fact-reason-action-business-loop

fact-reason-action-business-loop is a skill for Codex from SuperChason/ontology-driven-ai-data-management-skills. It costs 84 tokens per session (1,460 once invoked), scanned A, original, MIT.

A method for connecting observed facts, business rules, and permitted actions into one traceable loop. It records what happened, explains why a conclusion follows, defines allowed actions, and captures the results.

In plain words
What is it for?
Use it to model business processes, document evidence and rules, propose or authorize system actions, and define how outcomes update later decisions.
Why use it?
It helps turn vague business descriptions into decisions that can be checked and acted on. It also makes missing evidence, permissions, unresolved questions, and feedback visible.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to model business processes, document evidence and rules, propose or authorize system actions, and define how outcomes update later decisions.

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Install with agentmods
npx agentmods add skills/superchason/ontology-driven-ai-data-management-skills/fact-reason-action-business-loop
Install

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.

Any agent
npx skills add SuperChason/ontology-driven-ai-data-management-skills --skill fact-reason-action-business-loop
Clone the repo
git clone --depth 1 https://github.com/SuperChason/ontology-driven-ai-data-management-skills

Made for: Codex.

Wrote 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.

agentmods badge for fact-reason-action-business-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/fact-reason-action-business-loop/github.svg)](https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/fact-reason-action-business-loop)
Your own site
<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.

agentmods 80×15 button for fact-reason-action-business-loop

Your own site · 80×15
<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>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,460 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash b8583006f382, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/fact-reason-action-business-loop/SKILL.md · 107 lines

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

  1. 需要把模糊业务描述转成Agent可用模型
  2. 需要解释一个判断怎样落到系统动作
  3. 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

  • 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。
  • 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:

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。

Read the full file on GitHub · 107 lines

Files

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.

Changes

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.

  1. 5d ago Changed b8583006f382
  2. 8d ago Changed · +7 lines 957156e59c03
  3. 12d ago First seen · 100 lines · 84 tokens per session scan A bf429b4eecfc

Subscribe to this mod's changes

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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