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 SuperChason/ontology-driven-ai-data-management-skills --skill five-ring-ontology-engineering-lifecyclegit clone --depth 1 https://github.com/SuperChason/ontology-driven-ai-data-management-skillsWrote 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/superchason/ontology-driven-ai-data-management-skills/five-ring-ontology-engineering-lifecycle)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/five-ring-ontology-engineering-lifecycle"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/five-ring-ontology-engineering-lifecycle/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/superchason/ontology-driven-ai-data-management-skills/five-ring-ontology-engineering-lifecycle"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/five-ring-ontology-engineering-lifecycle.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 | $0.00076 | $0.01591 |
| Opus 5 | $0.00038 | $0.00796 |
| Sonnet 5 | $0.00015 | $0.00318 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
five-ring-ontology-engineering-lifecycle 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
本体工程“五环联动”生命周期
方法骨架
- 用预处理、建模、入库、平台、实施五个环节规划本体全生命周期。
- 预处理把知识原料变成清晰、正确、完整、最简的输入。
- 建模将输入形式化，并经过模型、工具、专家和用例校验。
- 入库完成注册、版本、来源、权限和生产准入。
- 平台提供可视化、存储、检索、服务和运行监控能力。
- 实施把本体放进真实闭环，通过建用优复和双线运维持续演进。
- 场景交付链路作为五环内的施工顺序：场景与Agent定位、场景深描、数据准备、知识结构、语义模型、概念与逻辑模型、实例、验证、发布和运行。
触发场景
用户会在什么情境下需要这个 Skill
- 需要规划企业本体建设项目
- 已有模型原型但缺少上线和运营路径
- 要明确本体团队、平台和业务实施的责任边界
语言信号
- “按五环规划实施”
- “本体项目需要哪些阶段和产出”
- “从知识采集到上线怎么走”
- 英文信号：ontology lifecycle, five-ring, production readiness
与相邻 Skill 的区分
- 与
ontology-model-multilayer-quality-gate：本 skill 规划全生命周期；多层质量门专注建模环节的质量准入。 - 与
point-line-plane-ontology-scaling：本 skill 管理工程环节；点线面管理业务范围的扩展顺序。
执行步骤
按当前任务选择必要步骤；已有可靠成果直接复用：
-
定义五环目标
- 动作：为预处理、建模、入库、平台、实施分别写清输入、输出、责任和质量门。
- 完成标准：五环均有责任人、产出物和验收条件。
-
评估当前成熟度
- 动作：盘点已有资料、模型、存储、工具、服务、场景和运营机制。
- 完成标准：每一环标为可用、需补强或缺失，并附证据。
-
安排最短闭环
- 动作：调用
ontology-scenario-delivery，围绕首个真实场景安排从Agent任务到模型、实例、验证和发布的必要能力。 - 完成标准：任务依赖和先后顺序明确，未引入无场景支撑的平台能力。
- 动作：调用
-
设置生产门
- 动作：在注册、发布和运行前检查版本、权限、测试、回滚、监控和责任。
- 完成标准：所有阻断项关闭后才允许进入生产。
-
建立双线运营
- 动作：分别监控源数据变化与语义模型变化，按建用优复处理反馈。
- 完成标准：形成问题分流、变更评审、回归测试和复用台账。
固定输出
- 五环生命周期总图：预处理、建模、入库、平台和实施的输入、输出、责任和依赖
- 当前成熟度评估表：环节、现有能力、证据、可用、需补强或缺失结论和影响
- 阶段产物与准入矩阵：环节、必备产物、质量门、责任人、验收人和进出条件
- 最短建设闭环路线图：场景、任务、数据、知识、模型、实例、验证、发布和运行的顺序
- 角色与职责矩阵：业务、数据、模型、平台、安全、测试和运营责任
- 生产准入、版本、权限、回滚与监控清单
- 双线运营与反馈台账：数据变化、语义变化、问题分流、影响、评审和回归状态
每一环都必须对应真实场景与可验收产物；未关闭的生产阻断项不得用计划表达代替准入结论。
使用边界
不要在以下情况使用
- 仅需制作一次性概念演示
- 没有明确业务场景却先建设大而全平台
- 把五环当成固定采购清单，忽略现有能力
常见失败模式
- 用不成熟的AI能力自动治理AI：AI输出从建议变成控制面配置，伪相关、错误因果和识别偏差被持续复用。
- 为建设本体而建设本体博物馆：项目目标从解决问题偏移到交付模型资产，缺少场景牵引、调用契约和量化验收。
- 本体静态交付后缺少版本和反馈运营：业务变化与本体发布脱节，运行异常也无法回流建模端，旧版本继续影响Agent决策。
- 新本体与存量数据和系统形成新孤岛：语义层与事实源、既有知识资产及执行系统没有稳定映射，模型无法随真实业务同步。
使用折扣与复核要求
- 通用方法未规定具体产品和组织规模，实施节奏需结合团队能力与存量架构。
- 大模型生成形式结构无法直接证明业务语义正确，生产使用需保留专家确认、工具校验、真实用例和审计记录。
相关 Skills
depends-on→ontology-ai-scenario-fit-and-spike。composes-with→ontology-scenario-delivery；五环负责项目生命周期，总控 Skill 负责单场景施工和交付物。composes-with→ontology-model-multilayer-quality-gate；本 skill 规划全生命周期；多层质量门专注建模环节的质量准入。
What ships with it
3 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.
- 4d ago Changed bdbf9358517b
- 8d ago Changed · +9 lines 2220620d5945
- 12d ago First seen · 100 lines · 76 tokens per session scan A 12777100b77b
five-ring-ontology-engineering-lifecycle is a skill published in the GitHub repository SuperChason/ontology-driven-ai-data-management-skills (10 stars, last pushed 5d ago), licensed MIT. It adds 76 tokens to every session and 1,591 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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