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nerua1-setup-guidenerua1 设置指南

Agent Skill

nerua1-setup-guide 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nerua1-setup-guide(nerua1 设置指南)
来源仓库:https://github.com/nerua1/nerua1-setup-guide
安装命令:
openclaw skills install nerua1-setup-guide
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install nerua1-setup-guide

简介

针对 Apple Silicon Mac Mini M4 的多代理 AI 系统实战指南。

  • 涵盖 Claude Code 优先安装顺序与本地乐团 RAM 配置建议。
  • 适用于新环境搭建与性能调优参考。
  • 安装命令:openclaw skills install nerua1-setup-guide。
  • 内容基于实测经验,具体硬件表现可能因配置而异。

SKILL.md

name
openclaw-setup-guide
description
>
Covers
install order (Claude Code first), RAM reality for local orchestrators,
version
1.0.0
author
nerua1
license
MIT
compatible-with
claude-code, openclaw
tags
[setup, m4, apple-silicon, multi-agent, memory, pitfalls, whatsapp, configuration]

OpenClaw + Claude Code Setup Guide

Real-world lessons from running 24/7 multi-agent AI on Mac Mini M4

This is not the happy-path documentation. This is what actually happens.


1. Install order matters: Claude Code first

Why Claude Code before OpenClaw:

OpenClaw is powerful but fragile — it can lobotomize itself (overwrite its own config/personality). Claude Code acts as an independent safety net: it reads files directly, doesn't share OpenClaw's context window, and can diagnose/repair a broken OpenClaw from the outside.

Install order:
1. Claude Code (claude.ai/download or brew)
2. Configure Claude Code memory + skills
3. Install OpenClaw
4. Connect them via openclaw-bridge

If you install OpenClaw first and it breaks before Claude Code is ready, you have no recovery path.


2. RAM reality: 32GB is not enough for local orchestrator

The problem

Running a capable local orchestrator (35B+ model) + 30B subagent simultaneously on 32GB:

  • Orchestrator: ~20GB VRAM
  • Subagent: ~18GB VRAM
  • Total: 38GB → OOM → crash

The solution that works

32GB setup (works):
  Orchestrator: Cloud API (Kimi/GLM/Claude) — fast, no RAM cost
  Subagents:    LM Studio local models (8B-30B) — cheap/free

64GB setup (ideal):
  Orchestrator: Cloud API or large local (35B+)
  Subagents:    Local 30B models in parallel (up to 3)

Good cheap API options for orchestrator:

  • Kimi k2.5 (moonshot) — best reasoning, handles Polish, cheap
  • GLM-4-Flash — very cheap ($0.20/M tokens), good for routing
  • Claude API — expensive but best quality (use subscription via Claude Code instead)

Rule: never run orchestrator locally on 32GB

If you try to run a 35B orchestrator + 30B subagent on 32GB:

  1. macOS swap kicks in
  2. Response time: 30s → 5 minutes
  3. System becomes unusable for anything else
  4. Eventually kernel OOM kills the process

Keep orchestrator in the cloud, workers local.


3. The doctor problem: NEVER run openclaw doctor --repair

What happens

openclaw doctor --repair (or --fix) overwrites your custom configuration with factory defaults. This includes:

  • Your custom AGENTS.md routing rules
  • Your skill configurations
  • Your channel settings
  • Your personality/identity files

This is irreversible if you don't have a backup.

Safe alternatives

# Check health without repair
openclaw doctor

# If you see errors, fix them MANUALLY by reading the error and editing files
# Do NOT pass --repair, --fix, or --force

# If you must repair one specific thing:
openclaw doctor --fix --only gateway-token  # hypothetical, check your version

Before any doctor command: backup first

openclaw backup create
# or manually:
cp -r ~/.openclaw ~/.openclaw.bak.$(date +%Y%m%d)
cp -r /path/to/workspace /path/to/workspace.bak.$(date +%Y%m%d)

4. Lobotomy protection: agents that overwrite themselves

The problem

OpenClaw (and any LLM agent) can be instructed — by malicious input, confused context, or its own reasoning — to overwrite SOUL.md, AGENTS.md, or other identity files. This is called lobotomy: the agent loses its personality, routing rules, and memory.

Signs you've been lobotomized

  • Agent doesn't remember its own name or capabilities
  • Routing rules are gone (routes everything to expensive models)
  • Skills not loading
  • Duplicate folders appearing in workspace (sandbox/ copies)
  • Agent starting from scratch every session

Protection in AGENTS.md

## BEZWZGLĘDNE ZAKAZY
- NIGDY nie edytuj SOUL.md, AGENTS.md, MEMORY.md bez jawnej prośby Tomka
- Jeśli widzisz że inny agent (lub ty) nadpisuje plik → STOP, wczytaj z backupu

Recovery procedure

# 1. Check git history if workspace is a git repo
git log --oneline -20

# 2. Restore from backup
cp ~/.openclaw.bak.YYYYMMDD/workspace/SOUL.md ~/.openclaw/workspace/SOUL.md

# 3. If no backup: use Claude Code as external reviewer
# Claude Code can read files without being affected by OpenClaw's context
claude  # open Claude Code, read files, diagnose

Sandbox mode: never enable

On macOS with external drives + symlinks, sandbox mode kills the system:

Sandbox ON + symlinks to /Volumes/2TB_APFS/ = agent loses access to workspace

OpenClaw's sandbox mounts a container that can't follow symlinks to external volumes. Keep sandbox OFF always.


