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 scenario-data-requirements-readinessgit 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/scenario-data-requirements-readiness)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/scenario-data-requirements-readiness"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/scenario-data-requirements-readiness/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/scenario-data-requirements-readiness"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/scenario-data-requirements-readiness.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 | $0.00094 | $0.01129 |
| Opus 5 | $0.00047 | $0.00564 |
| Sonnet 5 | $0.00019 | $0.00226 |
| Haiku 4.5 | $0.00009 | $0.00113 |
Grade A, and why
scenario-data-requirements-readiness 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 8d 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
场景数据需求与数据准备度分析
方法骨架
- 从 Agent 任务、业务判断和动作逐项反推所需事实，再确定数据对象、字段、粒度、时间和质量要求。
- 业务、财务和 IT 可作为数据领域标签；同时标记主数据、事实、规则配置、指标、结果、日志等数据角色。
- 在本阶段完成来源系统、数据集、表、字段和接口定位，明确责任、权限、更新和样本可得性。
- 业务与财务数据可能交叉，按业务含义和实际用途允许多标签，不用部门归属替代语义判断。
- 输出数据准备度和缺口对 Agent 任务的影响，关键数据不可得时缩小场景范围或设计样本验证。
- 数据到本体类、属性、关系和实例的对应留给后续映射阶段。
需要详细字段模板时读取 数据需求与准备度契约。
执行步骤
-
从任务反推数据
- 动作：对每项 Agent 任务列出判断、行动、所需事实和最低数据集合。
- 完成标准：每项数据需求都有明确用途和对应任务。
-
分类数据领域与角色
- 动作：标注业务、财务、IT 技术等领域，以及主数据、事实、规则配置、指标、结果、日志和审计角色。
- 完成标准：交叉数据保留多标签，IT 技术数据和业务语义数据边界明确。
-
明确数据要求
- 动作：定义对象、字段、粒度、时间范围、版本、更新频率、质量、权限和样本要求。
- 完成标准：需求可以被数据责任人核验。
-
定位物理来源
- 动作：确认来源系统、数据集、表、字段、接口、责任人和访问方式。
- 完成标准：已定位数据可直接追溯，未定位项有责任人和计划。
-
检查跨系统关联
- 动作：检查统一标识、主数据、映射表、编码、时间、版本和状态对齐条件。
- 完成标准：每个跨系统关联有现有键、映射方案或缺口结论。
-
评估准备度与影响
- 动作：将数据标为可用、需清洗、缺映射、缺权限、时效不足、只有人工记录或缺失，评估对任务的影响。
- 完成标准：阻断项、替代数据、样本方案和首期调整明确。
固定输出
- Agent 任务—数据需求矩阵
- 业务、财务、IT 数据清单与数据角色
- 数据对象、字段、指标、粒度、时间、版本和质量要求
- 来源系统—数据集—表—字段—接口登记表
- 数据责任人、权限、敏感级别和使用限制
- 跨系统标识与关联需求
- 数据准备度报告
- 数据缺口、任务影响与补充方案
使用边界
- 项目主数据即使由 IT 部门维护，也按其业务含义归入业务数据，并增加 IT 管理标签。
- 接口定义、系统日志、调用审计、权限元数据和更新时间通常归入 IT 技术数据。
- 在本阶段记录表字段的物理位置；字段的统一业务定义进入场景语义模型。
- 在本阶段不生成本体实例；后续映射 Skill 使用已确认的物理来源和语义定义。
相关 Skills
depends-on→business-scenario-deep-analysis；任务、判断和动作是数据需求的依据。feeds-into→scenario-related-knowledge-structure与scenario-related-semantic-modeling。- 与
data-to-ontology-mapping-and-instantiation区分：本 Skill 定位真实数据；后者把数据对应到本体模型。
审计信息
- 首次公开版本：2026-08-31
- 来源说明：面向场景驱动的企业数据准备独立整理。
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.
- 8d ago First seen · 77 lines · 94 tokens per session scan A 81cdd940b79e
scenario-data-requirements-readiness 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 94 tokens to every session and 1,129 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.
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.