Create and validate typed knowledge graphs of entities and relations.
npx skills add https://github.com/gaos6e/MyOpenclaw --skill ontology-gaos6e
Please help me install this Agent Skill. Skill: ontology Source: https://github.com/gaos6e/MyOpenclaw/tree/main/workspace/skills/ontology Command: npx skills add https://github.com/gaos6e/MyOpenclaw --skill ontology-gaos6e
A typed vocabulary + constraint system for representing knowledge as a verifiable graph to support memory, reasoning, and cross-skill data sharing.
Initialize storage and begin modeling entities with the CLI and schema tooling.
High-intent search queries and answers about installing and using this skill.
Build a typed knowledge graph by defining entity types and relations in a schema.yaml file, then use a CLI workflow to create, relate, and validate entities. This enforces type schemas and cardinality constraints to ensure data integrity across your graph store.
A typed knowledge graph provides a verifiable vocabulary and constraint system for representing knowledge, enabling memory, reasoning, and cross-skill data sharing. It supports multi-step graph transformations and query traversals to derive insights and drive workflows.
Validate type constraints and cardinality by defining allowed entity types and relations in schema.yaml, then running the validate CLI command against your append-only graph store to enforce data integrity and schema compliance.
Yes, YAML is required to define schemas. The schema.yaml file specifies entity types such as Person, Project, and Task, along with their typed relations and cardinality constraints, serving as the foundation for graph validation.
Yes, you can plan multi-step graph transformations and execute query traversals across the typed knowledge graph. This allows you to derive insights from related entities and drive complex workflows through the CLI interface.
An append-only graph store prevents modification or deletion of existing entities and relations, meaning graph transformations must be planned as new additions rather than updates, which ensures data verifiability but limits destructive editing.
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