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 ontology-logical-model-generationgit 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/ontology-logical-model-generation)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/ontology-logical-model-generation"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/ontology-logical-model-generation/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/ontology-logical-model-generation"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/ontology-logical-model-generation.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 | $0.00113 | $0.01144 |
| Opus 5 | $0.00056 | $0.00572 |
| Sonnet 5 | $0.00023 | $0.00229 |
| Haiku 4.5 | $0.00011 | $0.00114 |
Grade A, and why
ontology-logical-model-generation 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.
What it actually says
逻辑本体模型生成
方法骨架
- 把概念模型按已确认或候选状态形式化为机器可解析的类、属性、关系、词汇、约束、规则接口和查询模板。
- 先选择满足场景需求的最小技术组合；RDF/OWL/SKOS/SHACL/SPARQL按实际任务使用，流程、规则和权限可映射到企业现有引擎。
- 业务语义编号和来源必须保留在逻辑元素元数据中，防止形式模型失去业务证据。
- 候选语义与生产语义分模块或分状态发布，禁止在生成过程中把未确认内容自动升级。
- 按交付用途执行技术自检；正式使用前进入相应技术与业务质量门。
需要形式化映射规则时读取 逻辑模型契约。
执行步骤
-
确定形式化配置
- 动作：选择命名空间、URI、模块、版本、依赖、序列化格式和需要的标准能力。
- 完成标准：技术选择能够支持当前查询、校验、推理和集成需求。
-
生成类、属性和词汇
- 动作：把对象、概念继承、数据属性、对象关系、术语和分类转换为逻辑元素。
- 完成标准：元素与概念模型编号一一对应，名称和定义完整。
-
生成约束与规则接口
- 动作：把数量、类型、值域、时间、一致性和权限前置约束转为 SHACL 或目标引擎可执行结构。
- 完成标准：每条关键约束有来源、严重级别和测试样本。
-
生成查询和动作接口
- 动作：为 Agent 关键任务建立查询模板，并为动作保留输入、输出、前置、效果和权限契约。
- 完成标准：每项关键任务至少有可验证的模型访问路径。
-
生成元数据与追溯
- 动作：记录模型标识、版本、状态、适用范围、责任、依赖和语义来源。
- 完成标准：逻辑元素能够回溯场景语义、概念视图和证据。
-
执行生成后自检
- 动作：解析文件并检查名称、引用、孤立元素、依赖和候选隔离，生成待独立审核的问题单。
- 完成标准：文件可解析，自检结果不冒充独立质量结论。
固定输出
- 本体命名空间、URI 和模块规则
- RDF/OWL 类、属性和关系模型
- SKOS 术语和分类模型
- SHACL 约束模型
- 规则或决策表的目标实现映射
- SPARQL 查询模板
- Action 契约和权限引用
- 模型元数据、版本和依赖
- 语义—逻辑元素追溯表
- 生成后自检报告与独立审核输入
使用边界
- 业务语义来源不明、冲突未裁决或概念模型未确认时，隔离受影响元素，列出假设或冲突分支，继续可完成的草稿生成和技术验证；不赋予其生产效力。
- SWRL、OWL-S、ODRL、BPMN 等根据目标平台选择，不设为所有场景必交格式。
- SHACL 用于数据图约束，规则引擎、流程引擎和 IAM 继续承担各自运行职责。
- 自检结果只支持其检查范围；生产准入仍需项目规定的独立质量审核和真实业务用例，候选交付无需等待这些外部环节。
相关 Skills
depends-on→ontology-conceptual-model-design。feeds-into→data-to-ontology-mapping-and-instantiation与ontology-model-multilayer-quality-gate。
审计信息
- 首次公开版本：2026-08-31
- 来源说明：结合开放语义标准和企业本体工程实践独立整理。
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.
- 4d ago Changed · +17 tokens per session d51fcadd531f
- 8d ago First seen · 77 lines · 96 tokens per session scan A 2eac047bc2ce
ontology-logical-model-generation 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 113 tokens to every session and 1,144 once invoked, about $0.0006 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 2026-09-04.
Other skills, from other repositories
open-ontologies
AI-native ontology engineering using 50+ MCP tools backed by an in-memory Oxigraph triple store. Build, validate, query, and govern RDF/OWL ontologies with a generate-validate-iterate loop. Use when building ontologies, knowledge graphs, RDF data, SPARQL queries, BORO/4D modeling, SHACL validation, clinical…
mykg
Run mykg knowledge-graph commands inside Claude Code from one slash command /mykg. The user describes intent in natural language (extract, append, sync, resume, approve, walkthrough, parse-docs, fetch-web, query); the skill parses intent, builds the right mykg CLI command from the live --help output, confirms, runs…
ontology-ontology-architect
Design a domain ontology from evidence using layered modeling, stable concept definitions, explicit relations, SKOS-first structure, and selective OWL formalization with recorded modeling decisions.
graphify
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
graph-mutation-plan
Cookbook for composing an applygraphmutations plan — stable entitykey patterns, the canonical label/edge vocabulary, evidence/invalidation/confidence discipline, and a worked example. Load this when building a non-trivial mutation plan.
understand-knowledge
Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.