kdlbs/kandev：README 来源编辑指南

kdlbs/kandev 的 README 将项目描述为"AI Kanban & Development Environment. Orchestrate multiple agents, review changes, open PRs. Multi-provider, self-hostable, no telemetry."。本文只整理仓库能直接核验的内容，不把星标、Fork 或宣传语当成质量证明。README 在"Kandev"下的说明是：Manage and run tasks in parallel. Orchestrate agents. Review changes. Ship value.。这给出的首先是项目边界，而不是已经完成的生产验证。

从 README 的"Vision"和相关条目看，读者可以先判断它是否解决自己的具体问题：Your workflow - Every team is different, and not every developer uses AI the same way. Define workflows once, share them across the team, and give everyone a consistent process for working with agents - regardless of experience level.。如果你的目标与这段说明不一致，就不应仅凭项目热度采用它。这里保留原项目名、命令和组件名，方便回到一手来源核对。 README 还列出了另一条可核对的信息：Review-first - Humans support production systems. We need to understand (yet) and trust the code that gets deployed.。这类原文条目可以帮助读者设计试运行步骤，但不能代替自己的环境测试。

README 把工作方式分散写在"What"等段落中。可确认的线索包括：Organize work across kanban and pipeline views with opinionated workflows and execute multiple tasks in parallel. Assign agents from any provider, and review their output in an integrated workspace - file editor, file tree, terminal,。这篇整理没有把未写出的架构、性能或安全边界补成结论；真正的运行链仍应结合仓库目录、配置文件和版本标签检查。

第一次安装应从 README 给出的入口开始。当前可复核的命令是： brew install kdlbs/kandev/kandev kandev 如果仓库没有提供命令，本文不会替它编造安装步骤，而是建议先打开 README 的"What"部分，确认系统依赖、默认端口和首次初始化动作。

日常使用的细节取决于项目实际文档。README 的"What"段落提到：Run it locally or self-host it on your own infrastructure and access it from anywhere via Tailscale or any VPN.。对于配置文件、环境变量、权限和数据目录，当前稿只记录来源明确的部分；未写明的默认值必须在测试环境中验证，并保留可回滚的配置副本。 同一部分还提到：Remote agents - Running multiple agents on a large codebase can quickly saturate a local machine. The goal is a single control plane: offload execution to servers, orchestrate from anywhere, including your phone.。