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 point-line-plane-ontology-scalinggit 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/point-line-plane-ontology-scaling)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/point-line-plane-ontology-scaling"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/point-line-plane-ontology-scaling/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/point-line-plane-ontology-scaling"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/point-line-plane-ontology-scaling.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 | $0.00086 | $0.01578 |
| Opus 5 | $0.00043 | $0.00789 |
| Sonnet 5 | $0.00017 | $0.00316 |
| Haiku 4.5 | $0.00009 | $0.00158 |
Grade A, and why
point-line-plane-ontology-scaling 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
本体“点—线—面”扩展与复用
方法骨架
- 用点、线、面控制本体从单场景到企业级知识结构的扩展节奏。
- 点围绕一个高价值闭环,建立最小对象、规则、事实和Action。
- 线在同一领域内抽取公共概念,连接多个已验证场景和流程。
- 面识别跨领域交互点,通过共享顶层概念、映射和联邦查询实现协同。
- 扩展依据复用证据发生,保留领域自治,避免提前强行统一。
- 每一层都要有业务价值、质量基线和下一层准入条件。
触发场景
用户会在什么情境下需要这个 Skill
- 首个本体场景验证成功,准备扩大范围
- 多个局部本体开始重复或冲突
- 需要规划领域本体和跨域语义协同
语言信号
- “怎么从点扩到线和面”
- “这个本体怎么复用到其他场景”
- “跨领域概念冲突怎么处理”
- 英文信号:point-line-plane, ontology scaling, federated ontology
与相邻 Skill 的区分
- 与
ontology-ai-scenario-fit-and-spike:场景适配与穿刺负责找到并验证第一个点;本 skill 负责成功后的扩展。 - 与
five-ring-ontology-engineering-lifecycle:五环描述工程生命周期;点线面描述业务范围和语义复用层级。
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:
-
确认点已成立
- 动作:检查单场景业务价值、语义质量、运行闭环和复用线索。
- 完成标准:点的验收通过且剩余风险可控。
- 判停条件:若点未通过,回到穿刺验证,禁止扩大。
-
抽取领域公共层
- 动作:对多个已验证场景比较对象、术语、规则和Action,抽取稳定公共概念。
- 完成标准:公共层有两处以上复用证据,场景差异保留在局部层。
-
贯通领域线
- 动作:建立领域内公共层、场景层、实例层和动作层映射。
- 完成标准:跨场景查询或流程能够沿统一语义运行并回归通过。
-
识别跨域交互点
- 动作:只处理真实业务链路上的共享对象、事件、指标和权限冲突。
- 完成标准:每个映射有来源、责任人和冲突处置。
-
形成联邦面
- 动作:通过桥接本体、映射规则和联邦查询协同,保留领域版本和自治。
- 完成标准:跨域用例通过,且没有强制覆盖领域有效语义。
固定输出
- 单点场景成立证据卡:业务价值、语义质量、运行闭环、验收结论、剩余风险和复用线索
- 场景语义复用比较表:场景、对象、术语、属性、关系、规则、指标、Action、一致项和差异项
- 领域公共语义资产清单:资产编号、定义、两处以上复用证据、主责方、使用场景、版本和状态
- 领域线映射表:公共层、场景层、实例层、动作层的引用、扩展、映射和回归关系
- 跨域协同需求表:需求编号、共享对象或事件、提供方、使用方、交付内容、映射或桥接方式、冲突、责任和状态
- 联邦面方案:顶层共享语义、领域自治边界、桥接本体、联邦查询、版本和权限机制
- 点—线—面扩展路线图与分层准入条件
公共资产只从已验证的稳定共性中抽取,场景差异和领域有效语义保留在自治层。
使用边界
不要在以下情况使用
- 首个场景尚未形成价值闭环
- 为了统一术语而统一,没有跨域任务支撑
- 试图一次性重构全企业数据和系统
常见失败模式
- 用全量模型重构适配企业业务:企业知识被压入模型参数,适配成本从局部语义治理升级为模型训练、维护和全量数据治理。
- 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。
- 强行统一跨领域概念与规则:全局抽象覆盖局部上下文,领域差异被压平后又在推理和行动阶段重新爆发。
- 未经穿刺验证就扩大建设范围:技术可行性、业务价值和组织协同的未知项同时被延后,试错成本随范围线性或更快增长。
使用折扣与复核要求
- 点线面没有给出固定组织结构,跨域权责和标准生效仍需企业治理机制确认。
- 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;场景适配与穿刺负责找到并验证第一个点;本 skill 负责成功后的扩展。composes-with→five-ring-ontology-engineering-lifecycle;五环描述工程生命周期;点线面描述业务范围和语义复用层级。
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 ac05a937e812
- 8d ago Changed · +7 lines 98296c5c3163
- 12d ago First seen · 101 lines · 86 tokens per session scan A 82e46fc8d8ef
point-line-plane-ontology-scaling 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 86 tokens to every session and 1,578 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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