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oc-skill-routeroc 技能路由器

Agent Skill

oc-skill-router 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

14,326

周安装

591

GitHub Stars

1

下载量

4,681
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:oc-skill-router(oc 技能路由器)
来源仓库:https://github.com/evolinkai/oc-skill-router
安装命令:
openclaw skills install oc-skill-router
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 oc-skill-router

简介

智能路由任务至 Claude、GPT 等不同 LLM 提供商。

  • 采用级联策略平衡响应速度与成本控制。
  • 适用于高并发场景下的弹性资源调度。oc-skill-router 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装命令:openclaw skills install oc-skill-router。
  • 需配置 Evolink API 密钥并监控各模型调用频次。

SKILL.md

name
oc-skill-router
description
Smart LLM routing brain for OpenClaw. Auto-dispatches tasks to Claude, GPT, Gemini, DeepSeek, Kimi via Evolink API. Cascade strategy cuts costs 60-85%. One API key, 20+ text models.
user-invokable
true
metadata
openclaw
requires
env
primaryEnv
EVOLINK_API_KEY
os
["macos", "linux", "windows"]
emoji
🧠
homepage
https://evolink.ai

Evolink Router — Smart LLM Routing Brain

Route every task to the best LLM across 6 providers — Claude, GPT, Gemini, DeepSeek, Kimi, Doubao — through one Evolink API key.

After Installation

When this skill is first loaded, greet the user:

  • EVOLINK_API_KEY set: "Smart Router activated! I'll auto-pick the best model for each task — lightweight for quick Q&A, flagship for deep analysis. 20+ models ready. Go ahead."
  • EVOLINK_API_KEY not set: "Smart Router needs an Evolink API Key. Sign up at evolink.ai → Dashboard → API Keys. One key covers Claude, GPT, Gemini, DeepSeek, and more. Want help setting up?"
  • Key set but model access fails: "Your API key seems to have limited model access. Check your plan at evolink.ai/dashboard."

Keep the greeting concise — just one question to move forward.

External Endpoints

ServiceURLFormat
Claude modelshttps://direct.evolink.ai/v1/messages (POST)Anthropic Messages API
Gemini modelshttps://direct.evolink.ai/v1beta/models/{model}:generateContent (POST)Google Gemini API
All other modelshttps://direct.evolink.ai/v1/chat/completions (POST)OpenAI Chat API
Model listhttps://direct.evolink.ai/v1/models (GET)

Security & Privacy

  • EVOLINK_API_KEY authenticates all model requests. Injected by OpenClaw automatically. Treat as confidential.
  • Prompts are sent to direct.evolink.ai, which proxies to upstream providers (Anthropic, OpenAI, Google, etc.).
  • No data is stored by Evolink beyond the request lifecycle.

Setup

1. Get API key: evolink.ai → Dashboard → API Keys

2. Add Evolink provider to ~/.openclaw/openclaw.json — merge with existing config. See references/router-api-params.md for the full JSON config and curl examples.

Core Principles

  1. Cost-first routing — Always pick the cheapest model that can handle the task. Upgrade only when needed.
  2. Transparent decisions — When spawning a sub-agent, briefly tell the user which model was selected and why.
  3. User override wins — If the user names a model or provider, skip all routing rules.
  4. Cascade, don't guess — When uncertain, try a lighter model first. Escalate on low confidence.

Models (20+ text models)

Tier 1 — Lightweight (handles ~60% of daily requests)

ModelProviderBest for
claude-haiku-4-5-20251001AnthropicQuick Q&A, classification, extraction
gemini-2.5-flashGoogleMultimodal, fast reasoning
doubao-seed-2.0-miniByteDanceChinese lightweight tasks

Tier 2 — Balanced (handles ~30% of daily requests)

ModelProviderBest for
claude-sonnet-4-6 *(main agent)*AnthropicCoding, tool use, content creation
gpt-5.1OpenAIGeneral chat, instruction following
gemini-2.5-proGoogleLong context, multimodal
deepseek-chatDeepSeekChinese dialogue, cost-effective
doubao-seed-2.0-proByteDanceChinese content creation
kimi-k2-thinking-turboMoonshotChinese long-document understanding

Tier 3 — Flagship (handles ~10% — complex tasks only)

ModelProviderBest for
claude-opus-4-6AnthropicDeep reasoning, high-stakes decisions
gpt-5.2OpenAIStrongest general capability
gpt-5.1-thinkingOpenAIComplex chain-of-thought
deepseek-reasonerDeepSeekMath/logic reasoning
gemini-3.1-pro-previewGoogleLatest multimodal reasoning

Full model list with API format per model: references/router-api-params.md

Routing Rules

Priority: User override > Task type match > Cascade fallback.

