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// Skill profile

Ontology to ExpertPack Converter

name: ontology-to-expertpack

by brianhearn · published 2026-03-22

数据处理自动化任务加密货币
Total installs
0
Stars
★ 0
Last updated
2026-03
// Install command
$ claw add gh:brianhearn/brianhearn-ontology-to-expertpack
View on GitHub
// Full documentation

---

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. 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. 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](https://expertpack.ai) and the `expertpack` ClawHub skill for full pack maintenance workflows.

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

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