ontology
In data and AI, this skill provides typed knowledge graphs for building structured agent memories and composable knowledge representations, enhancing AI system understanding and reasoning.
npx skills add https://github.com/sundial-org/awesome-openclaw-skills --skill ontologyBefore / After Comparison
1 组Agent memory lacks structure, making it difficult to efficiently store and retrieve complex information. Limited skill combinations hinder flexible adaptation to diverse tasks, impacting agent performance.
Building typed knowledge graphs provides agents with structured memory. Enhances skill composability, improving the agent's ability to understand and execute complex tasks.
Ontology
A typed vocabulary + constraint system for representing knowledge as a verifiable graph.
Core Concept
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 }
When to Use
| 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 |
Core Types
# 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 }
Storage
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.
Workflows
Create Entity
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"alice@example.com"}'
Query
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
Link Entities
python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001
Validate
python3 scripts/ontology.py validate # Check all constraints
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
Skill Contract
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"
Planning as Graph Transformation
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.
Integration Patterns
With Causal Inference
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"
}
Cross-Skill Communication
# 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
})
Quick Start
# Initialize ontology storage
mkdir -p memory/ontology
touch memory/ontology/graph.jsonl
# Create schema (optional but recommended)
cat > memory/ontology/schema.yaml << 'EOF'
types:
Task:
required: [title, status]
Project:
required: [name]
Person:
required: [name]
EOF
# Start using
python3 scripts/ontology.py create --type Person --props '{"name":"Alice"}'
python3 scripts/ontology.py list --type Person
References
references/schema.md— Full type definitions and constraint patternsreferences/queries.md— Query language and traversal examples
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