Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add L-LesterYu/OpenClaw-hot-skills-zh --skill ontology-zhgit clone --depth 1 https://github.com/L-LesterYu/OpenClaw-hot-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/ontology-zh)<a href="https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/ontology-zh"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/ontology-zh/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/ontology-zh"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/ontology-zh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.01793 |
| Opus 5 | $0.00052 | $0.00897 |
| Sonnet 5 | $0.00021 | $0.00359 |
| Haiku 4.5 | $0.00010 | $0.00179 |
Grade A, and why
ontology-zh scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to ontology — 62 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
本体 (Ontology)
类型化词汇表 + 约束系统，用于将知识表示为可验证的图结构。
核心概念
一切皆为实体，拥有类型、属性以及与其他实体的关系。每次变更都会在提交前进行类型约束验证。
实体: { id, type, properties, relations, created, updated }
关系: { from_id, relation_type, to_id, properties }
何时使用
| 触发词 | 操作 |
|---|---|
| "记住……" | 创建/更新实体 |
| "我知道关于X什么？" | 查询图 |
| "将X关联到Y" | 创建关系 |
| "显示项目Z的所有任务" | 图遍历 |
| "什么依赖于X？" | 依赖查询 |
| 规划多步骤工作 | 建模为图变换 |
| 技能需要共享状态 | 读写本体对象 |
核心类型
# 智能体与人员
Person: { name, email?, phone?, notes? }
Organization: { name, type?, members[] }
# 工作
Project: { name, status, goals[], owner? }
Task: { title, status, due?, priority?, assignee?, blockers[] }
Goal: { description, target_date?, metrics[] }
# 时间与地点
Event: { title, start, end?, location?, attendees[], recurrence? }
Location: { name, address?, coordinates? }
# 信息
Document: { title, path?, url?, summary? }
Message: { content, sender, recipients[], thread? }
Thread: { subject, participants[], messages[] }
Note: { content, tags[], refs[] }
# 资源
Account: { service, username, credential_ref? }
Device: { name, type, identifiers[] }
Credential: { service, secret_ref } # 切勿直接存储密钥
# 元数据
Action: { type, target, timestamp, outcome? }
Policy: { scope, rule, enforcement }
存储
默认路径：memory/ontology/graph.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"}
通过脚本或直接文件操作进行查询。对于复杂图结构，建议迁移到 SQLite。
仅追加规则
在处理现有的本体数据或 schema 时，应追加/合并变更，而非覆盖文件。这保留了历史记录，避免覆盖先前的定义。
工作流
创建实体
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[email protected]"}'
查询
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
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 233 lines · 104 tokens per session scan A f74bdb70072f
ontology-zh is a skill published in the GitHub repository L-LesterYu/OpenClaw-hot-skills-zh (54 stars, last pushed 5mo ago), licensed MIT. It adds 104 tokens to every session and 1,793 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to ontology, differing in 62 lines, and is treated as a copy.
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