point-line-plane-ontology-scaling

point-line-plane-ontology-scaling is a skill for Codex from SuperChason/ontology-driven-ai-data-management-skills. It costs 86 tokens per session (1,578 once invoked), scanned A, original, MIT.

A planning method for expanding an ontology from one proven business scenario to shared concepts across a field and then across fields. “Point, line, plane” describes these increasing levels of reuse.

In plain words
What is it for?
Use it to decide when to extract shared concepts, connect related workflows, and coordinate separate domain models through mappings and federated queries.
Why use it?
It provides a controlled way to reuse concepts while avoiding premature company-wide standardisation.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to decide when to extract shared concepts, connect related workflows, and coordinate separate domain models through mappings and federated queries.

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Install with agentmods
npx agentmods add skills/superchason/ontology-driven-ai-data-management-skills/point-line-plane-ontology-scaling
Install

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.

Any agent
npx skills add SuperChason/ontology-driven-ai-data-management-skills --skill point-line-plane-ontology-scaling
Clone the repo
git clone --depth 1 https://github.com/SuperChason/ontology-driven-ai-data-management-skills

Made for: Codex.

Wrote 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.

agentmods badge for point-line-plane-ontology-scaling

README.md
[![agentmods](https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/point-line-plane-ontology-scaling/github.svg)](https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/point-line-plane-ontology-scaling)
Your own site
<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.

agentmods 80×15 button for point-line-plane-ontology-scaling

Your own site · 80×15
<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>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,578 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash ac05a937e812, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/point-line-plane-ontology-scaling/SKILL.md · 108 lines

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

  1. 首个本体场景验证成功,准备扩大范围
  2. 多个局部本体开始重复或冲突
  3. 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

  • 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。
  • 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:

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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-onontology-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-withfive-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。

Read the full file on GitHub · 108 lines

Files

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.

Changes

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.

  1. 4d ago Changed ac05a937e812
  2. 8d ago Changed · +7 lines 98296c5c3163
  3. 12d ago First seen · 101 lines · 86 tokens per session scan A 82e46fc8d8ef

Subscribe to this mod's changes

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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