Ontology Engineer

Extract candidate ontology models from existing data. Build and maintain personal knowledge graphs.

Core principle: Make implicit business models in existing data explicit. Don't create from scratch.

Division of labor: Scripts handle mechanical extraction (file scanning, format conversion, table parsing). LLM handles semantic judgment (entity identification, property selection, relationship discovery, naming, cross-source merging).

Security model:

• No external API calls. The LLM running this skill (Claude, OpenClaw, etc.) IS the semantic engine. No credentials, no network endpoints, no data exfiltration paths.
• User-scoped scanning. Step 1.5 is a MANDATORY interactive checkpoint — the user reviews and approves every folder before any content is read. Nothing is analyzed without explicit confirmation.
• Local-only output. All artifacts (graph.jsonl, schema.yaml, review.md) are written to a user-specified local directory. No data leaves the machine.
• Append-only writes. Scripts only create/append files. No deletion, no modification of existing user files.

When This Skill Adds Value (and When It Doesn't)

Knowledge graphs and ontology extraction are not universally useful. Before starting, assess fit:

| Scenario | Value | Why |