Ontology

类型化知识图谱,结构化Agent记忆与可组合技能。Typed knowledge graph for structured agent memory and composable skills。触发关键词: graph, knowledge, typed, ontology, agent, structured。

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.

text

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

Person: { name, email?, phone?, notes? }
Organization: { name, type?, members[] }

Project: { name, status, goals[], owner? }
Task: { title, status, due?, priority?, assignee?, blockers[] }
Goal: { description, target_date?, metrics[] }

Event: { title, start, end?, location?, attendees[], recurrence? }
Location: { name, address?, coordinates? }

Document: { title, path?, url?, summary? }
Message: { content, sender, recipients[], thread? }
Thread: { subject, participants[], messages[] }
Note: { content, tags[], refs[] }

Account: { service, username, credential_ref? }
Device: { name, type, identifiers[] }
Credential: { service, secret_ref }  # Never store secrets directly

Action: { type, target, timestamp, outcome? }
Policy: { scope, rule, enforcement }

Storage

Default: memory/ontology/graph.jsonl

jsonl

text

{"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":"[e-mail]"}'

Query

bash

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

bash

python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001

Validate

bash

python3 scripts/ontology.py validate  # Check all constraints

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

Skill Contract

Skills that use ontology should declare:

yaml

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:

text

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:

python

action = {
    "action": "create_entity",
    "domain": "ontology",
    "context": {"type": "Task", "project": "proj_001"},
    "outcome": "created"
}

Cross-Skill Communication

python

commitment = ontology.create("Commitment", {
    "source_message": msg_id,
    "description": "Send report by Friday",
    "due": "2026-01-31"
})

tasks = ontology.query("Commitment", {"status": "pending"})
for c in tasks:
    ontology.create("Task", {
        "title": c.description,
        "due": c.due,
        "source": c.id
    })

Quick Start

bash

mkdir -p memory/ontology
touch memory/ontology/graph.jsonl

python3 scripts/ontology.py schema-append --data '{
  "types": {
    "Task": { "required": ["title", "status"] },
    "Project": { "required": ["name"] },
    "Person": { "required": ["name"] }
  }
}'

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 patterns
• references/queries.md — Query language and traversal examples

Instruction Scope

Runtime 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.

依赖说明

运行环境

• Agent平台: 支持SKILL.md的任意AI Agent( Code / Cursor / Codex / CLI等)
• 操作系统: Windows / macOS / Linux

依赖说明

依赖项	类型	是否必需	获取方式
LLM API	API	必需	由Agent内置LLM提供

API Key 配置

• 本Skill基于Markdown指令,无需额外API Key(除内容中明确标注的外部API)

可用性分类

• 分类: MD+execute(纯Markdown指令,部分功能需要exec命令行执行能力)
• 说明: 基于Markdown的AI Skill,通过自然语言指令驱动Agent执行任务

核心能力

• Typed knowledge graph for structured agent memory and composable skills
• 触发关键词: graph, knowledge, typed, ontology, agent, structured

适用场景

场景	输入	输出
基础使用	用户请求	处理结果

不适用于：需要人工判断的复杂决策场景

示例

示例1：基础用法

text

# 请参考上方使用说明进行配置和调用
result = "ready"
```bash
mkdir -p memory/ontology
touch memory/ontology/graph.jsonl

python3 scripts/ontology.py schema-append --data '{
  "types": {
    "Task": { "required": ["title", "status"] },
    "Project": { "required": ["name"] },
    "Person": { "required": ["name"] }
  }
}'

python3 scripts/ontology.py create --type Person --props '{"name":"Alice"}'
python3 scripts/ontology.py list --type Person

请参考上方使用说明进行配置和调用

result = "ready"

text

## 错误处理

| 错误场景 | 原因 | 处理方式 |
|---------|------|---------|
| 配置错误 | 参数缺失或格式错误 | 检查依赖说明中的配置要求 |
| 运行时错误 | 运行环境不满足 | 确认运行环境符合依赖说明 |
| 网络错误 | 连接超时或不可达 | 检查网络连接后重试，参考国内替代方案 |

## 常见问题

### Q1: 如何开始使用Ontology？
A: 请先阅读使用流程章节，确认环境满足依赖说明中的要求。

### Q2: 遇到错误怎么办？
A: 请参考错误处理章节，按照表格中的处理方式操作。

### Q3: Ontology有什么限制？
A: 请参考已知限制章节了解具体限制。

## 已知限制

- 需要LLM支持，无LLM环境无法使用
- 复杂场景可能需要人工辅助判断
- 性能取决于底层模型能力