Ontology

sundial-org/awesome-openclaw-skills/skills/ontology

作者 sundial-orgb80cde2ef852无许可证663 个星标收录于 2026年10月8日更新于 2026年10月8日仓库7个月前更新

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.

包含脚本AI & Agents

AI 生成的概览

维护由实体和关系组成的类型化知识图谱，用于结构化智能体记忆与跨技能状态共享。

功能: 提供类型化词汇表与约束系统，将实体（Person、Project、Task、Event、Document 等）及其属性和关系存储在 JSONL 图文件中。随附的 Python 脚本支持创建、查询、关联和校验实体，模式规则可强制必填字段、枚举取值和关系基数。它还说明如何把多步计划建模为经过校验的图变换，并在技能之间共享状态。
适用场景: 适用于智能体需要持久化结构化记忆的场景，例如记住事实、查询某个实体的已知信息、关联对象或追踪依赖关系。也适合多步规划以及需要通过共享图谱交换状态的技能。
运行要求: 需要 Python 3 来运行 scripts/ontology.py，并需要可写存储位置，默认是 memory/ontology/graph.jsonl，可选 schema.yaml。未提及凭据或网络访问；该技能附带一个可执行脚本和两份参考文档。

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 }

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 & PeoplePerson: { name, email?, phone?, notes? }Organization: { name, type?, members[] }
# WorkProject: { name, status, goals[], owner? }Task: { title, status, due?, priority?, assignee?, blockers[] }Goal: { description, target_date?, metrics[] }
# Time & PlaceEvent: { title, start, end?, location?, attendees[], recurrence? }Location: { name, address?, coordinates? }
# InformationDocument: { title, path?, url?, summary? }Message: { content, sender, recipients[], thread? }Thread: { subject, participants[], messages[] }Note: { content, tags[], refs[] }
# ResourcesAccount: { service, username, credential_ref? }Device: { name, type, identifiers[] }Credential: { service, secret_ref }  # Never store secrets directly
# MetaAction: { type, target, timestamp, outcome? }Policy: { scope, rule, enforcement }

# Agents & PeoplePerson: { name, email?, phone?, notes? }Organization: { name, type?, members[] }
# WorkProject: { name, status, goals[], owner? }Task: { title, status, due?, priority?, assignee?, blockers[] }Goal: { description, target_date?, metrics[] }
# Time & PlaceEvent: { title, start, end?, location?, attendees[], recurrence? }Location: { name, address?, coordinates? }
# InformationDocument: { title, path?, url?, summary? }Message: { content, sender, recipients[], thread? }Thread: { subject, participants[], messages[] }Note: { content, tags[], refs[] }
# ResourcesAccount: { service, username, credential_ref? }Device: { name, type, identifiers[] }Credential: { service, secret_ref }  # Never store secrets directly
# MetaAction: { 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"}

{"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

bash

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

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 scripts/ontology.py get --id task_001python3 scripts/ontology.py related --id proj_001 --rel has_task

python3 scripts/ontology.py query --type Task --where '{"status":"open"}'python3 scripts/ontology.py get --id task_001python3 scripts/ontology.py related --id proj_001 --rel has_task

Link Entities

Validate

Constraints

Define in memory/ontology/schema.yaml:

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

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:

yaml

# In SKILL.md frontmatter or headerontology:  reads: [Task, Project, Person]  writes: [Task, Action]  preconditions:    - "Task.assignee must exist"  postconditions:    - "Created Task has status=open"

# In SKILL.md frontmatter or headerontology:  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_0013. CREATE Task { title: "Prepare agenda", assignee: p_001 }4. RELATE Task -> for_event -> event_0015. CREATE Task { title: "Send summary", assignee: p_001, blockers: [task_001] }

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_0013. CREATE Task { title: "Prepare agenda", assignee: p_001 }4. RELATE Task -> for_event -> event_0015. 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:

python

# When creating/updating entities, also log to causal action logaction = {    "action": "create_entity",    "domain": "ontology",     "context": {"type": "Task", "project": "proj_001"},    "outcome": "created"}

# When creating/updating entities, also log to causal action logaction = {    "action": "create_entity",    "domain": "ontology",     "context": {"type": "Task", "project": "proj_001"},    "outcome": "created"}

Cross-Skill Communication

python

# Email skill creates commitmentcommitment = ontology.create("Commitment", {    "source_message": msg_id,    "description": "Send report by Friday",    "due": "2026-01-31"})
# Task skill picks it uptasks = ontology.query("Commitment", {"status": "pending"})for c in tasks:    ontology.create("Task", {        "title": c.description,        "due": c.due,        "source": c.id    })

# Email skill creates commitmentcommitment = ontology.create("Commitment", {    "source_message": msg_id,    "description": "Send report by Friday",    "due": "2026-01-31"})
# Task skill picks it uptasks = ontology.query("Commitment", {"status": "pending"})for c in tasks:    ontology.create("Task", {        "title": c.description,        "due": c.due,        "source": c.id    })

Quick Start

bash

# Initialize ontology storagemkdir -p memory/ontologytouch 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 usingpython3 scripts/ontology.py create --type Person --props '{"name":"Alice"}'python3 scripts/ontology.py list --type Person

# Initialize ontology storagemkdir -p memory/ontologytouch 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 usingpython3 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 patterns
references/queries.md — Query language and traversal examples

来源与署名

来源：sundial-org/awesome-openclaw-skills位于skills/ontology提交b80cde2

许可证：无许可证

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Ontology

Ontology

Core Concept

When to Use

Core Types

Storage

Workflows

Create Entity

Query

Link Entities

Validate

Constraints

Skill Contract

Planning as Graph Transformation

Integration Patterns

With Causal Inference

Cross-Skill Communication

Quick Start

References

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