What makes Ontology notable?

1.9K downloads. Trust evidence available

How should Ontology be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Ontology

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin... Skill: Ontology Owner: kaising-openclaw1 Summary: Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-17T04:27:41.138Z | user v1.0 长期记忆技能 - 结构化知识图谱 Archive index: Archive v1.0.0: 6 files, 13647 bytes Files: _meta.json (135b), references/queries.md (5471b), r

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.9k

Updated

Oct 9, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.9K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 1.9K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.0release · observed Apr 17, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17epxftgh3h1zzcr8g36j2dax83xh8g:long-term-memory

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-kaising-openclaw1-long-term-memory/snapshot"

Documentation

CLAWHUB

21,781 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: ontology
description: 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.
---

# 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

```yaml
# 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`

```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.

### Append-Only Rule

When working with existing ontology data or schema, **append/merge** changes instead of overwriting files. This preserves history and avoids clobbering prior definitions.

## Workflows

### Create Entity

```bash
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[email protected]"}'
```

### Query

```bash
python3 scripts/ontology.py query --type Task --where '{"status":"open"}'
python3 scrip

_meta.json

{
  "ownerId": "kn7c3kgfvnhefjww9yw5h6n7zx83wgtx",
  "slug": "long-term-memory",
  "version": "1.0.0",
  "publishedAt": 1776400061138
}

references/queries.md

# Query Reference

Query patterns and graph traversal examples.

## Basic Queries

### Get by ID

```bash
python3 scripts/ontology.py get --id task_001
```

### List by Type

```bash
# All tasks
python3 scripts/ontology.py list --type Task

# All people
python3 scripts/ontology.py list --type Person
```

### Filter by Properties

```bash
# Open tasks
python3 scripts/ontology.py query --type Task --where '{"status":"open"}'

# High priority tasks
python3 scripts/ontology.py query --type Task --where '{"priority":"high"}'

# Tasks assigned to specific person (by property)
python3 scripts/ontology.py query --type Task --where '{"assignee":"p_001"}'
```

## Relation Queries

### Get Related Entities

```bash
# Tasks belonging to a project (outgoing)
python3 scripts/ontology.py related --id proj_001 --rel has_task

# What projects does this task belong to (incoming)
python3 scripts/ontology.py related --id task_001 --rel part_of --dir incoming

# All relations for an entity (both directions)
python3 scripts/ontology.py related --id p_001 --dir both
```

### Common Patterns

```bash
# Who owns this project?
python3 scripts/ontology.py related --id proj_001 --rel has_owner

# What events is this person attending?
python3 scripts/ontology.py related --id p_001 --rel attendee_of --dir outgoing

# What's blocking this task?
python3 scripts/ontology.py related --id task_001 --rel blocked_by --dir incoming
```

## Programmatic Queries

### Python API

```python
from scripts.ontology import load_graph, query_entities, get_related

# Load the graph
entities, relations = load_graph("memory/ontology/graph.jsonl")

# Query entities
open_tasks = query_entities("Task", {"status": "open"}, "memory/ontology/graph.jsonl")

# Get related
project_tasks = get_related("proj_001", "has_task", "memory/ontology/graph.jsonl")
```

### Complex Queries

```python
# Find all tasks blocked by incomplete dependencies
def find_blocked_tasks(graph_path):
    entities, relations = load_graph(graph_path)
    blocked = []
    
    for entity in entities.values():
        if entity["type"] != "Task":
            continue
        if entity["properties"].get("status") == "blocked":
            # Find what's blocking it
            blockers = get_related(entity["id"], "blocked_by", graph_path, "incoming")
            incomplete_blockers = [
                b for b in blockers 
                if b["entity"]["properties"].get("status") != "done"
            ]
            if incomplete_blockers:
                blocked.append({
                    "task": entity,
                    "blockers": incomplete_blockers
                })
    
    return blocked
```

### Path Queries

```python
# Find path between two entities
def find_path(from_id, to_id, graph_path, max_depth=5):
    entities, relations = load_graph(graph_path)
    
    visited = set()
    queue = [(from_id, [])]
    
    while queue:
        current, path = queue.pop(0)
        
        if current == to_id:
            return p

references/schema.md

# Ontology Schema Reference

Full type definitions and constraint patterns for the ontology graph.

