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RTK：Coding Agent 不该把整段 Shell 输出塞进上下文 介绍 RTK 如何在命令输出进入模型上下文前过滤测试、Git、搜索和构建噪声，把 Context Engineering 做到工具输出边界。
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Google packages Skills and MCP in one plugin How the Google Cloud Developer Plugin packages Skills, MCP configuration, and rules, and what this means for distribution, versions, and permissions.
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Google 把 Skill 和 MCP 打成一个插件 拆解 Google Cloud Developer Plugin 如何把 Skill、MCP 配置和规则按 Agent Plugins 规范打包，暴露跨 Coding Agent 的分发、版本与权限问题。
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Munim Computer Use lets agents act on desktop controls Munim Computer Use reads the accessibility tree and operates desktop controls by element ID, reducing reliance on fragile screenshot-coordinate automation.
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Munim Computer Use：让 Agent 按控件操作桌面 介绍 Munim Computer Use 如何先读取可访问性树，再按元素 ID 操作桌面控件，减少依赖截图坐标的脆弱自动化。
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jcode moves coding-agent bottlenecks into the harness How jcode handles multi-agent resources, state synchronization, and verification in its harness, beyond the capabilities of the model alone.
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jcode：把 Coding Agent 的瓶颈从模型转到 Harness 介绍 jcode 如何把多 Agent 的资源、状态同步和验证放到 Harness 层，说明并行 Coding Agent 的瓶颈不只是模型能力。
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Coding agent security is now a supply-chain problem Coding agents read and execute skills, MCP tools, dependencies, and repository instructions. These need software supply-chain controls.
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AI Coding Agent 的安全问题已经进了供应链 说明 Coding Agent 会读取并执行 Skill、MCP、依赖和仓库指令，因此这些组件应按软件供应链治理，而不只是做 Prompt 安全。
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GitHub Agentic Workflows: agents inside GitHub Actions How GitHub Agentic Workflows puts judgment-based automation inside GitHub Actions while retaining permissions, controlled outputs, approval, and security.
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GitHub Agentic Workflows：把 Agent 装进 GitHub Actions 介绍 GitHub Agentic Workflows 如何把需要判断的自动化放进 GitHub Actions，同时保留权限、输出、审批和安全边界。
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AI Engineering Harness: verifiable delivery for AI code A multi-agent engineering workflow uses isolated worktrees, CI, cold-start review, evidence gates, and layered context to control delivery of AI-generated code.