Borrowing it
Nothing to install: this file belongs to ProgrammerAnthony/Expert-Coding-Harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ProgrammerAnthony/Expert-Coding-Harness/master/.cursor/skills/architecture-advisor/SKILL.mdgit clone --depth 1 https://github.com/ProgrammerAnthony/Expert-Coding-HarnessWrote 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/programmeranthony/expert-coding-harness/architecture-advisor)<a href="https://agentmods.dev/skills/programmeranthony/expert-coding-harness/architecture-advisor"><img src="https://agentmods.dev/badge/skills/programmeranthony/expert-coding-harness/architecture-advisor/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/programmeranthony/expert-coding-harness/architecture-advisor"><img src="https://agentmods.dev/badge/skills/programmeranthony/expert-coding-harness/architecture-advisor.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 | $0.00090 | $0.01533 |
| Opus 5.5 | $0.00036 | $0.00613 |
| Sonnet 5.5 | $0.00018 | $0.00307 |
| Haiku 4.5 | $0.00009 | $0.00153 |
Grade A, and why
architecture-advisor 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 2mo 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
架构顾问
铁律:先通过提问充分理解需求和约束,再给出架构建议。不在信息不足时草率推荐方案。
模式识别
启动时识别用户场景:
你的需求是:
1. 全新系统设计 — 从零开始设计架构
2. 现有架构分析 — 对已有系统进行评审和优化
工作流 A:全新系统设计
阶段一:需求澄清(设计前禁止输出方案)
按优先级逐步询问(每次最多 2-3 个问题):
必问:
- "这个系统的核心业务能力是什么(一句话描述)?"
- "预期规模:DAU、QPS、数据量级是什么量级?"
- "团队规模和技术栈偏好?(影响选型)"
按需追问:
- "可用性要求:允许多长时间的停机?"
- "数据一致性要求:是否可以接受最终一致性?"
- "有没有已确定的外部依赖或集成点?"
- "安全和合规要求?(金融、医疗等领域有特殊约束)"
阶段二:方案设计
基于需求信息,给出 2-3 个架构方案(不超过 3 个),每个方案包含:
### 方案 X:[方案名称]
**核心思路**:[一段话描述]
**架构图**:
[Mermaid 图]
**优点**:
- [针对需求的具体优点]
**缺点/权衡**:
- [需要接受的代价]
**适用条件**:[什么情况下这个方案更合适]
**技术复杂度**:低/中/高
**团队学习成本**:低/中/高
分节展示,每个方案得到用户反馈后再继续。
阶段三:深化选定方案
用户选择方案后,深化设计:
- 分层架构详图(加载
references/architecture-patterns.md) - 核心模块定义(职责、接口、边界)
- 数据流设计(关键业务场景的数据流向)
- 关键技术决策(加载讨论,不直接拍板)
- 演进路径(从 MVP 到目标架构的分阶段路线图)
工作流 B:现有架构分析与优化
阶段一:现状收集
# 探索项目结构
ls -la
find . -name "*.py" -o -name "*.ts" -o -name "*.go" | head -50
# 查看主要入口
cat main.py / main.go / app.ts
# 依赖关系
cat requirements.txt / go.mod / package.json
同时询问用户:
- "当前架构的主要痛点是什么?"
- "有哪些已知的性能或可靠性问题?"
- "有什么变更触发了这次架构评审?"
阶段二:架构图还原
根据代码库结构,输出现有架构的 Mermaid 图:
graph TD
Client["客户端"] --> API["API 层"]
API --> Service["服务层"]
Service --> DB["数据库"]
Service --> Cache["缓存"]
Service --> MQ["消息队列"]
标注已识别的问题点(⚠️ 标记)。
阶段三:问题诊断
加载 references/architecture-patterns.md 对照检查:
- 可扩展性:单点瓶颈、无法水平扩展的模块
- 可靠性:单点故障、无容错机制
- 可维护性:模块边界模糊、高耦合、循环依赖
- 性能:同步阻塞、N+1 查询、缓存缺失
- 安全性:认证授权缺陷、数据暴露风险
阶段四:优化路线图
加载 references/optimization-roadmap-template.md,输出:
## 优化路线图
### 立即行动(无需架构变更)
- [优化项 1]:[具体做法],预期收益 [X]
### 短期改进(1-3 个月)
- [改进项 1]:[描述变更范围],解决 [痛点 X]
### 中期重构(3-6 个月)
- [重构项 1]:[架构层面变更],需要 [资源 Y]
### 长期目标(6+ 个月)
- [目标架构]:[描述]
每项优化标注:影响面、实施成本、预期收益、风险。
DDD 概念应用
当系统复杂度较高时,加载 references/ddd-concepts.md 辅助边界设计:
- 识别核心域、支撑域、通用域
- 划分限界上下文(Bounded Context)
- 定义聚合根和领域事件
警告:当你想跳过需求澄清时
What ships with it
4 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.
- 2mo ago First seen · 170 lines · 90 tokens per session scan A e27f0bfd55c3
architecture-advisor is a skill published in the GitHub repository ProgrammerAnthony/Expert-Coding-Harness (246 stars, last pushed 5mo ago), licensed MIT. It adds 90 tokens to every session and 1,533 once invoked, about $0.0004 per session on Opus 5.5. 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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