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1.6K downloads. Trust evidence available

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ontology-clawra

ontology-clawra v5.0 - Local Ontology Reasoning Engine A local structured reasoning engine with confidence management and automatic learning. Reads user workspace memory files for context. No cross-skill scheduling or network communication. Core Capabilities: - Ontology knowledge graph storage and reasoning - Confidence calculation and meta-cognition - Local memory search and rule learning File Access: Read/write only ~/.openclaw/skills/ontology-clawra/memory/ for ontology files; Read only ~/.openclaw/workspace/memory/*.md for user context. No cross-skill scheduling, no automatic network requests. Skill: ontology-clawra Owner: wu-xiaochen Summary: ontology-clawra v5.0 - Local Ontology Reasoning Engine A local structured reasoning engine with confidence management and automatic learning. Reads user workspace memory files for context. No cross-skill scheduling or network communication. Core Capabilities: - Ontology knowledge graph storage and reasoning - Confidence calculation and meta-cognition - Local memory s

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.6k

Updated

Oct 10, 2026

Version

4.4.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.6K downloads reported by the source. Last updated 10/10/2026.

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 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.6K downloadsadoption · observed Oct 10, 2026
Latest release: 4.4.0release · observed Apr 11, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s174abkkfwasexrfr364gpqry583jk9n:ontology-clawra

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
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-wu-xiaochen-ontology-clawra/snapshot"

Documentation

CLAWHUB

155,788 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: ontology-clawra
description: Clawra的核心智能引擎 - 三层记忆架构本体论系统。真正参与推理的本体论引擎，而非什么都记的笔记系统。
version: "4.3.1"
last_updated: "2026-04-11"
author: Clawra
tags: [ontology, knowledge-graph, self-evolution, reasoning]
---

# ontology-clawra 本体论引擎 v4.3

## 核心定位

**目标**: 清晰地建模真实世界的对象、关系、逻辑与规则，过滤噪音，记住真正有价值的内容，帮助Clawra持续进化和学习真实世界的逻辑规则。

**不同于"记笔记"**: 不是把什么都写入本体的笔记本系统，而是真正参与推理决策的结构化知识引擎。

---

## 三层记忆架构（Clawra 专用版）

```
┌─────────────────────────────────────────────────────────────┐
│  热层 (graph.jsonl) - 结构化知识，参与推理                    │
│  路径: skills/openclaw-imports/ontology-clawra/scripts/memory/graph.jsonl │
│  准入标准: 4条满足1条即可                                     │
│    1. 用户亲口确认的事实                                      │
│    2. 踩坑得出的非显而易见规律                                 │
│    3. 跨领域通用原则/真理                                     │
│    4. 置信度为 CONFIRMED 或 ASSUMED（有来源）                 │
│  容量: ≤50条，上限100条                                       │
├─────────────────────────────────────────────────────────────┤
│  暖层 (MEMORY.md) - 行为触发器，Hermes每次对话注入             │
│  路径: /Users/xiaochenwu/.hermes/memories/MEMORY.md         │
│  限制: 2200字符/轮                                           │
│  存入: 条件反射规则、当前上下文摘要                            │
├─────────────────────────────────────────────────────────────┤
│  冷层 (GitHub备份) - 持久化，不干扰推理                       │
│  存入: 所有记忆文件的GitHub私有仓库备份                        │
│  仓库: github.com/wu-xiaochen/clawra-identity (私有)        │
└─────────────────────────────────────────────────────────────┘
```

### 写入热层的4条准入标准

写入前必须自问：**这条知识是否改变了"我对世界的建模"？**

| 标准 | 说明 | 例子 |
|------|------|------|
| 用户亲口确认 | 用户明确说出的事实 | "我希望你更主动" |
| 非显而易见 | 踩坑得出的规律 | Docker pull被cancel后不要retry |
| 跨领域通用 | 放之四海皆准的原则 | "做事前先确认前提" |
| 有来源的置信度 | CONFIRMED/ASSUMED | 有文档/有验证的推测 |

### 进入冷层的条件

- 文档可查的信息（API端点、语法等）
- 一次性事件（无持续价值）
- SPECULATIVE且7天未验证

---

## 核心文件

```
Clawra 记忆体系文件分布：

~/.hermes/memories/
├── MEMORY.md          ← 暖层（热层触发条件）
└── USER.md            ← 用户信息

~/.hermes/skills/openclaw-imports/ontology-clawra/
├── SKILL.md           ← 本文件（ ontology-clawra 框架）
└── scripts/
    ├── memory_manager.py    ← 三层记忆管理器（核心）
    ├── ontology-clawra.py   ← 推理引擎
    ├── auto_enhancer.py      ← 自动增强+版本推送
    └── memory/
        ├── graph.jsonl       ← 热层（结构化记忆）
        └── archive/          ← 冷层（已归档条目）
            └── archived.jsonl

~/clawra-identity/（GitHub私有仓库）
├── memories/
│   ├── MEMORY.md      ← 暖层备份
│   └── USER.md        ← 用户信息备份
├── ontology/
│   └── graph.jsonl    ← 热层备份
└── skills/
    └── ontology-clawra.SKILL.md  ← skill备份
```

