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-agent-role-designgit 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-agent-role-design)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/scenario-agent-role-design"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/scenario-agent-role-design/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-agent-role-design"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/scenario-agent-role-design.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 | $0.00092 | $0.01025 |
| Opus 5 | $0.00046 | $0.00513 |
| Sonnet 5 | $0.00018 | $0.00205 |
| Haiku 4.5 | $0.00009 | $0.00103 |
Grade A, and why
scenario-agent-role-design 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
识别场景中的智能体作用与任务
方法骨架
- 从场景目标、当前工作和关键困难出发，识别智能体可以承担的理解、检索、关联、判断、生成、协调与受控执行任务。
- 对每项任务分别判断智能体价值、确定性系统能力和人工责任，不因使用本体而预设必须建设 Agent。
- 智能体任务应落到明确用户、触发、输入、输出、完成条件和失败处理。
- 数据计算、固定校验和事务控制优先交给确定性系统；高风险决定和责任承担保留人工确认。
- 输出的知识与数据需求作为后续场景深描、数据需求和本体范围的输入。
执行步骤
-
建立场景最小认识
- 动作：确认用户、问题、目标结果、现有过程和已知系统。
- 完成标准：能够描述谁在什么情况下需要什么帮助。
-
拆解工作与判断
- 动作：列出当前人工任务、信息处理、判断节点、系统操作、异常和协同环节。
- 完成标准：每项工作有执行者、输入、输出和主要困难。
-
识别 Agent 候选任务
- 动作：判断智能体能否承担信息提取、意图识别、知识检索、跨源关联、规则解释、方案生成、任务协调或工具调用。
- 完成标准：每个候选任务写清价值、所需能力、数据与知识依赖。
-
划分三方边界
- 动作：明确 Agent、确定性系统和人工分别负责什么，以及交接条件。
- 完成标准：计算、判断、审批、执行和责任归属无重叠空白。
-
确定风险和成功条件
- 动作：评估错误后果、可逆性、权限和人工接管，定义任务完成条件和效果指标。
- 完成标准：每项 Agent 任务有自动化等级、人工介入点和验收方式。
-
形成后续建模输入
- 动作：汇总 Agent 任务需要的数据、知识、语义、规则、权限和Action。
- 完成标准：能够交给场景深描和数据需求分析继续细化。
固定输出
- 智能体作用说明
- Agent 候选任务清单与优先级
- 用户、Agent、确定性系统和人工责任矩阵
- 每项任务的触发、输入、输出、完成条件和失败处理
- 自动化等级、权限、人工介入和风险清单
- 数据、知识、本体与工具能力需求
- 保留、缩小或取消 Agent 化的判断
使用边界
- 场景信息不足时先给候选任务和澄清清单，避免直接确定自动执行。
- 固定公式、精确计算、状态事务和硬约束由确定性能力承接，并通过工具供 Agent 调用。
- 智能体只做简单查询且无需关系、规则和追溯时，本体范围可以收缩或取消。
- 具体动作的重试、降级和结果回写交给
action-contract-execution-feedback-loop。
相关 Skills
depends-on→ontology-ai-scenario-fit-and-spike；先完成本体适配初判。feeds-into→business-scenario-deep-analysis；把候选任务放回完整业务过程验证。composes-with→risk-based-agent-action-modes；高风险任务继续确定行动模式。
审计信息
- 首次公开版本：2026-08-31
- 来源说明：面向企业智能体场景设计独立整理。
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 · 73 lines · 92 tokens per session scan A 335f1895974a
scenario-agent-role-design 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 92 tokens to every session and 1,025 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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