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Agent Skill · 研究检索

litmus(litmus 搜索)

litmus 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。

litmus 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 22,774。

总安装 4,460周安装 184GitHub Stars 1下载 1,457核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.1

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:litmus(litmus 搜索)
来源仓库:https://github.com/kuberwastaken/litmus
安装命令:
openclaw skills install litmus
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 litmus

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

litmus 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。具有总监的并行自主 ML 研究代理,支持实验分支管理和技能库验证。通过安装命令 openclaw skills install litmus 集成到 OpenClaw 宿主环境。

使用前需确认权限范围、维护状态及是否涉及联网、命令执行或文件读写。建议结合来源仓库和原始 README 进一步核验具体用法和功能边界。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install litmus;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

litmus 怎么安装?

在终端执行 openclaw skills install litmus。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

litmus 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

litmus 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install litmus 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

litmus 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

litmus 用的人多吗?

公开数据是累计安装 4,460 次,近一周安装 184 次,下载 1,457 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

litmus 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
litmus

litmus 搜索 · 本页

4,4601执行命令MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
litmus
version
1.1.1
description
Parallel autonomous ML research agents with a Director, git worktrees for per-agent experiment branches, a Skills library for validated technique reuse, a Synthesizer that distills collective knowledge overnight, and circadian rhythm (leisure 03:00–06:00 for paper reading and creative thinking). Uses OpenClaw sessions_spawn, cron, and steer natively. Use when: (1) start or run ML research agents overnight, (2) check agent status or experiment results, (3) view leaderboard or morning digest, (4) steer or stop agents, (5) ask what agents discovered or are exploring, (6) set up Litmus for the first time. NOT for: general coding, non-ML tasks, or machines without a GPU.
homepage
https://github.com/kuberwastaken/litmus
source
https://github.com/kuberwastaken/litmus
license
MIT-0
tags
[research, ml, machine-learning, training, autonomous-agents, overnight, experiments, gpu, llm, autoresearch]
metadata
openclaw
emoji
🔬
requires
bins
["uv", "git", "python3"]
recommends
bins
["nvidia-smi"]
os
[linux, darwin]
configPaths
["~/.litmus/"]
optional
env
["CLAWRXIV_API_KEY"]

Litmus — Parallel Autonomous ML Research Agents

Litmus spawns multiple OpenClaw subagents that experiment on your GPU overnight. Each runs on its own git branch in a shared lab repository — every experiment is a commit, agents can read each other's code, cherry-pick breakthroughs, and build on the global best at any time.

Validated techniques accumulate in a Skills library (~/.litmus/shared/skills/). A Synthesizer runs at 04:00 to distill collective knowledge into skills and write a research agenda for the next day. A Director runs every 2 hours to steer workers, trigger Compass Resets on stagnation, and orchestrate cross-agent knowledge transfer.

What makes it more than autoresearch:

  • Git worktrees: agents share one repo, each on their own branch — full experiment history,

cherry-pick, and cross-agent code inspection via git -C ~/.litmus/repo log --all

  • Skills library: validated techniques persist and compound — agents don't re-discover wins
  • Synthesizer: distills all overnight notes into reusable skills and a research agenda
  • Compass Reset: Director detects stagnation and forces structured pivots using the skills gap
  • Two-phase experiment budget: quick 90-second check before committing to a full run
  • Structured attempt records: JSON per experiment in shared/attempts/ for rich analytics
  • Leisure mode (03:00–06:00): workers read papers, write moonshot hypotheses, identify gaps
  • Morning digest: research narrative delivered to your chat at 08:00

Everything is a native OpenClaw subagent. No external processes, no PID files.


First-Time Setup

Recommended — ask your OpenClaw agent (runs a guided onboarding conversation):

"Install https://clawhub.ai/kuberwastaken/litmus and set it up for my machine"

Full onboarding instructions: {baseDir}/references/onboarding.md — read that file first.

Or manually:

git clone https://github.com/kuberwastaken/litmus ~/.litmus
bash ~/.litmus/scripts/setup.sh

Clones Karpathy's training harness, builds the shared lab git repo at ~/.litmus/repo/, installs Python deps via uv, downloads ~1 GB of training data. Wait for it to finish.