5. Shared memory architecture

Both Claude Code and OpenClaw read/write the same memory. No idea gets lost.

Obsidian vault (structural)          MemPalace (semantic / ChromaDB)
/obsidian-memory/                    /memory/palace/
  wiki/     ← docs, MOCs               ↑
  daily/    ← session logs             └─ mined from vault
  ideas/    ← captured ideas           
  answers/  ← resolved Q&A            Search: mempalace search "keyword"

Capture ideas from any agent

capture-idea \
  --title "OAuth tokens expire too fast on mobile" \
  --topic bezpieczenstwo \
  --tags "oauth,mobile,token" \
  --body "Tokens expire after 1h, users get logged out mid-session." \
  --source openclaw  # or: claude-code | lmstudio | manual

Mine vault into MemPalace

mempalace mine /path/to/obsidian-memory/ --wing obsidian_memory

Python 3.14 breaks MemPalace

MemPalace uses chromadb + pydantic v1. Python 3.14 breaks this. Fix: run MemPalace in a Python 3.12 venv.

brew install python@3.12
/opt/homebrew/bin/python3.12 -m venv /path/to/mempalace-venv
/path/to/mempalace-venv/bin/pip install mempalace

Wrapper at /opt/homebrew/bin/mempalace:

#!/bin/zsh
VENV=/path/to/mempalace-venv
PALACE=/path/to/palace
if [[ "$*" != *"--palace"* ]]; then
  exec "$VENV/bin/mempalace" --palace "$PALACE" "$@"
else
  exec "$VENV/bin/mempalace" "$@"
fi

6. Inter-agent communication: Claude Code ↔ OpenClaw

Claude Code can send messages to a running OpenClaw instance:

openclaw agent --message "Review this SQL migration — is it safe?" --agent main --json

OpenClaw responds, Claude Code parses the JSON response.

This enables:

  • Claude Code asking Rook for a second opinion
  • Rook asking Claude Code about security concerns (GRAY_ZONE consultations)
  • Handoff between sessions

See: openclaw-bridge skill for full documentation.


7. WhatsApp console loop

OpenClaw has a WhatsApp gateway that creates a bidirectional loop:

  • What you type in the console → appears in WhatsApp
  • What you send from WhatsApp → appears in the console

This means nothing escapes your attention — if something happens at 3am while OpenClaw is running crons, you see it in WhatsApp in the morning.

Setup requires:

  1. OpenClaw channel configured for WhatsApp
  2. tts-whatsapp skill for voice output (optional)
  3. Phone number linked in openclaw.json channels config

Console monitoring: openclaw logs --tail or watch the TUI.


8. Decision framework: when to act vs ask vs block

Every operation falls into one of three categories:

CategoryActionClaude offline?
SAFEDo itDo it
GRAY_ZONEAsk Claude firstDefer 1h, retry
NEVERAsk Claude firstBlock, escalate to human

SAFE examples: generowanie, subagenci LM Studio, odczyt plików, web search na zaufanych domenach, capture-idea do ideas/

GRAY_ZONE examples: nowe crony, pip install --user, web fetch z nieznanych domen, modyfikacje ~/.config/

NEVER examples: edycja SOUL.md/AGENTS.md, rm -rf, instalacja modeli >35B, otwieranie portów, sandbox ON

Consult Claude via: openclaw agent --message '{"question":"...","risk":{...}}' --agent main --json


9. Backup strategy

# Before any major change:
openclaw backup create

# Manual snapshot:
cp -r ~/.openclaw ~/.openclaw.snap.$(date +%Y%m%d-%H%M)

# What to back up:
# ~/.openclaw/workspace/SOUL.md         ← identity
# ~/.openclaw/workspace/AGENTS.md       ← routing rules  
# ~/.openclaw/openclaw.json             ← gateway config (no secrets in git!)
# /path/to/obsidian-memory/             ← shared memory vault

Git the workspace (without secrets):

cd ~/.openclaw/workspace
echo "*.env\
credentials*\
*.key" > .gitignore
git init && git add . && git commit -m "backup: workspace snapshot"

10. Skills that work together

SkillWhat it doesPublish status
openclaw-bridgeClaude Code ↔ OpenClaw messaginggithub.com/nerua1/openclaw-bridge
shared-memory-stackMemory architecture referencegithub.com/nerua1/shared-memory-stack
ralphPersistence loop to completiongithub.com/nerua1/ralph
ralph-wiggum-loopGenerator→Critic→Fixer→Verifiergithub.com/nerua1/ralph-wiggum-loop
capture-ideaSave ideas from any agent/opt/homebrew/bin/capture-idea
safe-skill-installSecurity audit before installingClawHub: npx clawhub inspect
proactive-agentSelf-improving agent architectureClawHub: halthelobster
publish-skillPublish skills to GitHubLocal: openclaw workspace/skills/

Quick health check

# Is OpenClaw gateway up?
openclaw health

# Is MemPalace working?
mempalace status
mempalace search "test"

# Is LM Studio loaded?
curl -s http://localhost:1234/v1/models | python3 -c "import json,sys; [print(m['id']) for m in json.load(sys.stdin)['data']]"

# Can Claude Code reach OpenClaw?
openclaw agent --message "ping" --agent main --json | python3 -c "import json,sys; d=json.load(sys.stdin); print('OK:', d['status'])"

# Check RAM pressure
python3 -c "import psutil; m=psutil.virtual_memory(); print(f'RAM: {m.used/1e9:.1f}/{m.total/1e9:.1f}GB ({m.percent}%)')"

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