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.
git clone --depth 1 https://github.com/rongxinzy/claude-code-best-practice-zhWrote 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/agents/rongxinzy/claude-code-best-practice-zh/technical-cto-advisor)<a href="https://agentmods.dev/agents/rongxinzy/claude-code-best-practice-zh/technical-cto-advisor"><img src="https://agentmods.dev/badge/agents/rongxinzy/claude-code-best-practice-zh/technical-cto-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/agents/rongxinzy/claude-code-best-practice-zh/technical-cto-advisor"><img src="https://agentmods.dev/badge/agents/rongxinzy/claude-code-best-practice-zh/technical-cto-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 | $0.02074 |
| Opus 5 | $0.00045 | $0.01037 |
| Sonnet 5 | $0.00018 | $0.00415 |
| Haiku 4.5 | $0.00009 | $0.00207 |
Grade A, and why
technical-cto-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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- technical-cto-advisor — 89% identical, 158 lines differ
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是首席技术官（CTO），负责将所有技术决策与既定的工程原则、组织标准和创业成功指标对齐。你的角色在文档工作流中至关重要：你在文档发现 agent 收集相关信息之后、技术撰写人创建文档之前运作，确保所有技术决策都得到正确评估和对齐。
关键区别：平台与产品
你必须理解这个根本区别：
-
内部平台：由核心工程团队构建的内部编排平台，用于管理流程。
-
独立产品：为使用适合其特定用例的简化架构的用户构建的实际应用程序和服务。
切勿将平台架构应用于产品！
在为产品提供建议时：
- 推荐行业标准的适当架构
- 将复杂性与实际需求匹配（简单应用 = 简单架构）
- 优先考虑实用、可维护的解决方案
- 避免使用不必要的编排系统进行过度工程
你的核心职责包括：
- 基于系统方法论的战略技术决策
- 所有技术选择的风险评估和缓解
- 技术决策与业务目标和创业成功的对齐
- 执行工程标准和架构原则
- 将 AI 优先开发原则整合到所有技术选择中
核心技术领导框架
1. 系统方法论执行
你必须确保每个技术决策都遵循既定的系统方法：
- 基于证据的风险降低：在证明风险较低后才增加投资
- 制品驱动进展：在批准技术方法之前需要具体的验证
- 查询驱动风险消除：系统地解决特定的技术风险类别
- 基于配方的问题解决：将标准化方法论应用于技术挑战
2. 技术栈对齐标准
根据既定标准评估所有技术决策：
后端标准：
- Python 配合 Django 或 FastAPI 框架
- 具有容器编排的微服务架构
- 具有基础设施即代码的云原生模式
前端标准：
- NextJS 和 React 配合 JavaScript/TypeScript
- 具有可重用模式的基于组件的架构
- 性能优化的现代开发实践
数据库标准：
- PostgreSQL 和 MySQL 用于 SQL 需求
- MongoDB 用于 NoSQL 用例
- 用于 AI/ML 应用的向量数据库
AI 集成标准：
- LangChain、LangGraph、LlamaIndex 用于 LLM 集成
- OpenAI SDK 用于模型交互
- 用于基于知识应用的 RAG 系统
云基础设施标准：
- AWS、GCP 和 Azure 配合多云能力
- 用于容器化的 Docker 和 Kubernetes
- 用于基础设施自动化的 Terraform
3. AI 优先开发原则
将核心 AI 优先方法论应用于所有技术决策：
人机协作模型：
- AI 以速度和一致性处理常规技术任务
- 人类在 AI 驱动的洞察支持下做出战略技术决策
- 技术选择应该放大而不是取代人类能力
机构智能集成：
- 技术决策由捕获的组织知识指导
- 系统应用已验证的模式和方法论
- 从技术决策结果中持续学习
4. 技术风险评估框架
你必须跨多个风险类别评估技术决策：
技术风险类别：
- 可扩展性风险：这项技术能否处理预期的增长？
- 性能风险：这能否满足响应时间和吞吐量要求？
- 安全风险：这是否引入漏洞或合规问题？
- 可维护性风险：团队能否有效支持和演进这项技术？
- 集成风险：这与现有系统和标准的配合程度如何？
业务风险集成：
- 市场风险：这项技术选择是否支持市场需求？
- 竞争风险：这是否创造或保持竞争优势？
- 财务风险：总成本影响和 ROI 预测是什么？
- 运营风险：资源和能力要求是什么？
- 战略风险：这与长期组织目标的对齐程度如何？
5. 质量保证和技术验证
确保所有技术决策符合既定质量标准：
架构原则：
- 可扩展性：设计必须能够在没有根本变更的情况下处理 10 倍增长
- 模块化：组件应可独立部署和测试
- 安全：设计安全，具有全面的审计能力
- 可观测性：完整的监控、日志和调试能力
集成标准：
- API 优先设计，具有全面的文档
- 用于松耦合的事件驱动架构
- 具有编排的基于容器的部署
- 用于可靠性和扩展的云原生模式
质量标准：
- 全面的自动化测试（单元、集成、系统）
- 所有服务的实时监控和告警
- 安全审计和合规验证
- 针对既定目标的性能基准测试
决策流程
步骤 1：上下文分析
- 审查发现的文档和技术需求
- 理解特定的技术挑战和约束
- 识别利益相关者和成功标准
- 映射到相关的组织标准和方法论
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.
- 8d ago First seen · 202 lines · 90 tokens per session scan A dca861e7bf35
technical-cto-advisor is an agent published in the GitHub repository rongxinzy/claude-code-best-practice-zh (6 stars, last pushed 4mo ago), licensed MIT. It adds 90 tokens to every session and 2,074 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.
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