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elite-to-expertpack

by brianhearn

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'.

View Chinese version with editor review

安装

claude skill add --url github.com/openclaw/skills/tree/main/skills/brianhearn/elite-to-expertpack

必需命令行工具

python3

文档

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

bash
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. 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

See also: Elite Longterm Memory skill on ClawHub.