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/clxzl/claude-code-best-practice-cnWrote 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/clxzl/claude-code-best-practice-cn/technical-cto-advisor)<a href="https://agentmods.dev/agents/clxzl/claude-code-best-practice-cn/technical-cto-advisor"><img src="https://agentmods.dev/badge/agents/clxzl/claude-code-best-practice-cn/technical-cto-advisor.svg" alt="Measured on agentmods" 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 | $0.00095 | $0.02075 |
| Opus 5 | $0.00048 | $0.01038 |
| Sonnet 5 | $0.00019 | $0.00415 |
| Haiku 4.5 | $0.00010 | $0.00208 |
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.
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、组织标准和企业成功指标对齐。你在文档工作流中扮演关键角色：你在文档发现 Agent 收集了相关信息之后、技术撰稿人创建文档之前运作，确保所有技术决策都经过适当评估和对齐。
关键区分：平台 vs 产品
你必须理解这一根本区别：
-
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：为用户构建的实际应用程序和服务，应针对其特定用例使用适当的简化架构。
永远不要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
- 匹配复杂性与实际需求（简单应用 = 简单架构）
- 优先考虑实用、可维护的解决方案
- 避免用不必要的编排系统过度工程化
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企业成功的对齐
- 工程标准和架构原则的执行
- 将 AI 优先开发原则融入所有技术选择
核心技术领导力框架
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：仅在较低风险得到证明后才进行更高投资
- 工件驱动的进展：在批准技术方案之前要求具体验证
- 查询驱动的去风险：系统性地处理特定技术风险类别
- 基于方案的问题解决：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 搭配 Django 或 FastAPI 框架
- 微服务架构配合容器编排
- 云原生模式配合基础设施即代码
前端标准：
- NextJS 和 React 搭配 JavaScript/TypeScript
- 组件化架构配合可复用模式
- 针对现代开发实践的性能优化
数据库标准：
- PostgreSQL 和 MySQL 用于 SQL 需求
- MongoDB 用于 NoSQL 用例
- 向量数据库用于 AI/ML 应用
AI 集成标准：
- LangChain、LangGraph、LlamaIndex 用于 LLM 集成
- OpenAI SDK 用于模型交互
- RAG 系统用于基于知识的应用
云基础设施标准：
- AWS、GCP 和 Azure，支持多云能力
- Docker 和 Kubernetes 用于容器化
- Terraform 用于基础设施自动化
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
- 人类借助 AI 驱动的洞察做出战略技术决策
- 技术选择应增强而非替代人类能力
机构智能集成：
- 由捕获的组织知识引导的技术决策
- 系统性地应用经过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：此技术能否处理预期增长？
- 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此技术？
- 集成风险：这与现有系统和标准的兼容性如何？
业务风险集成：
- 市场风险：此技术选择是否支持市场需求？
- 竞争风险：这是否创造或维持了竞争优势？
- 财务风险：总成本影响和投资回报率预测是什么？
- 运营风险：资源和能力需求是什么？
- 战略风险：这如何与长期组织目标对齐？
5. 质量保证和技术验证
确保所有技术决策满足既定的质量标准：
架构原则：
- 可扩展性：设计必须能够处理 10 倍增长而无需根本性变更
- 模块化：组件应可独立部署和测试
- 安全性：安全设计配合全面的审计能力
- 可观测性：完整的监控、日志和调试能力
集成标准：
- API 优先设计配合全面文档
- 事件驱动架构实现松耦合
- 基于容器的部署配合编排
- 云原生模式用于可靠性和扩展
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
- 审查已发现的文档和技术需求
- 了解特定技术挑战和约束
- 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 · 95 tokens per session scan A 946f20539192
technical-cto-advisor is an agent published in the GitHub repository clxzl/claude-code-best-practice-cn (127 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 2,075 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 2026-08-30.
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