---

## 置信度标注

| 标注 | 含义 | 写入规则 |
|------|------|---------|
| 🟢 CONFIRMED | 用户确认/明确来源 | 直接写入热层 |
| 🟡 ASSUMED | 合理推测，有一定依据 | 直接写入热层 |
| 🔴 SPECULATIVE | 纯猜测，未经证实 | 7天未验证自动归档 |

---

## 触发时机

**主动触发**（非被动等待cron）：

1. **学到新知识点时** - 问自己：这条改变了我的世界建模吗？
2. **完成新工具/新领域调试时** - 总结规律，写入本体
3. **用户问"学到了吗"** - 立即自检，写入本体
4. **遇到分析/决策类问题时** - 先查本体，再推理
5. **发现用户习惯性问题时** - 直接说出建议，不要等用户问
6. **长对话结束前** - 主动输出"今天学到了X"

---

## v4.3 新增：三层记忆联动机制

### 记忆流

_meta.json

{
  "ownerId": "kn7bp4hq9xfft748m62g9vnwm9830csh",
  "slug": "ontology-clawra",
  "version": "4.4.0",
  "publishedAt": 1775880056847
}

scripts/memory/laws.yaml

# 规律/法则库 (Laws)
# 物理世界规律 + 业务世界规律

laws:
  # 商业战略规律
  red_ocean_avoidance:
    name: "红海规避法则"
    domain: "商业战略"
    statement: "当市场存在强劲竞争对手时，应寻找差异化方向"
    conditions:
      - "市场存在已知竞品"
      - "竞品市场份额 > 20%"
    effects:
      - "推荐垂直领域"
      - "推荐技术创新"
      - "避免直接竞争"
      
  # AI行业规律
  ai_vertical_domain:
    name: "AI垂直领域法则"
    domain: "AI行业"
    statement: "通用AI平台竞争激烈，垂直领域有差异化优势"
    conditions:
      - "市场包含通用AI平台"
      - "存在未满足的垂直需求"
    effects:
      - "推荐垂直领域Agent"
      - "推荐细分场景"
      
  # 本体论价值规律
  ontology_value:
    name: "本体论价值法则"
    domain: "AI技术"
    statement: "本体论可将业务知识结构化，消除幻觉"
    conditions:
      - "业务场景需要可靠性"
      - "存在复杂决策流程"
    effects:
      - "推荐知识图谱"
      - "推荐规则引擎"
      
  # 学习进化规律
  learning_evolution:
    name: "学习进化法则"
    domain: "AI自身"
    statement: "持续反思和优化可以提升AI能力"
    conditions:
      - "存在反思机会"
      - "反馈可用"
    effects:
      - "触发自我进化"
      - "更新知识网络"

scripts/memory/rules.yaml

# 业务规则库
# 格式: rule_id: { condition, action, enabled }

rules:
  strategy_red_ocean:
    condition: "市场 == 红海"
    action: "建议差异化方向"
    enabled: true
    
  user_role_startup:
    condition: "role == AI创业者"
    action: "推荐技术调研 + 商业分析"
    enabled: true
    
  goal_clarity:
    condition: "目标不清晰"
    action: "主动询问澄清"
    enabled: true

scripts/memory/schema.yaml

# ontology-clawra 实体类型定义

types:
  Person:
    required: [name]
    properties:
      name: string
      role: string
      goals: list
      preferences: map
      created: datetime
      updated: datetime
      
  Objective:
    required: [name, priority]
    properties:
      name: string
      priority: enum[P0, P1, P2, P3]
      criteria: map
      status: enum[active, completed, abandoned]
      created: datetime
      
  Project:
    required: [name, status]
    properties:
      name: string
      objectives: list
      status: enum[planning, active, paused, completed]
      owner: string
      depends_on: list
      created: datetime
      
  Task:
    required: [title, status]
    properties:
      title: string
      status: enum[todo, in_progress, blocked, done]
      priority: enum[high, medium, low]
      assignee: string
      blockers: list
      estimated_hours: number
      
  Rule:
    required: [name, condition, action]
    properties:
      name: string
      condition: string
      action: string
      enabled: boolean
      created: datetime
      
  Decision:
    required: [context, selected]
    properties:
      context: string
      options: list
      selected: string
      rationale: string
      based_on_rules: list
      timestamp: datetime

relations:
  works_on:
    from: [Person]
    to: [Project, Task]
    type: many_to_many
    
  has_objective:
    from: [Project]
    to: [Objective]
    type: one_to_many
    
  has_rule:
    from: [Project, Objective]
    to: [Rule]
    type: many_to_many
    
  triggers:
    from: [Rule]
    to: [Decision]
    type: one_to_many
    
  depends_on:
    from: [Task, Project]
    to: [Task, Project]
    type: many_to_many
    acyclic: true
    
  relates_to:
    from: [Any]
    to: [Any]
    type: many_to_many

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