Starting Research

1 — Prepare workspaces (creates git worktrees)

bash {baseDir}/scripts/prepare-agents.sh --agents 4 --templates architecture,optimizer,general,general

Creates git worktrees under ~/.litmus/agents/, each on its own branch in ~/.litmus/repo/. The shared lab git repo means every agent's experiments are immediately visible to all others:

git -C ~/.litmus/repo log --all --oneline --graph

2 — Spawn research subagents

sessions_spawn
  task: "Read program.md in your current directory and run the research loop forever."
  runtime: "subagent"
  mode: "session"
  agentId: "litmus-worker-arch-1"
  cwd: "~/.litmus/agents/arch-1"

Repeat for each agent, then:

sessions_yield message: "Research agents running. I'll notify you on new discoveries."

Templates: architecture · optimizer · regularization · general Full template details: {baseDir}/references/templates/

3 — Start the Director Layer

bash {baseDir}/scripts/setup-cron.sh --timezone "Your/Timezone"

Registers 6 cron jobs:

CronDefault scheduleRole
litmus-directorEvery 2h during research hoursReviews results, steers workers, Compass Reset on stagnation
litmus-leisure03:00 dailySwitches workers to paper-reading / creative thinking mode
litmus-synthesizer04:00 dailyDistills notes into skills library, writes research agenda
litmus-dawn06:00 dailyWakes workers, queues synthesizer's priority experiments
litmus-watchdogEvery 30 minLiveness check, escape mode on zero improvements
litmus-digest08:00 dailyMorning research narrative delivered to your chat

All times are configurable during onboarding — the setup agent pitches defaults and asks what you'd like to change. Common presets: night owl (01:00/02:00/04:00/07:00), early bird (23:00/00:30/02:00/05:30), intensive (1h director). Pass custom times to scripts/setup-cron.sh with --leisure-start, --synthesizer-time, --dawn-time, --digest-time, --director-hours, --watchdog-minutes.


Managing Agents

Status (experiment counts, best val_bpb, git tree):

bash {baseDir}/scripts/status.sh

Leaderboard (cross-agent, from shared/attempts/ JSON):

bash {baseDir}/scripts/results.sh --top 10
bash {baseDir}/scripts/results.sh --agent arch-1  # single agent

Full lab git history (all agents' experiments as a tree):

git -C ~/.litmus/repo log --all --oneline --graph

Inspect any experiment:

git -C ~/.litmus/repo show <commit-hash>  # see what changed
cat ~/.litmus/shared/attempts/<hash>.json  # see the metrics

Steer (redirect mid-run, no restart):

subagents action: "steer"  target: "litmus-worker-arch-1"
  message: "Stop refining depth. Checkout the best commit from opt-2 and combine their LR with DEPTH=10."

Stop:

subagents action: "kill"  target: "all"

What Agents Write Overnight

PathContents
~/.litmus/shared/attempts/<hash>.jsonStructured record for every experiment (agent, val_bpb, status, title)
~/.litmus/shared/skills/<name>.mdValidated reusable techniques with YAML frontmatter
~/.litmus/shared/notes/discoveries/Per-improvement discovery notes
~/.litmus/shared/notes/anomalies/Unexpected result notes
~/.litmus/shared/notes/moonshots/Speculative hypotheses from leisure
~/.litmus/shared/notes/synthesis/Synthesizer's research agenda and combination matrix
~/.litmus/shared/discoveries.mdCross-agent knowledge base (flat, for quick reading)
~/.litmus/shared/midnight-reflections.mdLeisure agent's nightly narrative
~/.litmus/repo/ (git)All experiment commits across all agents on their branches

Reference Files

  • {baseDir}/references/onboarding.md — first-time setup conversation
  • {baseDir}/references/program.md — worker agent loop (git-aware, skills-reading, two-phase budget)
  • {baseDir}/references/director.md — Director cron (Compass Reset, cross-pollination)
  • {baseDir}/references/leisure.md — Leisure mode (paper reading, structured notes, skill extraction)
  • {baseDir}/references/synthesizer.md — Synthesizer cron (knowledge distillation, skills library)
  • {baseDir}/references/dawn.md — Dawn cron (wake workers, queue experiments)
  • {baseDir}/references/watchdog.md — Watchdog cron (liveness, escape mode)
  • {baseDir}/references/digest.md — Morning digest (research narrative)
  • {baseDir}/references/templates/ — Research focus templates
  • {baseDir}/references/clawrxiv.md — ClawRxiv integration (optional auto-publishing)

平台分布

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按下载量换算1,132

安全审计

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install litmus 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。目前只有一个公开来源,建议同时核对源仓库。

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