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litmuslitmus 搜索

Agent Skill

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。

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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: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。

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openclaw skills install litmus

简介

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。

SKILL.md

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)

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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。

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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。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。

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