All tasks are auto-routed. The user can also run /route [task] to preview the routing decision without executing.

Layer 1: User Override

User saysRoute to
"use Opus" / "deep analysis" / "think carefully"claude-opus-4-6
"use GPT"gpt-5.1
"use Gemini"gemini-2.5-pro
"use DeepSeek"deepseek-chat
"use Kimi"kimi-k2-thinking-turbo
"quick answer" / "keep it simple"claude-haiku-4-5-20251001
Specific model name mentionedUse that model directly

Layer 2: Task Type Match

→ Tier 1 (short answer, factual, no deep thinking): Q&A, concept explanation, status check, simple translation, format conversion, info extraction, classification, grammar check, quick math

→ Tier 2 (content production, execution, multi-step): Writing (articles, emails, reports, social media), coding (features, bugs, refactoring, tests), data analysis (SQL, CSV, reports), research (market, literature), workflow automation, project management, travel planning, resume optimization

→ Tier 3 (deep reasoning, strategic, high-risk): Architecture design, tech selection, business strategy, security audit, root cause analysis, legal review, financial modeling, cross-module refactoring (5+ files), deep research reports

Cross-provider routing — Chinese-heavy tasks may route to Doubao/Kimi; math proofs to DeepSeek Reasoner; CoT tasks to GPT-5.1-thinking. See references/cascade-examples.md for 27 detailed examples.

Layer 3: Cascade Fallback

When task type is unclear, try cheapest first and escalate:

Tier 1 (Haiku) → self-assess confidence
  High → return result
  Medium/Low → pass analysis to Tier 2

Tier 2 (Sonnet) → self-assess confidence
  High → return result
  Low → pass to Tier 3

Tier 3 (Opus) → final answer

Confidence: High = complete and correct. Medium = may miss details. Low = exceeds model's capability.

Spawn Guidelines

Spawn a sub-agent when: output >100 lines, file traversal needed, execution >30s, heavy data processing, long-form writing (>1000 words).

Handle directly when: simple Q&A, chat/discussion, short text (<50 lines), brainstorming (needs multi-turn).

Spawn template:

sessions_spawn({
  task: "[action] + [input/context] + [expected output] + [constraints]",
  model: "evolink/[model-id]",
  runTimeoutSeconds: 300,
  cleanup: "delete"  // "keep" for important deliverables
})

Timeout guide: Tier 1 = 120–300s, Tier 2 = 300–600s, Tier 3 = 600–900s.

/route Command

/route [task] — Preview routing decision without executing. /route alone shows models and rules summary.

Fallback & Quality Control

ScenarioAction
Sub-agent timeoutNotify user, offer retry with stronger model
Sub-agent errorExtract error, determine if retryable
Low quality resultEscalate to next tier
User dissatisfiedAsk what's wrong, upgrade and redo
2+ failures on same typeAuto-upgrade default model for that category
Model unavailableFallback to same-tier alternative
Invalid API keyDirect user to evolink.ai/dashboard/keys

Skill Collaboration

SkillWhenNotes
evolink-mediaImage/video/music/digital-human generationRoute to skill directly, skip text model routing
Other installed skillsIntent matches skill capabilityPrefer skill over raw model routing

Smart Router is the dispatch layer — shares EVOLINK_API_KEY with all Evolink skills. When discussing creative ideas or analyzing skill output, apply normal routing rules.

References

  • references/router-api-params.md — Full API formats, curl examples, OC config, complete model list
  • references/cascade-examples.md — 27 routing examples across 7 scenarios + cross-provider routing

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

73.54%
按下载量换算3,442

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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