action-contract-execution-feedback-loop

action-contract-execution-feedback-loop is a skill for Codex from SuperChason/ontology-driven-ai-data-management-skills. It costs 92 tokens per session (1,475 once invoked), scanned A, original, MIT.

A specification method for making a business action into a tool an AI agent can call safely. It defines the action’s inputs, outputs, permissions, preconditions, expected effects, error handling, and audit information.

In plain words
What is it for?
Use it to define and test API-backed actions, including validation, timeouts, retries, fallbacks, duplicate-request handling, partial failures, result updates, and audit records.
Why use it?
It prevents vague system calls by making success, failure, retries, compensation, and human takeover explicit before execution.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to define and test API-backed actions, including validation, timeouts, retries, fallbacks, duplicate-request handling, partial failures, result updates, and audit records.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/superchason/ontology-driven-ai-data-management-skills/action-contract-execution-feedback-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 action-contract-execution-feedback-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 action-contract-execution-feedback-loop

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

agentmods 80×15 button for action-contract-execution-feedback-loop

Your own site · 80×15
<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>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,475 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 $0.00092 $0.01475
Opus 5 $0.00046 $0.00737
Sonnet 5 $0.00018 $0.00295
Haiku 4.5 $0.00009 $0.00147

Measured 3d ago against content hash 9ff6695d4d71, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

skills/action-contract-execution-feedback-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.

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

  1. 需要把一个业务动作封装成Agent可调用工具
  2. 要设计动作执行前校验、重试和降级
  3. 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

  • 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。
  • 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:

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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-onrisk-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-withfact-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

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. 3d ago Changed · +10 tokens per session 9ff6695d4d71
  2. 7d ago Changed · +7 lines 162d3d6693d0
  3. 11d ago First seen · 100 lines · 82 tokens per session scan A 3a0edad330c9

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

graph-mutation-plan

Cookbook for composing an applygraphmutations plan — stable entitykey patterns, the canonical label/edge vocabulary, evidence/invalidation/confidence discipline, and a worked example. Load this when building a non-trivial mutation plan.

potpie-ai/potpie · 51 tokens

open-ontologies

AI-native ontology engineering using 50+ MCP tools backed by an in-memory Oxigraph triple store. Build, validate, query, and govern RDF/OWL ontologies with a generate-validate-iterate loop. Use when building ontologies, knowledge graphs, RDF data, SPARQL queries, BORO/4D modeling, SHACL validation, clinical…

fabio-rovai/open-ontologies · 110 tokens

report-generation

A workflow for generating data-analysis reports as interactive HTML with charts. It is intended for trend, statistics, monthly, weekly, and other reports, using database queries and ECharts, a web charting library.

zj-unicom-ai/UniEmployee · 95 tokens

mykg

Run mykg knowledge-graph commands inside Claude Code from one slash command /mykg. The user describes intent in natural language (extract, append, sync, resume, approve, walkthrough, parse-docs, fetch-web, query); the skill parses intent, builds the right mykg CLI command from the live --help output, confirms, runs…

SenolIsci/mykg · 175 tokens

business-overview

A business performance analysis workflow that uses sales, finance, inventory, and customer data to describe how a company is operating.

zj-unicom-ai/UniEmployee · 35 tokens

enterprise-sales

A Chinese-language workflow for preparing sales visits and solution documents for government and enterprise customers. It covers checking customer records, understanding the industry, finding suitable products, and generating a Word proposal.

zj-unicom-ai/UniEmployee · 34 tokens