Elite To Expertpack

通过 Python 脚本读取工作区中的会话状态、Git-Notes、MEMORY.md 与日志等记忆层，去重、剥离密钥后生成符合 ExpertPack schema 的 manifest.yaml、overview.md、索引与分类目录，输出兼容 Obsidian，含上下文分层与 EK 测量占位信息。

brianhearn

热度 59.17

最近更新 2026-05-11 17:32:33

分类：效率依赖：代码执行 / 读写文件应用场景：文件转换 / 工作流 / 笔记整理 / 摘要总结

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add elite-to-expertpack

Metadata

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. 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'.
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:

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.