## Core Types

### Agents & People

```yaml
Person:
  required: [name]
  properties:
    name: string
    email: string?
    phone: string?
    organization: ref(Organization)?
    notes: string?
    tags: string[]?

Organization:
  required: [name]
  properties:
    name: string
    type: enum(company, team, community, government, other)?
    website: url?
    members: ref(Person)[]?
```

### Work Management

```yaml
Project:
  required: [name]
  properties:
    name: string
    description: string?
    status: enum(planning, active, paused, completed, archived)
    owner: ref(Person)?
    team: ref(Person)[]?
    goals: ref(Goal)[]?
    start_date: date?
    end_date: date?
    tags: string[]?

Task:
  required: [title, status]
  properties:
    title: string
    description: string?
    status: enum(open, in_progress, blocked, done, cancelled)
    priority: enum(low, medium, high, urgent)?
    assignee: ref(Person)?
    project: ref(Project)?
    due: datetime?
    estimate_hours: number?
    blockers: ref(Task)[]?
    tags: string[]?

Goal:
  required: [description]
  properties:
    description: string
    target_date: date?
    status: enum(active, achieved, abandoned)?
    metrics: object[]?
    key_results: string[]?
```

### Time & Location

```yaml
Event:
  required: [title, start]
  properties:
    title: string
    description: string?
    start: datetime
    end: datetime?
    location: ref(Location)?
    attendees: ref(Person)[]?
    recurrence: object?  # iCal RRULE format
    status: enum(confirmed, tentative, cancelled)?
    reminders: object[]?

Location:
  required: [name]
  properties:
    name: string
    address: string?
    city: string?
    country: string?
    coordinates: object?  # {lat, lng}
    timezone: string?
```

### Information

```yaml
Document:
  required: [title]
  properties:
    title: string
    path: string?  # Local file path
    url: url?      # Remote URL
    mime_type: string?
    summary: string?
    content_hash: string?
    tags: string[]?

Message:
  required: [content, sender]
  properties:
    content: string
    sender: ref(Person)
    recipients: ref(Person)[]
    thread: ref(Thread)?
    timestamp: datetime
    platform: string?  # email, slack, whatsapp, etc.
    external_id: string?

Thread:
  required: [subject]
  properties:
    subject: string
    participants: ref(Person)[]
    messages: ref(Message)[]
    status: enum(active, archived)?
    last_activity: datetime?

Note:
  required: [content]
  properties:
    content: string
    title: string?
    tags: string[]?
    refs: ref(Entity)[]?  # Links to any entity
    created: datetime
```

### Resources

```yaml
Account:
  required: [service, username]
  properties:
    service: string  # github, gmail, aws, etc.
    username: string
    url: url?
    credential_ref: ref(Credential)?

Device:
  required: [name, type]
  properties:
    name

skill-card.md

## Description:

Typed knowledge graph for structured agent memory and composable skills.

This skill is ready for commercial/non-commercial use.

## Publisher:

[kaising-openclaw1](https://clawhub.ai/user/kaising-openclaw1)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and agents use this skill to create, query, relate, and validate typed entities for persistent workspace memory, task planning, and cross-skill state sharing.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill persists shared workspace memory and can retain sensitive or stale information.

Mitigation: Avoid storing secrets or tokens, review ontology entries before persistence, and keep credential records limited to references such as secret_ref.

Risk: Create, update, delete, relate, and schema-append operations can change the memory graph or schema.

Mitigation: Require explicit user confirmation before mutations and run validation after changes.

Risk: Validation is available but may not enforce every documented higher-level constraint before data is saved.

Mitigation: Treat validation output as a review aid, inspect proposed graph changes, and add project-specific checks for constraints that matter operationally.

## Reference(s):

- [Ontology Schema Reference](references/schema.md)
- [Query Reference](references/queries.md)
- [ClawHub skill page](https://clawhub.ai/kaising-openclaw1/skills/long-term-memory)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with JSON, YAML, Python, and shell command examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May create or update local workspace files under memory/ontology when the provided CLI is used.]

## Skill Version(s):

1.0.0 (source: ClawHub release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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