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 data-to-ontology-mapping-and-instantiationgit 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/data-to-ontology-mapping-and-instantiation)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/data-to-ontology-mapping-and-instantiation"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/data-to-ontology-mapping-and-instantiation/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/data-to-ontology-mapping-and-instantiation"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/data-to-ontology-mapping-and-instantiation.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 | $0.00106 | $0.01172 |
| Opus 5 | $0.00053 | $0.00586 |
| Sonnet 5 | $0.00021 | $0.00234 |
| Haiku 4.5 | $0.00011 | $0.00117 |
Grade A, and why
data-to-ontology-mapping-and-instantiation 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 5d 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
数据到本体映射与实例构建
方法骨架
- 使用前序阶段已经确认的数据来源、字段位置、业务语义和逻辑本体，建立数据到类、属性、关系、概念和实例的映射。
- 数据记录映射为类实例，字段映射为数据属性，关联键或推导结果映射为对象关系，代码值映射为术语概念或受控值。
- 跨系统实体先确定统一标识和消歧规则，再生成 URI 和关系实例。
- 每个实例保留来源系统、记录标识、时间、版本、转换和授权信息。
- 本阶段直接引用已定位的系统、表、字段和接口，不重新承担物理数据源发现。
需要详细映射字段和实例规则时读取 数据到本体映射契约。
执行步骤
-
检查前置输入
- 动作：确认逻辑本体版本、数据语义、物理来源、样本、权限和质量状态一致。
- 完成标准：未定位来源或未确认语义单列为阻断，不进入正式实例生成。
-
建立记录到类映射
- 动作：定义哪类数据记录生成哪个本体类实例，以及筛选、合并和排除条件。
- 完成标准：每个实例类有数据粒度、来源和生成条件。
-
建立字段到属性映射
- 动作：映射数据属性、类型、单位、时间、空值、枚举和转换规则。
- 完成标准：每个必需属性有来源、转换和缺失处理。
-
建立关系与概念映射
- 动作：把外键、映射表、事件关联或推导结果转换为对象关系，把代码值转换为受控概念或状态。
- 完成标准：关系方向、证据、有效期和冲突处理明确。
-
设计实体标识与版本
- 动作：定义业务键、跨源实体合并、URI、版本实例、历史保留和删除策略。
- 完成标准：同一业务实体稳定识别，版本变化不会覆盖必要历史。
-
生成并校验样例实例
- 动作：使用真实或脱敏样本生成实例，执行数据类型、必填、关系和来源校验。
- 完成标准：样例实例满足逻辑本体和 SHACL 约束，问题可回溯前序阶段。
-
定义增量实例维护
- 动作：设计新增、更新、删除、失效、重放和异常恢复规则。
- 完成标准：运行时数据变化能够安全反映到实例层并保留审计。
固定输出
- 数据记录—本体类映射表
- 数据字段—本体属性映射表
- 外键/关联—对象关系映射表
- 编码值—术语概念或状态映射表
- 业务事件—事件类映射表
- 实体统一标识和 URI 规则
- 类型、单位、时间和空值转换规则
- 实例与关系生成规则
- 样例实例和来源证明
- 增量更新、失效、删除和异常处理规则
- 实例校验报告与上游问题清单
使用边界
- 查找来源系统、表、字段和接口由
scenario-data-requirements-readiness完成。 - 字段在业务上表示什么由
scenario-related-semantic-modeling完成。 - 本 Skill 只把已完成来源定位和语义定义的数据映射到逻辑本体。
- 来源缺失、权限未授权或语义冲突时，不生成生产实例。
- 推导关系必须标记规则、版本和证据，不能伪装成源系统直接事实。
相关 Skills
depends-on→scenario-data-requirements-readiness、scenario-related-semantic-modeling和ontology-logical-model-generation。feeds-into→ontology-model-multilayer-quality-gate与ontology-golden-case-testing。
审计信息
- 首次公开版本：2026-08-31
- 来源说明：面向企业数据本体化和实例治理独立整理。
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.
- 5d ago Changed · +9 tokens per session 4f46211be812
- 8d ago First seen · 83 lines · 97 tokens per session scan A 289692b67cc3
data-to-ontology-mapping-and-instantiation is a skill published in the GitHub repository SuperChason/ontology-driven-ai-data-management-skills (10 stars, last pushed 6d ago), licensed MIT. It adds 106 tokens to every session and 1,172 once invoked, about $0.0005 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.
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