Measure EK ratio:

python3 /path/to/expertpack/tools/eval-ek.py .

Review
```
overview.md
```
and
```
manifest.yaml
```
Commit to git and publish to ClawHub

Learn more: https://expertpack.ai • ClawHub expertpack skill

See also: Elite Longterm Memory skill on ClawHub.