<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Automation on Commentary of Takao</title><link>https://takao.blog/zh/tags/automation/</link><description>Recent content in Automation on Commentary of Takao</description><generator>Hugo -- gohugo.io</generator><language>zh</language><copyright>Commentary of Takao</copyright><lastBuildDate>Wed, 15 Jul 2026 22:01:08 +0900</lastBuildDate><atom:link href="https://takao.blog/zh/tags/automation/index.xml" rel="self" type="application/rss+xml"/><item><title>2026 年 OWASP ZAP：高级扫描和 CI/CD 集成</title><link>https://takao.blog/zh/web/owasp-zap-advanced-2026/</link><pubDate>Tue, 09 Jun 2026 00:00:00 +0900</pubDate><guid>https://takao.blog/zh/web/owasp-zap-advanced-2026/</guid><description>&lt;img src="https://takao.blog/img/thumbnail/owasp-zap-advanced-2026-zh.png" alt="Featured image of post 2026 年 OWASP ZAP：高级扫描和 CI/CD 集成" /&gt;&lt;h2 id="超越基本扫描"&gt;超越基本扫描
&lt;/h2&gt;&lt;p&gt;OWASP ZAP 自早期以来已经发生了显着的发展。到 2026 年，它不再只是一个点击式代理扫描器，而是一个功能齐全的安全自动化平台，具有强大的 API、可编写脚本的自动化框架和深度 CI/CD 集成。如果您首先需要基础知识，请阅读我们的 &lt;a class="link" href="https://takao.blog/web/how-to-owasp-zap/" &gt;OWASP ZAP 安装和设置指南&lt;/a&gt;。本文介绍了大规模运行安全测试的团队的高级工作流程。&lt;/p&gt;</description></item><item><title>用于自动化的 Git Hooks：超越代码质量</title><link>https://takao.blog/zh/web/git-hooks-automation/</link><pubDate>Tue, 24 Dec 2024 00:00:00 +0900</pubDate><guid>https://takao.blog/zh/web/git-hooks-automation/</guid><description>&lt;img src="https://takao.blog/img/thumbnail/git-hooks-automation-zh.png" alt="Featured image of post 用于自动化的 Git Hooks：超越代码质量" /&gt;&lt;h2 id="介绍"&gt;介绍
&lt;/h2&gt;&lt;p&gt;Git 挂钩是在 Git 生命周期中的特定点自动运行的可执行脚本。虽然许多开发人员仅将它们与代码格式化和 linting 联系起来，但它们的潜力远远超出了工作流程自动化、项目管理和团队协作。本文探讨了如何利用 Git hook 来实现全面的自动化策略。&lt;/p&gt;</description></item><item><title>AI内容生成</title><link>https://takao.blog/zh/web/ai-content-generation/</link><pubDate>Tue, 29 Oct 2024 00:00:00 +0900</pubDate><guid>https://takao.blog/zh/web/ai-content-generation/</guid><description>&lt;img src="https://takao.blog/img/thumbnail/ai-content-generation-zh.png" alt="Featured image of post AI内容生成" /&gt;&lt;p&gt;AI content generation has moved from experimentation to production. Developers are no longer asking whether AI can generate content but how to integrate it reliably, at scale, and with quality control. This article provides a practical guide for building content systems with AI, focusing on technical architecture, quality assurance, and ethical deployment rather than prompt engineering tips.&lt;/p&gt;
&lt;h2 id="llm-powered-content-pipelines"&gt;LLM-Powered Content Pipelines
&lt;/h2&gt;&lt;p&gt;A well-architected AI content pipeline consists of several stages. It starts with content specification input including structured metadata, topic briefs, and tone guidelines. A prompt construction layer uses a template system with variable injection, guardrails, and few-shot examples. The LLM API dispatch routes requests to providers such as OpenAI, Anthropic, or open-source models via vLLM or Ollama. Post-processing handles format validation, content extraction, and cleanup before the result enters a human review queue.&lt;/p&gt;</description></item></channel></rss>