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Ontology To Expertpack

核心功能：将Ontology技能的graph.jsonl知识图谱（可选schema.yaml）转换为合规ExpertPack，具备多层检索支持，生成EK元数据占位、分类化内容等结构。典型场景：Ontology技能向ExpertPack迁移，实现知识可移植部署与高效管理。通过Python脚本执行，输出为Obsidian兼容目录，含manifest、概述、分类内容等文件，可直接作为Obsidian vault使用，后续可进行EK评估等优化。

brianhearn
brianhearn
热度 60.92
最近更新 2026-05-11 17:32:33
分类：知识依赖：代码执行 / 读写文件应用场景：文件转换 / 数据处理

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 ontology-to-expertpack
Metadata
name
ontology-to-expertpack
description
Convert an Ontology skill knowledge graph into a structured ExpertPack. Use when migrating from the Ontology skill's entity/relation graph (memory/ontology/graph.jsonl) to ExpertPack's richer format with multi-layer retrieval, EK measurement, and portable deployment. Output is Obsidian-compatible — includes YAML frontmatter on all content files and can be opened as an Obsidian vault. Triggers on: 'ontology to expertpack', 'convert ontology', 'export ontology', 'migrate ontology', 'ontology graph to pack', 'upgrade ontology'. Requires the Ontology skill's graph.jsonl and optionally schema.yaml.
metadata
{"openclaw":{"homepage":"https://expertpack.ai","requires":{"bins":["python3"]}}}

Ontology to ExpertPack Converter

Converts an OpenClaw Ontology skill's append-only knowledge graph into a fully compliant ExpertPack with multi-layer retrieval support.

How to Use

Run the converter script:

python3 {skill_dir}/scripts/convert.py \
  --graph memory/ontology/graph.jsonl \
  --output ~/expertpacks/my-knowledge-pack

Optional flags:

  • --schema memory/ontology/schema.yaml — uses type definitions and relation rules
  • --name "My Knowledge Pack" — custom pack name (defaults to "Ontology Export")
  • --type auto|person|product|process|composite — override auto-detected pack type

What It Produces

A complete ExpertPack at the output directory:

  • manifest.yaml — pack identity, type, context tiers, EK metadata placeholder
  • overview.md — summary of graph contents, entity/relation counts, navigation guide
  • Content organized by mapped category (relationships/, workflows/, facts/, concepts/, operational/, governance/)
  • _index.md in each content directory
  • relations.yaml — typed entity relation graph (schema 2.3 compliant)
  • glossary.md — entity types and terms
  • Lead summaries and ## section headers for optimal chunking

Filenames use kebab-case. Content files kept under 3KB.

Post-Conversion Steps

  1. cd into the generated ExpertPack directory
  2. Verify content files are 400–800 tokens each (Schema 2.5 — no external chunker needed for correctly-sized files)
  3. Run EK evaluator to measure esoteric knowledge ratio
  4. Review and refine manifest.yaml context tiers
  5. Commit to git and share via expertpack.ai or ClawHub

See expertpack.ai and the expertpack ClawHub skill for full pack maintenance workflows.

Keep the output pack git-friendly and ready for iterative deepening.