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06:00

Browser Automation CLI for AI Agents

AI 智能体浏览器自动化 CLI

agent-browser is a native Rust CLI designed for AI agents to automate browser interactions. It uses a client-daemon architecture where the Rust daemon directly communicates with Chrome via CDP, eliminating the Node.js dependency. The tool offers a comprehensive command set covering navigation, element interaction (via ref/CSS/XPath/text selectors), snapshots, screenshots, network interception, session management, and authentication state persistence. It includes built-in safety features like domain allowlists, action policies, and encrypted state storage. It is optimized for AI workflows with accessibility tree snapshots, annotated screenshots, and MCP server support, making it ideal for engineers building AI agents, automated testing, web scraping, or enabling LLMs to control browsers reliably.

github.com · 64 min · AI Agents · Browser Automation · CDP · CLI · Mcp · Vercel
06:00

5 Agent Skills I Use Every Day to Encode My Development Process

每日必用的5个Agent技能:编码你的开发流程

Matt Pocock, a seasoned engineer, shares 5 agent skills he uses daily to encode rigorous, repeatable processes for LLM agents, addressing their lack of memory and tendency to drift. Key skills include: grill-me (exhaustive questioning before coding), to-prd (turning discussions into PRDs), to-issues (slicing PRDs into vertical issues), tdd (forcing red-green-refactor cycles for quality), and improve-codebase-architecture (identifying shallow modules for deepening). The core insight: short, well-crafted skills can dramatically boost agent output quality.

www.aihero.dev · 8 min · Agent Skills · Claude Code · Process Encoding · TDD · Vertical Slices
06:00

How To Make Codebases AI Agents Love

如何让代码库成为AI代理的“理想家园”——深模块设计实践

This article argues that codebase structure matters more than prompts or AGENTS.md files for AI agent output quality. The core idea is applying 'deep modules' from A Philosophy of Software Design: each module exposes a simple interface controlling lots of implementation. The author introduces 'grey box modules'—developers own and test the interface, AI owns the implementation inside. This improves feedback loops (tests are feedback), navigability (filesystem mirrors mental model), and reduces cognitive load (developers only track 7-8 module boundaries). The article notes TypeScript's difficulty enforcing boundaries and recommends the Effect library. For engineers optimizing AI coding workflows.

www.aihero.dev · 5 min · Agent Architecture · AI Engineering · Code · Software Engineering