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 action-contract-execution-feedback-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/action-contract-execution-feedback-loop)<a href="https://agentmods.dev/skills/superchason/ontology-driven-ai-data-management-skills/action-contract-execution-feedback-loop"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/action-contract-execution-feedback-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/action-contract-execution-feedback-loop"><img src="https://agentmods.dev/badge/skills/superchason/ontology-driven-ai-data-management-skills/action-contract-execution-feedback-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 | $0.00092 | $0.01475 |
| Opus 5 | $0.00046 | $0.00737 |
| Sonnet 5 | $0.00018 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00147 |
Grade A, and why
action-contract-execution-feedback-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 3d 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.
Action 契约与“校验—执行—反馈”闭环
方法骨架
- 把业务动作定义成可校验、可调用、可追踪的Action契约。
- 契约包含目标系统、功能、输入输出、前置条件、效果、权限、幂等和审计要求。
- 执行前同时验证合规性、业务可行性和技术安全性。
- 执行结果按成功、可恢复失败和不可恢复失败分类。
- 反馈驱动重试、补偿、降级、人工接管或终止,并更新事实状态。
- 输出是一份可交给系统集成、测试和治理共同使用的动作规格。
触发场景
用户会在什么情境下需要这个 Skill
- 需要把一个业务动作封装成Agent可调用工具
- 要设计动作执行前校验、重试和降级
- 系统调用已有API但缺少业务契约和反馈闭环
语言信号
- “帮我定义Action契约”
- “这个动作失败后怎么办”
- “怎么做执行前校验和结果回写”
- 英文信号:action contract, pre-check, retry and fallback
与相邻 Skill 的区分
- 与
risk-based-agent-action-modes:行动模式先决定控制方式;本 skill 定义选定模式下的具体Action契约。 - 与
fact-reason-action-business-loop:业务闭环确定有哪些行动;本 skill 把单个行动细化为可治理调用。
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:
-
定义动作身份
- 动作:写清动作名称、业务目的、目标系统、责任主体、版本和幂等键。
- 完成标准:动作边界唯一且能够审计。
-
定义输入输出
- 动作:为参数、类型、来源、必填、敏感级别、输出和错误码建立契约。
- 完成标准:输入可校验,输出能驱动后续事实更新。
-
定义前置与效果
- 动作:列出状态、权限、额度、时间、依赖和预期状态变化。
- 完成标准:每个前置条件可判断,每个效果可验证。
-
配置执行控制
- 动作:设置合规、可行和安全校验,以及事务、超时、重试、补偿、降级、终止和人工接管。
- 完成标准:成功与各类失败均有唯一处置路径。
-
闭合反馈与测试
- 动作:定义结果回写、审计字段、告警和测试用例。
- 完成标准:正常、重复、边界、越权、超时和部分失败用例通过。
固定输出
- Action 登记卡:动作编号、名称、业务目的、目标系统与功能、责任主体、版本和状态
- 输入参数定义表:参数、类型、来源、必填、敏感级别、校验和默认值
- 输出与错误定义表:输出、错误码、业务含义、可恢复性和后续状态
- 前置条件、权限与执行效果矩阵
- 执行控制矩阵:幂等、事务、超时、重试、补偿、降级、终止和人工接管
- 结果回写与审计表:成功、可恢复失败、不可恢复失败的反馈、告警和留痕
- Action 测试矩阵:正常、重复、边界、越权、超时、部分失败和恢复用例
每个 Action 使用唯一编号串联参数、权限、规则、测试和审计结果;未确认的接口、权限或事务条件标为受阻。
使用边界
不要在以下情况使用
- 仍未决定动作应自动、人工还是协同控制
- 目标系统没有稳定接口或事务保障
- 只需要生成只读说明,无实际调用
常见失败模式
- 约束和权限落入过紧或过松两端:场景风险与控制策略未建立映射,Agent无法判断何时自主、何时申请授权、何时停止。
- 目标模糊且行动原语契约残缺:Agent缺少可计算的成功条件及行动前后状态模型,只能猜测参数、条件和执行结果。
- 高风险行动采用规则直驱:决策正确性与执行授权被合并,缺少人工裁决和后果控制,单点错误直接转为现实损失。
- 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
- 契约完整不代表目标系统可靠,生产执行仍需接口监控、事务与安全基础设施。
- 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
depends-on→risk-based-agent-action-modes;行动模式先决定控制方式;本 skill 定义选定模式下的具体Action契约。composes-with→fact-reason-action-business-loop;业务闭环确定有哪些行动;本 skill 把单个行动细化为可治理调用。
审计信息
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.
- 3d ago Changed · +10 tokens per session 9ff6695d4d71
- 7d ago Changed · +7 lines 162d3d6693d0
- 11d ago First seen · 100 lines · 82 tokens per session scan A 3a0edad330c9
action-contract-execution-feedback-loop is a skill published in the GitHub repository SuperChason/ontology-driven-ai-data-management-skills (10 stars, last pushed 4d ago), licensed MIT. It adds 92 tokens to every session and 1,475 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 2026-08-31.
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