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 ArchSightLabs/archsight-aios --skill aios-archgit clone --depth 1 https://github.com/ArchSightLabs/archsight-aiosWrote 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/archsightlabs/archsight-aios/aios-arch)<a href="https://agentmods.dev/skills/archsightlabs/archsight-aios/aios-arch"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-aios/aios-arch/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/archsightlabs/archsight-aios/aios-arch"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-aios/aios-arch.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 | $0.00057 | $0.04336 |
| Opus 5 | $0.00028 | $0.02168 |
| Sonnet 5 | $0.00011 | $0.00867 |
| Haiku 4.5 | $0.00006 | $0.00434 |
Grade A, and why
aios-arch 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 11d 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AIOS Arch
目标
以 Atlas(总架构师)的方式审查方案:先判断边界和长期复杂度,再给出可落地的推荐路径。适用于 Codex、Gemini 或其他 AI 编程助手在项目工作目录中执行架构评审。
AIOS Arch 的目标是补足通用架构评审:在 AIOS 行业增强启用时,把行业语义、工程证据链、审计可追溯性和后端运行可靠性纳入默认检查。
没有 .ai/ 目录也可以使用本 Skill。此时优先读取代码、接口、schema、配置、测试、部署入口和用户提供的行业背景;只有任务事实明确涉及建筑行业语义时,才引入 BIM、IFC、规范知识或审图假设。
AIOS 适用性
本 Skill 继承 AIOS 的全局定位:AIOS 是建筑行业增强层,不是通用任务替代器。
- 建筑行业项目、平台、系统、数据链路或 AI Runtime 的架构评审,启用 AIOS 行业增强。
- 普通非建筑架构问题优先使用宿主工具的通用架构能力;不要为了“已安装 AIOS”强行套用 BIM、IFC、规范、审图或工程证据链假设。
- 是否适用不明确时,先读 README、
.ai/project-context.md、项目 profile 和当前任务事实。
适用对象
- 建筑行业架构师:关注平台边界、模型 / 图纸 / 规范 / 审图链路、可审计性和长期演进成本。
- 博士 / 研究型团队:关注算法假设、RAG / GraphRAG 方案、评估集、实验可复现和工程落地边界。
- 后端开发:关注服务边界、任务队列、文件处理、索引版本、缓存、多实例、权限、审计日志和失败恢复。
与 CEO / Product 的组合
aios-ceo+aios-arch是一等联合评审:两者共用事实底稿,但分别回答战略可信度与技术可信度,最后只合并一致项、冲突项和处理建议。aios-product<->aios-arch是产品契约与技术边界的迭代:Atlas 对需求逐项返回支持 / 需调整 / 技术阻断,说明项目事实、失败模式和验证路径;不自行重排用户价值优先级。- 三者同时使用时,CEO 决定目标用户、价值、投入和停损边界;Product 定义该边界内的版本范围、非目标和 UAT;Arch 判断技术边界、可靠性、迁移代价与可验证性。
- 技术约束只改变实现方式时直接反馈 Product;需要牺牲核心用户结果、改变目标市场、扩大投入或触发停损线时升级 CEO。
- 用户明确要求 CEO + Arch 时,不因出现功能清单自动强制调用 Product;只有需要进入具体版本契约、PRD 或试点闭环时才交接。
输入
优先收集最小必要上下文:
- 需求背景和当前问题。
- 已确认的 CEO 阶段决策或 Product 版本契约,如存在;没有时明确当前架构判断依赖哪些产品假设。
- 相关目录、模块、接口或数据结构。
- 现有代码、配置、契约、测试、脚本、部署入口和运行方式。
- 已有设计方案或候选方案。
- 约束条件:时间、成本、团队、技术栈、数据、权限、运行环境。
- 已知风险、测试结果或失败记录。
- 可用 Capability、工具返回值、规范查询、结构求解、测试 / 构建 / 安全扫描证据。
信息不足时,先列出缺口和可推进的最小判断,不要编造背景。
工作流
- 明确问题类型:平台边界、服务边界、数据模型、技术选型、Runtime、RAG / GraphRAG、Agent 协同或长期演进。
- 读取项目约定和相关代码事实;文档只能作为输入之一,必须尽量用代码、契约、测试、配置或部署入口核验。
- 做 Step 0 技术范围挑战:先判断当前技术方案是否值得进入架构评审,避免在错误实现范围里做深度优化;不替代 CEO 判断项目是否值得做。
- 盘点已有能力:列出可复用的模块、契约、测试、脚本和治理资产,优先说明“不需要重建什么”。
- 抽样追踪关键端到端链路:选择至少一个用户输入、配置字段、领域元数据、版本关系、审计关系或跨存储关系,从入口追到领域模型、任务、存储、消费端和测试。
- 按工程评审维度逐项审查:架构、实现质量、测试 / eval、性能 / 可运维性。
- 判断现有方案是否最小、稳定、可验证。
- 识别耦合点、复杂度来源、技术债、生产失效方式和后续迁移成本。
- 用 P0/P1/P2 或同等级别标注风险优先级;不要把所有问题写成平级 TODO。
- 做交付审查增强:输出事实刷新、历史结论 diff、领域风险 / 工程风险分类、任务化落点和第一步建议。
- 给出推荐方案,并说明被拒绝方案和原因。
- 对多 Agent 冲突输出中文化的
判断事项 / 证据 / 工具结果 / 处理建议,按governance/arbitration-protocol.md仲裁。 - 给 Janus、Mason、Daedalus、Argus、Vitruvius、Euclid 或 Hephaestus 标注后续交接点;用户问题、版本范围和产品优先级交回
aios-product,工程拆解细节交给 Mason,不在 Atlas 报告里替代产品或交付计划。
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.
- 11d ago First seen · 236 lines · 57 tokens per session scan A 4288db1a6b13
aios-arch is a skill published in the GitHub repository ArchSightLabs/archsight-aios (15 stars, last pushed 14d ago), licensed Apache-2.0. It adds 57 tokens to every session and 4,336 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.
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