What it does
Typed knowledge graph for structured agent memory and composable skills
Skills ZICQ category:Documents ontology OpenClaw
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
Official URL:ClawHub
Intro in this page language first. The official description stays in its original wording; we do not rewrite SKILL.md.
Typed knowledge graph for structured agent memory and composable skills
| Trigger | Action |
Per Agent Skills progressive disclosure: name and description load at startup (~100 tokens); the full SKILL.md body loads when the skill activates; scripts/, references/, and assets/ load only as needed. This file's sections: Ontology; Core Concept; When to Use; Core Types; Agents & People; Work.
File analysis: besides SKILL.md, the body references scripts/ontology.py, references/schema.md, references/queries.md. Those resources load on demand.
OntologyCore ConceptWhen to UseCore TypesAgents & PeopleWorkTime & PlaceInformationResourcesMetaStorageAppend-Only Rule
Source category:ClawHub Agent Memory
Market sources:ClawHub
nameontologydescriptionscripts/ontology.pyreferences/schema.mdreferences/queries.mdThese paths are extracted from the text. Check the upstream package to verify the files exist.
Invocation syntax and available tools depend on your Agent client. Client integration guide ↗
Choose the target agent and installation scope, keep referenced package files, then verify the skill appears in the client's catalog.
This skill references supporting files. Retrieve the complete directory from the source; copying SKILL.md alone may leave missing dependencies.
Copy these instructions to a compatible agent and confirm the target directory matches your client.
Install the agent skill "ontology" into my project. The full SKILL.md and official description are at https://zicq.com/en/skills/skl-346b60edd3ecf3ef-Ontology.html Save it as .cursor/skills/ontology/SKILL.md or .claude/skills/ontology/SKILL.md and keep the frontmatter name and description exactly as-is. This skill also ships scripts/, references/, or assets/ — fetch the whole folder from https://clawhub.ai/skills/ontology instead of creating only a SKILL.md.
No unambiguous GitHub package URL is recorded. Follow the original source's installer instructions.
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? }
# Information
Document: { title, path?, url?, summary? }
Message: { content, sender, recipients[], thread? }
Thread: { subject, participants[], messages[] }
Note: { content, tags[], refs[] }
# Resources
Account: { service, username, credential_ref? }
Device: { name, type, identifiers[] }
Credential: { service, secret_ref } # Never store secrets directly
# Meta
Action: { type, target, timestamp, outcome? }
Policy: { scope, rule, enforcement }
Default: memory/ontology/graph.jsonl
{"op":"create","entity":{"id":"p_001","type":"Person","properties":{"name":"Alice"}}}
{"op":"create","entity":{"id":"proj_001","type":"Project","properties":{"name":"Website Redesign","status":"active"}}}
{"op":"relate","from":"proj_001","rel":"has_owner","to":"p_001"}
Query via scripts or direct file ops. For complex graphs, migrate to SQLite.
When working with existing ontology data or schema, append/merge changes instead of overwriting files. This preserves history and avoids clobbering prior definitions.
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[email protected]"}'
python3 scripts/ontology.py query --type Task --where '{"status":"open"}'
python3 scripts/ontology.py get --id task_001
python3 scripts/ontology.py related --id proj_001 --rel has_task
python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001
python3 scripts/ontology.py validate # Check all constraints
Define in memory/ontology/schema.yaml:
types:
Task:
required: [title, status]
status_enum: [open, in_progress, blocked, done]
Event:
required: [title, start]
validate: "end >= start if end exists"
Credential:
required: [service, secret_ref]
forbidden_properties: [password, secret, token] # Force indirection
relations:
has_owner:
from_types: [Project, Task]
to_types: [Person]
cardinality: many_to_one
blocks:
from_types: [Task]
to_types: [Task]
acyclic: true # No circular dependencies
Skills that use ontology should declare:
# In SKILL.md frontmatter or header
ontology:
reads: [Task, Project, Person]
writes: [Task, Action]
preconditions:
- "Task.assignee must exist"
postconditions:
- "Created Task has status=open"
Model multi-step plans as a sequence of graph operations:
Plan: "Schedule team meeting and create follow-up tasks"
1. CREATE Event { title: "Team Sync", attendees: [p_001, p_002] }
2. RELATE Event -> has_project -> proj_001
3. CREATE Task { title: "Prepare agenda", assignee: p_001 }
4. RELATE Task -> for_event -> event_001
5. CREATE Task { title: "Send summary", assignee: p_001, blockers: [task_001] }
Each step is validated before execution. Rollback on constraint violation.
Log ontology mutations as causal actions:
# When creating/updating entities, also log to causal action log
action = {
"action": "create_entity",
"domain": "ontology",
"context": {"type": "Task", "project": "proj_001"},
"outcome": "created"
}
# Email skill creates commitment
commitment = ontology.create("Commitment", {
"source_message": msg_id,
"description": "Send report by Friday",
"due": "2026-01-31"
})
# Task skill picks it up
tasks = ontology.query("Commitment", {"status": "pending"})
for c in tasks:
ontology.create("Task", {
"title": c.description,
"due": c.due,
"source": c.id
})
# Initialize ontology storage
mkdir -p memory/ontology
touch memory/ontology/graph.jsonl
# Create schema (optional but recommended)
python3 scripts/ontology.py schema-append --data '{
"types": {
"Task": { "required": ["title", "status"] },
"Project": { "required": ["name"] },
"Person": { "required": ["name"] }
}
}'
# Start using
python3 scripts/ontology.py create --type Person --props '{"name":"Alice"}'
python3 scripts/ontology.py list --type Person
references/schema.md — Full type definitions and constraint patternsreferences/queries.md — Query language and traversal examplesRuntime instructions operate on local files (memory/ontology/graph.jsonl and memory/ontology/schema.yaml) and provide CLI usage for create/query/relate/validate; this is within scope. The skill reads/writes workspace files and will create the memory/ontology directory when used. Validation includes property/enum/forbidden checks, relation type/cardinality validation, acyclicity for relations marked acyclic: true, and Event end >= start checks; other higher-level constraints may still be documentation-only unless implemented in code.
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