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Typed knowledge graph for structured agent memory and composable skills.
Passed 17 of 20 test cases with the skill, and 5 of 20 without it.
and 21 more in the full report
14 fixed5 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write the CLI invocation to find all entities directly connected to project 'proj_001' via the 'has_task' relationship.
| Checked | Without skill | With skill |
|---|---|---|
| Runs python3 scripts/ontology.py related | Missed | Passed |
| Passes --rel has_task as argument | Missed | Passed |
The instructions your model reads when the skill loads.
A typed vocabulary + constraint system for representing knowledge as a verifiable graph.
Everything is an entity with a type, properties, and relations to other entities. Every mutation is validated against type constraints before committing.
Entity: { id, type, properties, relations, created, updated }
Relation: { from_id, relation_type, to_id, properties }
| Trigger | Action |
|---|---|
| "Remember that..." | Create/update entity |
| "What do I know about X?" | Query graph |
| "Link X to Y" | Create relation |
| "Show all tasks for project Z" | Graph traversal |
| "What depends on X?" | Dependency query |
| Planning multi-step work | Model as graph transformations |
| Skill needs shared state | Read/write ontology objects |
# Agents & People
Person: { name, email?, phone?, notes? }
Organization: { name, type?, members[] }
# Work
Project: { name, status, goals[], owner? }
Task: { title, status, due?, priority?, assignee?, blockers[] }
Goal: { description, target_date?, metrics[] }
# Time & Place
Event: { title, start, end?, location?, attendees[], recurrence? }
Location: { name, address?, coordinates? }
test cases passed, vs 5 / 20 without
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