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 fact-reason-action-business-loopgit 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/fact-reason-action-business-loop)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/fact-reason-action-business-loop"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/fact-reason-action-business-loop/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/fact-reason-action-business-loop"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/fact-reason-action-business-loop.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 | $0.00084 | $0.01460 |
| Opus 5 | $0.00042 | $0.00730 |
| Sonnet 5 | $0.00017 | $0.00292 |
| Haiku 4.5 | $0.00008 | $0.00146 |
Grade A, and why
fact-reason-action-business-loop 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.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
“事实—事理—行动”业务闭环建模
方法骨架
- 把业务问题拆成事实、事理和行动三个相互校验的层次。
- 事实记录对象、关系、状态、时间、来源和版本,回答当前发生了什么。
- 事理表达规则、因果、约束、流程和状态变化,回答为什么以及接下来可能怎样。
- 行动定义可执行能力、权限、前置条件和结果,回答允许做什么。
- 推理从事实出发受事理约束,产出行动建议;执行反馈再更新事实并校正规则。
- 交付物是一条有证据、有推理、有受控行动、有反馈的业务闭环。
触发场景
用户会在什么情境下需要这个 Skill
- 需要把模糊业务描述转成Agent可用模型
- 需要解释一个判断怎样落到系统动作
- 跨系统事实、规则和流程混在一起难以梳理
语言信号
- “帮我按事实事理行动拆解”
- “这个判断依据和动作怎么串起来”
- “怎样形成业务闭环”
- 英文信号:fact-reason-action, business loop, traceable action
与相邻 Skill 的区分
- 与
seven-plus-one-semantic-mapping:本 skill 先建立业务闭环骨架;7+1负责把骨架映射成更完整的语义规范。 - 与
fact-reason-goal-explainable-decision:本 skill 覆盖从认知到行动的整体结构;事实事理目标专注单次决策推理。
执行步骤
按当前任务选择必要步骤;已有可靠成果直接复用:
-
界定闭环
- 动作:写清触发事件、目标结果、责任主体、起止状态和涉及系统。
- 完成标准:闭环边界能用一句话说明,范围外事项单列。
-
登记事实
- 动作:列出对象、关系、状态、时间、来源、版本和可信度。
- 完成标准:每个参与推理的事实都有来源,缺失项明确标为待核实。
-
表达事理
- 动作:把规则、因果、约束、例外、流程和状态转换条件写成可判断语句。
- 完成标准:每条事理能引用事实并产生可核验结论;冲突规则有优先级或升级路径。
-
定义行动
- 动作:列出建议、查询、建单、审批或系统调用,注明权限、前置条件和结果。
- 完成标准:行动均能映射到责任人或现有系统能力。
- 判停条件:若行动无法授权或无法回传结果,停在建议层。
-
闭合反馈
- 动作:定义成功、失败、异常和人工调整怎样回写事实、触发后续动作或修正规则。
- 完成标准:输出完整的事实—事理—行动—反馈表及未闭合项。
固定输出
- 业务闭环边界卡:触发、目标、责任主体、起止状态、系统范围和范围外事项
- 事实表:事实编号、对象、关系、状态、时间、来源、版本、可信度和核验状态
- 事理表:事理编号、规则或因果、适用条件、优先级、例外、冲突和证据
- 行动表:行动编号、目标、对象、责任主体、权限、前置条件、执行方式和预期结果
- 事实—事理—行动追溯矩阵:输入事实、应用事理、中间结论、可选行动和最终行动
- 执行反馈与事实回写表:成功、失败、异常、人工调整和规则修正
- 未闭合项、风险边界与下一步清单
表中条目使用稳定编号串联,每个结论都能回到事实和事理,每个行动都有授权与结果回写路径。
使用边界
不要在以下情况使用
- 只需要解释术语或摘要文档
- 业务目标和触发事件尚未明确
- 行动由高风险系统执行且没有授权与审计机制
常见失败模式
- 数据连通但业务语义仍然隔离:多源系统只有数据级连接,缺少统一概念、关系、规则和场景上下文,模型只能按通用概率补齐含义。
- 用通用逻辑直接驱动自主决策:概率推理替代了企业决策逻辑,执行工具又把语言偏差转化成了现实状态变更。
- 把文档检索等同于可执行知识:向量相似性提供相关片段,却不能保证业务关系、方向、约束和动作条件完整呈现。
- 行动前无校验且失败后无反馈策略:计划层假设与真实系统状态没有校验,执行结果也未反馈给决策层重新规划。
使用折扣与复核要求
- 三层结构不能替代事务一致性、权限系统和真实数据质量治理。
- 大模型生成形式结构无法直接证明业务语义正确,生产使用需保留专家确认、工具校验、真实用例和审计记录。
相关 Skills
- 本 Skill 是基础入口,没有前置依赖。
审计信息
- 历史验证:v0.1.0 路由测试 6/6;v0.4.0 已通过输出契约结构校验,跨平台行为继续按版本抽样
- 首次公开版本:2026-08-21
- 来源说明:方法框架受《本体驱动的 AI 数据管理》启发;仓库不包含原书正文。
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.
- 5d ago Changed b8583006f382
- 8d ago Changed · +7 lines 957156e59c03
- 12d ago First seen · 100 lines · 84 tokens per session scan A bf429b4eecfc
fact-reason-action-business-loop 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 84 tokens to every session and 1,460 once invoked, about $0.0004 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 2026-08-31.
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