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 AgenticAIPlan/AgenticAISkills --skill agent-boundary-designgit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/agenticaiplan/agenticaiskills/agent-boundary-design)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/agent-boundary-design"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/agent-boundary-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/agenticaiplan/agenticaiskills/agent-boundary-design"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/agent-boundary-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 | $0.00063 | $0.01488 |
| Opus 5 | $0.00032 | $0.00744 |
| Sonnet 5 | $0.00013 | $0.00298 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
agent-boundary-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 12d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
智能体开发边界规范
适用场景
当用户需要为某个业务智能体明确以下内容时,使用本 Skill:
- 智能体应该自主完成什么
- 哪些动作必须人工确认
- 哪些高风险动作必须保留给人工
- 单 Agent 是否应该拆成多个 Agent
- 当前智能体是否因为“做太多”或“做太少”而影响用户信任
典型业务场景包括:
- 质量检测或质量拦截智能体
- 工单分派、升级和闭环跟踪智能体
- 审批辅助、风控预审或合规校验智能体
- 客服初判、销售线索分级、售后诊断等需要人机协同的业务流程智能体
输入要求
- 智能体服务的业务目标或任务描述
- 智能体面向的用户角色
- 智能体可能执行的关键动作列表
- 高风险动作、合规约束或错误代价
- 是否存在人工审核、人工接管或多 Agent 协作场景
- 是否已有业务 SOP、规则阈值、审批链路、系统接口或审计要求
执行步骤
- 先写清楚该智能体的单一职责,使用一句 job story 表达:当什么情况发生时,智能体需要完成什么动作,以帮助用户达成什么结果。
- 判断该智能体更接近建议型还是执行型:
- 建议型 Agent:输出判断、建议、草稿或分级结果,不直接调用业务系统产生后果。
- 执行型 Agent:会调用系统、修改状态、发出通知、触发审批、放行、拦截、派单或升级等动作。
- 混合型 Agent:先给出建议或预览,经过人工确认后才执行动作。
- 列出关键高风险动作(如有,通常 1-3 个),并逐项判断:
- 是否可逆
- 一旦出错代价有多高
- 当前模型或系统对该动作是否具备足够可靠性
- 按风险和可信度将动作划分为三类:
- Zone A:可自主执行,执行后通知用户即可
- Zone B:先生成结果或执行预览,再由用户确认
- Zone C:必须拒绝执行并转交人工
- 明确 handoff 规则:
- 什么条件下转人工
- 转交时要带上哪些上下文
- 人工处理后如何恢复到 Agent 流程
- 对照业务流程检查系统边界:
- 如果是建议型 Agent,明确它不能直接调用哪些系统或执行哪些动作。
- 如果是执行型或混合型 Agent,明确它会调用哪些业务系统,以及是否会触发放行、拦截、审批、告警、派单、升级等后果明确的动作。
- 对所有会产生业务后果的动作,说明是否需要记录操作员、时间戳、阈值依据和审计链路。
- 判断是否需要拆分多 Agent。只有在职责、信任等级、上下文或能力边界明显不同的情况下才拆分,否则优先保持单 Agent。
- 最后输出结构化的边界设计文档,并标注风险、假设和上线前检查项。
输出要求
输出必须使用结构化 Markdown,至少包含以下内容:
## Agent Boundary Spec
### 1. 智能体单一职责
- Job Story:
- 类型:建议型 / 执行型 / 混合型
### 2. Zone A:自主执行
- 动作:
- 触发条件:
- 调用系统(如适用):
### 3. Zone B:人工确认
| 动作 | 触发条件 | 确认方式 |
|------|----------|----------|
### 4. Zone C:拒绝执行并转人工
- 场景:
- 原因:
### 5. Handoff 规则
- 转人工条件:
- 转交内容:
- 恢复方式:
- 幂等保护:
- 审计要求:
### 6. 是否需要拆分多 Agent
- 结论:
- 理由:
### 7. 风险与待确认项
- 风险:
- 假设:
- 上线前检查项:
业务示例
示例:发动机 100 阶啸叫质量拦截智能体
- Job Story:当发动机在变速测试台架上完成扫频测试,智能体基于同步采集的转速信号与音频信号,实时追踪 100 阶啸叫声纹并判断是否超标,从而提升出厂拦截率并减少人工负担。
- Zone A:同步采集音频与转速信号,执行阶次跟踪,判断全转速范围内 100 阶幅值是否超标;当置信度达标且结论明确时,自动写入日志并给出放行或拦截结果。
- Zone B:当阈值附近震荡、同步误差超标、扫频范围不完整、连续多台异常或新型号首次测试时,展示幅值-转速曲线、置信度、超标区间,由质检员确认拦截、放行或重测。
- Zone C:当传感器离线、同步失锁、涉及整批次召回、结果与人工分析冲突,或超出“100 阶啸叫”定义范围时,必须拒绝执行并转交人工。
- Handoff:转人工时附带曲线图、原始音频和转速数据、触发原因、超标转速区间;人工确认后恢复结果记录步骤,并写入操作员 ID、时间戳与覆盖原因。
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.
- 12d ago First seen · 116 lines · 63 tokens per session scan A c729faf3e86c
agent-boundary-design is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 63 tokens to every session and 1,488 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…