| 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 placeholderoverview.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 directoryrelations.yaml — typed entity relation graph (schema 4.1 compliant)glossary.md — entity types and terms- Retriever-anchored opening definitions and
## section headers for optimal chunking
Filenames use kebab-case. Concept atoms target 400–800 tokens with a 1,000-token ceiling; procedural/reference files stay focused and independently retrievable.
Post-Conversion Steps
cd into the generated ExpertPack directory- Verify content files are 400–800 tokens each (Schema 2.5 — no external chunker needed for correctly-sized files)
- Run EK evaluator to measure esoteric knowledge ratio
- Review and refine
manifest.yaml context tiers
- 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.
