ClawHub

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. Output is Obsidian-compatible — includes YAML frontmatter on all content files and can be opened as an Obsidian vault. 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'.

Audits

Pass
ClawScanPass

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install elite-to-expertpack

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.