龙虾调教（完结篇）：从零搭建项目管理的 OpenClaw 助理
前四篇文章解释了为什么这样设计。这篇只讲怎么做——从零开始，把 OpenClaw 配置成一个帮你跑 Linear + GitHub 全链路项目管理的 AI 助理。
龙虾调教（四）：项目管理工程实践优化
把 OpenClaw 从「流程可用」推到生产：持久记忆、脚本执行层、端到端项目自动化。逐项拆解每个改动背后的判断。
龙虾调教（三）：OpenClaw 软件产品开发的项目管理工作流
本文分享如何以 Linear + GitHub 为管理底座，让 OpenClaw 担任敏捷教练和项目管理角色，覆盖需求收集、Sprint 规划、Code Review、验收到回顾的完整开发节奏，让人只在关键决策点介入，其余监控与自动化全部交给 AI。
龙虾调教（二）：让 OpenClaw 帮你把重复工作变成 SOP
本文介绍如何在 OpenClaw 中构建三层 Workflow 系统——通过 AGENTS.md 元规则、WORKFLOW.md 索引和 Playbook 文件，将重复性工作 SOP 化，让 AI 助手读完几个文件就能独立执行任何重复任务，无需每次重新解释。
龙虾调教（一）：成长为个人助手
本文记录了从默认安装出发，逐步将 OpenClaw 配置为私人助理的完整过程——注入身份与行为规范、建立跨 session 的持久记忆、扩展自定义 Skills的过程，让它真正融入你的工作流，而不只是一个问答工具。
OpenClaw Training (Final): Building a Project Management AI Assistant from Scratch
The first four posts explained the design rationale. This one is purely about how to do it — configuring OpenClaw from scratch into an AI assistant that runs your full Linear + GitHub project management pipeline.
OpenClaw Training (IV): Production-Grade Engineering Upgrades
Pushing OpenClaw from 'functionally usable' to production-ready: persistent memory, a script execution layer, and end-to-end project automation. A breakdown of every change and the reasoning behind it.
OpenClaw Training (III): A Project Management Workflow for Software Teams
How to use Linear + GitHub as the management foundation while letting OpenClaw serve as agile coach and project manager — covering the full development cycle from requirement intake to sprint retrospectives, with humans only at key decision points.
OpenClaw Training(II): Using OpenClaw to Turn Repetitive Work into SOPs
How to build a three-layer Workflow system in OpenClaw — using AGENTS.md meta-rules, a WORKFLOW.md index, and Playbook files — to turn repetitive tasks into SOPs that an AI assistant can execute independently after reading just a few files.
OpenClaw Training (I): Growing into a Personal Assistant
A complete walkthrough of configuring OpenClaw from its default state into a personal assistant with identity, persistent memory, and proactive capabilities