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 business-scenario-deep-analysisgit 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/business-scenario-deep-analysis)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/business-scenario-deep-analysis"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/business-scenario-deep-analysis/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/business-scenario-deep-analysis"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/business-scenario-deep-analysis.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 | $0.00098 | $0.00997 |
| Opus 5 | $0.00049 | $0.00498 |
| Sonnet 5 | $0.00020 | $0.00199 |
| Haiku 4.5 | $0.00010 | $0.00100 |
Grade A, and why
business-scenario-deep-analysis 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
业务场景完整梳理与建设范围确认
方法骨架
- 把场景从一句需求展开为有开始、结束、角色、过程、判断、动作、异常和验收的业务闭环。
- 将 Agent 候选任务放回真实业务过程验证，删除价值不足或缺少责任边界的任务。
- 同时梳理现状和目标过程，保留业务规则、系统能力和人工责任。
- 只识别对象、数据和知识需求的候选范围，正式语义定义留给后续阶段。
- 场景规格完成后重新确认本体范围、子本体边界和首期建设内容。
执行步骤
-
明确业务目标与角色
- 动作：写清目标用户、业务结果、责任主体、约束和评价方式。
- 完成标准：每个目标有责任角色和可观察结果。
-
确定场景边界
- 动作：定义触发事件、开始和结束、组织与系统范围、上游输入、下游结果和排除项。
- 完成标准：能够判断一项内容是否属于当前场景。
-
还原现状过程
- 动作：逐步记录参与者、输入、活动、判断、输出、系统操作、等待、返工和异常。
- 完成标准：关键痛点能够定位到具体步骤和证据。
-
设计目标过程
- 动作：把 Agent、确定性系统和人工分工嵌入流程，明确交接、权限、异常和回退。
- 完成标准：每项 Agent 任务有上下游、完成条件和失败处理。
-
识别建模候选
- 动作：列出业务对象、事件、状态、数据、规则、案例、权限、动作和目标候选。
- 完成标准：候选项均关联具体任务或判断，不收录与场景无关内容。
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确认建设范围
- 动作：重新判断本体支撑点、子本体划分、数据和知识缺口、首期范围与验收样本。
- 完成标准：形成可交给数据需求、知识结构和语义建模的正式场景规格。
固定输出
- 场景目标、角色和责任
- 触发、开始、结束、输入和输出
- 当前过程、目标过程和关键判断节点
- 业务对象、事件、状态、规则、权限和动作候选
- Agent 任务规格与三方交接
- 正常、边界、异常和权限场景
- 系统范围、数据候选和知识候选
- 本体支撑点、子本体边界和首期范围
- 真实样本需求与场景验收标准
使用边界
- 业务对象和关系在本阶段保持候选，正式定义进入场景语义模型。
- 数据表、字段和接口在下一阶段做准备度确认。
- 只有理想流程且缺少现状证据时，明确标记假设并补充真实案例。
- 场景深描发现本体价值只存在于局部任务时，缩小本体范围并更新适配结论。
相关 Skills
depends-on→scenario-agent-role-design；Agent 候选任务需要在完整过程里复核。feeds-into→scenario-data-requirements-readiness和scenario-related-knowledge-structure。
审计信息
- 首次公开版本：2026-08-31
- 来源说明：面向场景驱动本体工程独立整理。
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.
- 8d ago First seen · 74 lines · 98 tokens per session scan A 9a3c96c5e5b3
business-scenario-deep-analysis 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 98 tokens to every session and 997 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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