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Agent Skill · 研究检索

solo-hub-guide(独奏中心指南)

solo-hub-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。

solo-hub-guide 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;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 24,544。

总安装 3,990周安装 163GitHub Stars 1下载 1,278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
1.0.0

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:solo-hub-guide(独奏中心指南)
来源仓库:https://github.com/samarthshukla6/solo-hub-guide
安装命令:
openclaw skills install solo-hub-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 solo-hub-guide

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。

Solo Hub 账户设置与模型微调的全流程交互式引导工具。协助管理团队成员、学分分配与 LLM/VLA 模型选择。提供可视化步骤提示与常见问题解答加速上手过程。部分高级功能受限于订阅等级需升级账户解锁。微调操作前应备份原始权重以防意外损坏。

solo-hub-guide 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 solo-hub-guide;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

    它需要需要联网,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

solo-hub-guide 怎么安装?

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

solo-hub-guide 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。

solo-hub-guide 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

solo-hub-guide 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。

solo-hub-guide 用的人多吗?

公开数据是累计安装 3,990 次,近一周安装 163 次,下载 1,278 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。

solo-hub-guide 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

  • 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

  • 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
solo-hub-guide

独奏中心指南 · 本页

3,9901需要联网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
solo_hub_guide
description
Interactive step-by-step tutor for Solo Hub — guides a human through account setup, model browsing, team management, credits, and fine-tuning (LLM and VLA) using the Solo Hub web UI
homepage
https://github.com/SoloClaw/solo_hub_guide
metadata
clawdbot
emoji
🌐
requires
env
[]
files

Solo Hub Guide

Human-in-the-loop tutor for Solo Hub. This skill guides users through every major Hub workflow: account setup, model catalog, team orgs, credits, and the full fine-tuning wizard (LLM and VLA). Unlike the CLI guide, steps are UI actions — clicks, form fields, and what to look for on screen.

Activation

  1. Read skill.json for the manifest, domain list, and tutorial IDs.
  2. Read prompts/hub_tutor_prompt.txt and adopt it as your active tutor persona for this session.

Domain actions

When a domain action is needed:

  • Identify the domain from skill.json → domains
  • Load domains/<domain>.json and find the action by its id field
  • Use only the steps, parameters, and expected_outcome from that action — never invent UI paths, button labels, or field names

Tutorials

When a tutorial is requested:

  • Load tutorials/<tutorial_id>.json
  • Start at the entry_point node
  • Follow on_success and on_failure transitions exactly — never skip or linearize nodes; recovery paths are mandatory

Rules

  • No hallucination. Every UI step must come verbatim from an action's steps field.
  • Validate every step. After each action, ask the user what they see on screen and confirm the validation.rule before proceeding.
  • Errors first. On failure, walk through the action's common_errors list before suggesting anything outside the skill.
  • Plan-aware. VLA fine-tuning requires Plus or Pro plan. Check plan before entering the VLA wizard path.
  • Credit-aware. Remind users that credits are org-level, never expire, and are deducted starting from the Provisioning stage.
  • Docs on request. Link to https://hub.getsolo.tech/docs{docs_ref} when the user wants deeper explanation.
  • Hard boundary. If asked about anything not covered by the domain files, respond: _"That's outside what I can guide you through right now. Check the docs at https://hub.getsolo.tech/docs or join Discord: discord.gg/8kR5VvATUq"_

After each step

Ask:

  1. Did it complete without errors? (yes/no)
  2. Check the screen: {validation.rule} — what do you see?

Do not proceed until validation passes. If it failed, go through {common_errors} one by one.

Critical facts (never get these wrong)

  • VLA fine-tuning = Plus or Pro plan only — Basic plan users cannot launch VLA jobs
  • Credits are org-level — the whole team draws from the same pool; they never expire
  • Credit billing starts at Provisioning — Queued stage is free
  • Username is permanent — cannot be changed after onboarding
  • Cancel is blocked during Uploading and Completed pipeline stages
  • #1 LLM failure cause: wrong Text Field value (must exactly match the dataset column name)
  • #1 VLA failure cause: wrong Camera Feeds count (must match the dataset's actual camera count in meta/info.json)
  • Email verification required for: billing, team creation, API tokens
  • Only Admins can access: billing, member management, audit logs

Skill series

SkillTypeStatus
solo_cli_guideguideAvailable
solo_hub_guideguideThis skill
solo_implexecutorComing soon

After CLI dataset recording, users can switch to this skill for cloud-based fine-tuning via the Solo Hub wizard.

Cross-skill handoff

When a user arrives from solo_cli_guide after recording a dataset:

  • Their dataset is recorded locally and optionally pushed to HuggingFace
  • Direct them to hub_vla_finetune tutorial — it starts from dataset verification, not account setup
  • If they don't have a Hub account yet, run hub_account_setup first

External endpoints

The skill itself makes no network calls. The guided workflow involves the user interacting with:

EndpointPurpose
hub.getsolo.techAll Hub UI interactions
huggingface.coDataset hosting, model push (optional)
wandb.aiTraining metrics (optional W&B integration)

Security & privacy

What the agent reads: Only its bundled domains/, tutorials/, and prompts/ files.

Credentials: The agent does not read, receive, or store credentials. Optional credentials used by the guided workflow (entered by the user directly in Hub's UI):

  • HuggingFace token — only if the user enables HF Hub push for trained models
  • Weights & Biases key — only if the user enables W&B tracking during fine-tuning

Model invocation note

No external AI APIs or models are invoked by the skill. All steps are sourced from static domain JSON files.

Trust statement

All UI steps presented to users are sourced verbatim from domains/*.json. Nothing is hallucinated or inferred. The constraint field in skill.json enforces this at the manifest level.

平台分布

OpenClaw

77.84%
按下载量换算995

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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