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smart-router-omni智能路由器全向

Agent Skill

smart-router-omni 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

3,231

周安装

132

GitHub Stars

公开资料未说明

下载量

1,035
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:smart-router-omni(智能路由器全向)
来源仓库:https://github.com/e2e5g/smart-router-omni
安装命令:
openclaw skills install smart-router-omni
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 smart-router-omni

简介

全向智能路由器在混合环境中自动选择最优技能链组合执行任务。

  • 支持 OpenClaw 感知例程集成,实现跨平台技能协同运作。
  • 根据任务复杂度与环境状态动态调整资源分配策略。smart-router-omni 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装后即可在 OpenClaw 中作为中央调度器使用。
  • 使用前请验证各子技能兼容性并设置合理的超时降级机制。

SKILL.md

name
smart-router-omni
description
Universal smart routing skill that chooses the best installed skill or skill chain across mixed environments, and automatically applies OpenClaw-aware routing when an OpenClaw workspace or skill inventory is detected. Use when the user asks which skill to use, when multiple skills overlap, when the task spans several phases, or when Codex should inspect local skill metadata and environment signals before choosing a route.

Smart Router Omni

Start broad, then specialize. Default to universal routing, and switch into OpenClaw-aware mode when the environment supports it.

Load the right reference

  • Read references/scoring-rubric.md before scoring candidates.
  • Read references/ambiguity-and-fallbacks.md when confidence is weak or constraints are missing.
  • Read references/environment-detection.md before deciding whether to stay in universal mode or enter OpenClaw mode.
  • Read references/chain-patterns.md when the request obviously spans several phases.
  • Read references/research-notes-2026-03.md when you need the design rationale.

Workflow

  1. Normalize the request into a task card:

- goal - expected artifact - domain - required actions - constraints - environment dependencies

  1. Detect the environment using references/environment-detection.md:

- universal mode - OpenClaw-aware mode

  1. Scan visible skill roots conservatively:

- read SKILL.md frontmatter first - inspect agents/openai.yaml only for shortlisted candidates

  1. Apply hard filters before ranking:

- missing tools - missing login or auth - wrong output type - platform mismatch - safety or policy mismatch

  1. Score viable candidates with references/scoring-rubric.md.
  2. If one skill can finish the task, recommend that skill.
  3. If the task spans phases, recommend a short chain using references/chain-patterns.md.
  4. If confidence is low, ask compact clarification questions instead of forcing a route.
  5. Output:

- recommended skill or chain - confidence - why it won - prerequisites - fallbacks - clarifying questions if needed

Routing policy

  • Prefer explicit capability claims in description over name similarity.
  • Prefer specialist skills when the task, artifact, and dependencies are clear.
  • Prefer workflow skills when the request is end-to-end.
  • Prefer the smallest route that can finish the task well.
  • In OpenClaw-aware mode, treat browser, publishing, memory, and account-bound skills as dependency-sensitive.
  • Abstain and clarify when the top candidates are close.

Standard output

[Routing Decision]
Mode: universal | openclaw-aware
Request: ...
Recommended skill: ...
Confidence: high | medium | low
Why it fits: ...
Missing checks or prerequisites: ...
Fallbacks: ...
Suggested chain: ...
Clarifying question(s): ...

Guardrails

  • Do not rely on a fixed handwritten route table as the primary method.
  • Do not read every full SKILL.md before shortlisting.
  • Do not recommend unavailable skills.
  • Do not hide uncertainty when the top candidates are close.
  • Do not force one skill when a chain is clearly better.

Exit condition

Finish with one recommended skill or skill chain, an explicit mode, a confidence level, key reasons, fallbacks, and any blocking unknowns.


Copyright & License

Copyright (c) 2026 龙虾 (Lobster)

All Rights Reserved.

This skill is proprietary and confidential. The source code, algorithms, documentation, and any accompanying materials are protected by copyright law and international treaties.

You may NOT:

  • Copy, modify, or distribute the source code or documentation
  • Create derivative works based on this skill
  • Use this skill as a template or baseline for developing competing products
  • Reverse engineer, decompile, or disassemble any components
  • Remove or alter any proprietary notices or copyright labels

You MAY:

  • Install and use this skill in your OpenClaw installation
  • Receive updates and support as provided by the author

For licensing inquiries or custom development, contact the author directly.

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

74.76%
按下载量换算774

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。

来源信息

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