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 BuildrAI/Buildr --skill agent-first-designgit clone --depth 1 https://github.com/BuildrAI/BuildrWrote 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/buildrai/buildr/agent-first-design)<a href="https://agentmods.dev/skills/buildrai/buildr/agent-first-design"><img src="https://agentmods.dev/badge/skills/buildrai/buildr/agent-first-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/buildrai/buildr/agent-first-design"><img src="https://agentmods.dev/badge/skills/buildrai/buildr/agent-first-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.01558 |
| Opus 5 | $0.00030 | $0.00779 |
| Sonnet 5 | $0.00012 | $0.00312 |
| Haiku 4.5 | $0.00006 | $0.00156 |
Grade A, and why
agent-first-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 6d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
智能体优先设计
遵循当前工作空间(Workspace)的根规则:AGENTS.md 保存原则与边界,本技能(Skill)提供应用方法,不复制另一套原则,也不增设评分或批准门禁。
用于软件引入智能体(Agent)参与产品结果交付的设计,以及智能体工作系统的职责与流程改造。只用智能体写代码,不自动改变产品架构;已有软件渐进引入时,指导相关部分,保留其他功能的既有设计、业务规则、安全和授权边界。普通开发、验证或收尾不增加必读前置。
完整范式
智能体(Agent)对目标负责,技能(Skill)提供方法和边界,接口(Interface)提供可组合能力,应用(Application)维护事实并保障具体动作的安全。
智能体(Agent)依据现场选择、组合和调整工具,持续推进目标;软件不预先规定所有场景必须经过同一条流程。
技能(Skill)提供的方法本身可以是一种沉淀的工作流(Workflow):把目标、判断方法、常见顺序和安全边界保留下来,让智能体按现场调整。能够由现有工具和这类方法可靠完成的工作,不必先写成应用中的固定流程。
接口(Interface)提供被设计系统的可组合能力,不等于每个动作都要新增接口。原生 Git、系统工具和外部工具已经提供的能力可以直接使用;只有确需统一维护事实或保护具体副作用时,才增加最小的应用能力。Buildr 收尾是一个实践案例,不是所有产品的架构模板。
组合方式开放、事实可以重新观察、动作可以独立恢复、局部失败不扩散。
flowchart TB
A["智能体(Agent)<br/>对目标与结果负责"]
S["技能(Skill)<br/>方法、判断与安全边界"]
T["工具(Tools)<br/>Git、系统及外部工具"]
I["系统接口(Interface)<br/>已有可组合能力"]
P["应用(Application)<br/>必要事实与具体动作安全"]
F["产物(Artifact)<br/>身份、当前版本与变更"]
H["人使用的界面"]
A --> S
A --> T
A --> I
I --> P
P <--> F
T <--> F
H <--> P
图表达职责和使用关系,不要求每个功能建设全部模块。各入口对齐成果及当前事实;已有工具足够时,可以没有新的接口或应用,也无需把外部成果复制到统一数据库。
围绕产物接续工作
先找出本次产物(Artifact),包括中间成果与最终结果:例如 PPT、代码、Git 变更、数据记录和外部系统中已生效的结果。临时成果不因被生成或使用就自动成为长期受管工作资产(Work Asset)。
按实际场景检查:
- 共同对象在哪里? 各入口如何识别同一成果,谁维护权威内容和当前版本?外部操作以实际系统的可核验结果为准,不用聊天声明或本地副本代替。
- 人修改后怎样继续? 设计变化发现与重读当前版本的方式;智能体继续相关判断或修改前重新核对,写入时由能力边界校验版本并显式处理冲突。不固定通知技术,也不要求每次变化立即唤醒智能体。
- 智能体修改后人看到什么? 人应能从界面查看同一成果及必要变化,无需阅读完整执行过程。缓存或副本尚未更新时,明确陈旧状态,避免把旧内容当作当前事实。
- 怎样证明目标完成? 各入口版本一致只说明协作基础一致;质量、验证、交付及必要外部结果仍按用户目标验收。
按真实问题判断
先确认目标的开始、结束和用户得到的结果,查看当前实现、文档与实际案例。不要先列接口、状态或阶段清单。
围绕真正有争议的部分判断:
- 智能体决定什么? 需要理解意图、选择工具、调整顺序或处理新场景的部分,先保留给智能体(Agent)。不让软件再次充当通用判断者。
- 方法沉淀在哪里? 已有重复实践但需要按现场调整的组合,优先写成技能(Skill)。确实稳定、重复且必须统一控制副作用的组合,才考虑程序化。
- 已有工具够不够? 先验证被设计系统的现有接口、系统及外部工具。新增接口(Interface)前,说清它解决的具体缺口和为何不能由已有能力承担;没有缺口就不新增。
- 哪些事实需要软件保存? 找到真正的权威来源。能从 Git、文件或外部系统重新观察的事实,不重复保存成多个必须互相吻合的状态;必须保存的业务事实由唯一应用(Application)写入,并处理版本冲突。
- 检查保护什么? 指出具体对象、不变量和放行伤害。授权、对象错误、覆盖工作、丢失内容或完成误报应阻止相关动作;流程偏好、内部登记缺口和自动化信心不足不能自动阻塞整个目标。
- 失败怎样继续? 保留部分成功,重新观察当前事实,只恢复相关动作。不能证明安全删除时保留资源;不能证明完成时如实报告,不能用一条备注掩盖未完成目标。
What ships with it
1 file 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.
- 6d ago First seen · 77 lines · 60 tokens per session scan A e139eddd5891
agent-first-design is a skill published in the GitHub repository BuildrAI/Buildr (24 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,558 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 2026-09-04.
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