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ontology-to-expertpack

by brianhearn

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. 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.

View Chinese version with editor review

安装

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

必需命令行工具

python3

文档

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:

bash
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. Run the ExpertPack chunker to generate summaries/ and propositions/
  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.