tenmyword

Ontology 1.0.4

AI 智能体v1.0.0
作者:tenmyword更新于 2026-05-11ontology-1-0-4@clawhub_tenmyword/ontology-1-0-4
上下文管理记忆增强工具调用#Agent Memory

据 AlphaMoat 统计,截至 2026-09,Ontology 1.0.4 累计下载 2,488 次、获 0 个星标,在全部 5,078 个 AI 技能中排名第 4,109 位,在「AI 智能体」分类(969 个)中排名第 837 位。

累计安装
122
累计下载
2,488
星标
-
全站排名
#4,109
全站前 81% · 「AI 智能体」前 87%
平台累计安装累计下载星标评论
ClawHub ↗642,191--
SkillHub ↗1222,488-—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

主文列出人物、项目、任务、事件、文档等实体类型,提供创建、查询、关系连接与校验示例;已有图谱或 schema 应采用追加或合并方式更新。共享记忆会被后续智能体读取或修改,且写入前的校验保证可能有限;不要直接存放秘密或敏感个人信息,并在重要流程中将校验作为审查关卡。

资料发布于 2026-10-29

可以用来做什么

  • 规划实体及其关系的结构化记录
  • 查询项目任务或对象之间的依赖
  • 审查共享图谱的约束和变更流程

什么时候用

  • 需要在共享工作区中维护可查询的结构化实体与关系,并已明确敏感数据处理规则时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

设计任务与项目之间的关系模型,只输出 schema 草案,不写入图谱。
检查一项准备追加的图谱变更,列出校验和敏感数据审查步骤。

月度数据趋势

累计下载2,488
5971,6132,6282026-03 · 累计下载 7372026-05 · 累计下载 1,5442026-06 · 累计下载 1,7072026-07 · 累计下载 1,9782026-08 · 累计下载 2,1142026-09 · 累计下载 2,48826-0326-09
星标-
0112026-03 · 星标 -2026-05 · 星标 -2026-06 · 星标 -2026-07 · 星标 -2026-08 · 星标 -2026-09 · 星标 -26-0326-09

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-092,488—-
2026-082,114+6.9%-
2026-071,978+15.9%-
2026-061,707+10.6%-
2026-051,544—-
2026-03737—-

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

6.5 KB · ClawHub v1.0.0
---
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
展开全部(共 233 行)

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 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
# 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:

```python
# 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

```python
# 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

```bash
# 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

- `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.

「AI 智能体」分类下的热门技能

数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。