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mind-wander走神

Agent Skill

mind-wander 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。

总安装

2,274

周安装

92

GitHub Stars

公开资料未说明

下载量

714
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:mind-wander(走神)
来源仓库:https://github.com/jebadiahgreenwood/mind-wander
安装命令:
openclaw skills install mind-wander
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 mind-wander

简介

mind-wander 是一个基于本地大模型的自主探索代理,用于开放问题的推理与知识发现。

  • 适合在 OpenClaw 中处理复杂研究、创意探索或需要长期记忆支持的场景。
  • 通过私有知识图和 LLM 结合,支持上下文跟踪与问题空间拓展。
  • 安装命令:openclaw skills install mind-wander;需确认本地模型加载与资源占用情况。
  • 注意权限范围,避免触发不必要的联网或文件操作,建议检查仓库维护状态。

SKILL.md

name
mind-wander
description
>

Mind-Wander Skill

Autonomous background reasoning agent. Runs locally on Qwen3.5-9B, consumes zero Anthropic tokens, and elevates findings to your context only when genuinely novel.

How it works

ON_YOUR_MIND.md  →  Qwen3.5 wander agent (every 30min)
  open questions       ↓ tools: query_graph, search_web,
  tangents             ↓         read_file, sandbox_run,
  hypotheses           ↓         check_dead_ends, record_dead_end, elevate()
                       ↓
              novelty gate (strict)
                  ↙         ↘
           MENTAL_EXPLORATION.md    DEAD_ENDS.md + wander graph
           (elevated findings)      (closed threads, never in your context)
                  ↓
           memwatchd detects write
                  ↓
           graph-rag memory (your context)

Prerequisites

  • Qwen3.5-9B-Q8 pulled to Ollama: ollama pull qwen3.5-wander-q8 (or use install script)
  • FalkorDB running (shared with graph-rag-memory skill if installed)
  • Perplexity API key (optional but recommended for web search)
  • graph-rag-memory skill installed (recommended — shares FalkorDB and Ollama)

Quick start

# Install and set up
bash mind-wander/scripts/install.sh

# Write your first open question
echo "## What is the best approach to X?" >> ON_YOUR_MIND.md

# Run manually
python3 mind-wander/run.py --verbose

# Check findings
cat MENTAL_EXPLORATION.md
cat DEAD_ENDS.md

# Status
python3 mind-wander/run.py --status

The ON_YOUR_MIND.md anchor file

Create ON_YOUR_MIND.md in your workspace root with questions and tangents. The agent picks ONE per session. Format freely — the agent reads it as-is.

# On My Mind

## Open Questions
- Does X actually work better than Y in production?
- Is there a paper on Z that I haven't found yet?

## Tangents
- The implementation of A might connect to B in an interesting way

Mark completed items with ## ✅ COMPLETED so the agent skips them.

The novelty gate

  • Restatement of known facts → discarded, nothing written
  • New external finding intersecting open question → elevate()
  • Empirical sandbox result that changes understanding → elevate()
  • Definitively closed thread (≥2 targeted searches) → record_dead_end()

Tools available to the wander agent

ToolDescription
query_graphSearch primary FalkorDB graph for related facts
search_webPerplexity AI web search
read_fileRead workspace .md files
list_filesList workspace .md files
sandbox_runRun Python snippets (numpy/scipy, no network, 30s limit)
check_dead_endsCheck wander graph for previously closed threads
record_dead_endRecord a closed thread (lower bar than elevate)
elevateWrite finding to MENTAL_EXPLORATION.md (strict gate)

Configuration

Edit mind-wander/mind_wander_config.py:

WANDER_MODEL    = "qwen3.5-wander-q8"   # or q4 for lighter
WANDER_OLLAMA   = "http://172.18.0.1:11436"
MAX_TOOL_CALLS  = 20
COOLDOWN_HOURS  = 3   # min hours before revisiting same anchor item

Output files

FileContentsIn graph-rag?
MENTAL_EXPLORATION.mdElevated findings✅ via memwatchd
DEAD_ENDS.mdClosed threads summary❌ never
completions/wander/Full session JSON❌ training data only

Research context

This skill produced the first novel finding in its 10-minute test run: *"Cross-space routing (routing in nomic-space, retrieving in arctic/bge-m3 space) matches same-space baseline accuracy — suggesting domain routing is robust to embedding space discontinuities."* See NOVELTY_LOG.md for tracked findings.

See references/research.md for theoretical foundations and references/setup.md for detailed installation instructions.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

75.42%
按下载量换算538

安全审计

VirusTotal

未展示

ClawScan

可疑

Static analysis

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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