我的实习生 - Hermes 和 Openclaw 中的调研工作流
调研交给 AI,省的是写作时间,没省的是判断成本。这篇用三个量化指标(divergence/coverage/credibility)+ FSM 状态机 + 双层控制回路,把 Wikipedia 漫游时那套自然校正机制重新编码进 Hermes 调研系统。
调研交给 AI,省的是写作时间,没省的是判断成本。这篇用三个量化指标(divergence/coverage/credibility)+ FSM 状态机 + 双层控制回路,把 Wikipedia 漫游时那套自然校正机制重新编码进 Hermes 调研系统。
龙虾调教系列续篇。前五篇建立在 OpenClaw 上,这篇记录迁移到 Hermes Agent 之后,如何把原来那套改造思路在新底座上重新实现——以及哪些地方变得更好了。
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。
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。
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。
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。
Hand research to AI and you save writing time — not judgment cost. This post re-encodes the natural correction loop from Wikipedia wandering into the Hermes research system using three metrics (divergence/coverage/credibility), an FSM, and a dual-layer control loop.
The continuation of the OpenClaw Training series. The first five posts were built on OpenClaw; this one documents how to reimplement that same customization approach on Hermes Agent — and where things got better.
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.
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.
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.
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.