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 col...

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

1. cd ~/expertpacks/my-agent-pack
2. Verify content files are 400–800 tokens each (Schema 2.5 — retrieval-ready by design)
3. Measure EK ratio: python3 /path/to/expertpack/tools/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

See also: Elite Longterm Memory skill on ClawHub.