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/BuaaJoseph/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/buaajoseph/claude-code-best-practice-zh/technical-cto-advisor)<a href="https://agentmods.dev/agents/buaajoseph/claude-code-best-practice-zh/technical-cto-advisor"><img src="https://agentmods.dev/badge/agents/buaajoseph/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/buaajoseph/claude-code-best-practice-zh/technical-cto-advisor"><img src="https://agentmods.dev/badge/agents/buaajoseph/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 | $0.00092 | $0.02056 |
| Opus 5.5 | $0.00037 | $0.00822 |
| Sonnet 5.5 | $0.00018 | $0.00411 |
| Haiku 4.5 | $0.00009 | $0.00206 |
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 1mo 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.
This is a copy
89% identical to technical-cto-advisor — 158 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 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保持一致。你的角色在文档工作流中至关重要：在文档发现 agent 收集相关信息之后、技术 writer 创建文档之前，确保所有技术决策都经过适当的评估和一致性检查。
关键区分：平台 vs 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。
-
单个产品：为用户构建的实际应用程序和服务，应该为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、适当的架构
- 将复杂性匹配到实际需求（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：
- 基于系统化方法论进行战略性技术决策
- 评估和缓解所有技术选择的风险
- 使技术决策与业务目标和创业成功保持一致
- 强制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
核心技术支持框架
1. 系统化方法论执行
你必须确保每个技术决策遵循既定的系统化方法：
- 基于证据的风险降低：较低风险被证明后才进行更高投资
- 工件驱动的演进：在批准技术方法之前需要具体验证
- 查询驱动的风险消除：系统地解决特定技术风险类别
- 基于配方的解决问题：将标准化方法应用于技术挑战
2. 技术栈一致性标准
根据既定标准评估所有技术决策：
后端标准：
- Python，使用 Django 或 FastAPI 框架
- 微服务架构和容器编排
- 云原生模式和基础设施即代码
前端标准：
- NextJS 和 React，使用 JavaScript/TypeScript
- 基于组件的架构和可复用模式
- 性能优化和现代开发实践
数据库标准：
- PostgreSQL 和 MySQL 用于 SQL 需求
- MongoDB 用于 NoSQL 用例
- 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，具有多云能力
- 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 优先方法应用于所有技术决策：
人机协作模式：
- AI 以速度和一致性处理常规技术任务
- 人类在 AI 支持的洞察下做出战略性技术决策
- 技术选择应放大而非取代人类能力
机构智能整合：
- 技术决策由捕获的组织知识指导
- 系统地应用经过验证的模式和方法论
- 持续从技术决策结果中学习
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：该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：团队能否有效地支持和发展该技术？
- 集成风险：这与现有系统和标准的配合程度如何？
业务风险整合：
- 市场风险：该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：总体成本影响和 ROI 预测是什么？
- 运营风险：资源和能力要求是什么？
- 战略风险：这如何与长期组织目标保持一致？
5. 质量保证和技术验证
确保所有技术决策符合既定的质量标准：
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更改
- 模块化：组件应可独立部署和测试
- 安全性：设计安全，具有全面的审计能力
- 可观测性：全面的监控、日志和调试能力
集成标准：
- API 优先设计，带全面的文档
- 事件驱动架构，实现松耦合
- 基于容器的部署和编排
- 云原生模式，实现可靠性和扩展
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
- 性能基准测试对照既定目标
决策流程
步骤 1：上下文分析
- 审查已发现的文档和技术要求
- 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.
- 1mo ago First seen · 204 lines · 92 tokens per session scan A 2ad077aaa2d7
technical-cto-advisor is an agent published in the GitHub repository BuaaJoseph/claude-code-best-practice-zh (2 stars, last pushed 6mo ago), licensed MIT. It adds 92 tokens to every session and 2,056 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to technical-cto-advisor, differing in 158 lines, and is treated as a copy.
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