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
- 2. Detect the environment using
references/environment-detection.md:
- universal mode
- OpenClaw-aware mode
- 3. Scan visible skill roots conservatively:
- read
SKILL.md frontmatter first
- inspect
agents/openai.yaml only for shortlisted candidates
- 4. Apply hard filters before ranking:
- missing tools
- missing login or auth
- wrong output type
- platform mismatch
- safety or policy mismatch
- 5. Score viable candidates with
references/scoring-rubric.md. - If one skill can finish the task, recommend that skill.
- If the task spans phases, recommend a short chain using
references/chain-patterns.md. - If confidence is low, ask compact clarification questions instead of forcing a route.
- 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
CODEBLOCK0
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.
Smart Router Omni
从广泛开始,再走向专精。默认采用通用路由,当环境支持时切换到 OpenClaw 感知模式。
加载正确的参考
- - 在对候选方案评分前,先阅读 references/scoring-rubric.md。
- 当置信度较低或缺少约束条件时,阅读 references/ambiguity-and-fallbacks.md。
- 在决定是保持通用模式还是进入 OpenClaw 模式前,先阅读 references/environment-detection.md。
- 当请求明显涉及多个阶段时,阅读 references/chain-patterns.md。
- 当需要设计原理时,阅读 references/research-notes-2026-03.md。
工作流程
- 1. 将请求标准化为任务卡片:
- 目标
- 预期产出物
- 领域
- 所需操作
- 约束条件
- 环境依赖
- 2. 使用 references/environment-detection.md 检测环境:
- 通用模式
- OpenClaw 感知模式
- 3. 保守地扫描可见技能根目录:
- 先读取 SKILL.md 的前置信息
- 仅对入围候选方案检查 agents/openai.yaml
- 4. 在排序前应用硬性过滤器:
- 缺少工具
- 缺少登录或认证
- 输出类型错误
- 平台不匹配
- 安全或策略不匹配
- 5. 使用 references/scoring-rubric.md 对可行候选方案进行评分。
- 如果单个技能可以完成任务,推荐该技能。
- 如果任务跨越多个阶段,使用 references/chain-patterns.md 推荐一个短链。
- 如果置信度较低,提出简洁的澄清问题,而不是强行指定路由。
- 输出:
- 推荐的技能或链
- 置信度
- 胜出原因
- 前置条件
- 备选方案
- 如有需要,提出澄清问题
路由策略
- - 优先考虑 description 中明确的能力声明,而非名称相似性。
- 当任务、产出物和依赖关系明确时,优先考虑专精技能。
- 当请求是端到端时,优先考虑工作流技能。
- 优先选择能很好完成任务的最小路由。
- 在 OpenClaw 感知模式下,将浏览器、发布、内存和账户绑定技能视为依赖敏感型。
- 当顶级候选方案接近时,放弃并澄清。
标准输出
md
[路由决策]
模式:通用 | openclaw-感知
请求:...
推荐技能:...
置信度:高 | 中 | 低
匹配原因:...
缺失检查或前置条件:...
备选方案:...
建议链:...
澄清问题:...
护栏
- - 不要将固定的手工路由表作为主要方法。
- 在入围前不要读取每个完整的 SKILL.md。
- 不要推荐不可用的技能。
- 当顶级候选方案接近时,不要隐藏不确定性。
- 当链明显更好时,不要强行使用单个技能。
退出条件
以一个推荐的技能或技能链、明确的模式、置信度、关键原因、备选方案以及任何阻塞性未知项结束。
版权与许可
版权所有 (c) 2026 龙虾 (Lobster)
保留所有权利。
本技能为专有和机密内容。源代码、算法、文档及任何附带材料均受版权法和国际条约保护。
您不得:
- - 复制、修改或分发源代码或文档
- 基于本技能创作衍生作品
- 将本技能用作开发竞争产品的模板或基线
- 逆向工程、反编译或反汇编任何组件
- 移除或更改任何专有声明或版权标签
您可以:
- - 在您的 OpenClaw 安装中安装并使用本技能
- 接收作者提供的更新和支持
如需许可咨询或定制开发,请直接联系作者。