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Agent Skill · 研究检索

venice-router(威尼斯路由器)

venice-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。

venice-router 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;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 6,194。

总安装 23,013周安装 922GitHub Stars 2下载 7,450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
1.5.0

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:venice-router(威尼斯路由器)
来源仓库:https://github.com/plusone/venice-router
安装命令:
openclaw skills install venice-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 venice-router

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。

venice-router 用于智能路由查询请求至最合适的 AI 模型,兼顾成本与性能优化。适合在 OpenClaw 中处理复杂或多变语义的任务,自动匹配最低成本的足够能力模型。通过自然语言输入即可触发路由决策,具体参数与分类逻辑需参考原始仓库说明。

安装前需确认是否允许动态调用外部推理服务,并评估其对网络延迟与计费的影响。建议在非关键路径先行测试,确保路由策略符合预期且具备回退机制。

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 venice-router;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

    它需要接触敏感数据,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

venice-router 怎么安装?

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

venice-router 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。

venice-router 需要什么权限?

权限级别是接触敏感数据。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。

venice-router 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。

venice-router 用的人多吗?

公开数据是累计安装 23,013 次,近一周安装 922 次,下载 7,450 次,GitHub 2 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。

venice-router 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
venice-router

威尼斯路由器 · 本页

23,0132接触敏感数据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
venice-router
version
1.5.0
description
Supreme model router for Venice.ai — the privacy-first, uncensored AI platform. Automatically classifies query complexity and routes to the cheapest adequate model. Supports web search, uncensored mode, private-only mode (zero data retention), conversation-aware routing, cost budgets, function calling, thinking/reasoning mode, and 35+ Venice.ai text models. Use when the user wants to chat via Venice.ai, send prompts through Venice, or needs smart model selection to minimize API costs while keeping data private from Big Tech.
homepage
https://venice.ai
source
https://github.com/PlusOne/venice.ai-router-openclaw
user-invocable
true
metadata
openclaw
emoji
🦞🚀
homepage
https://github.com/PlusOne/venice.ai-router-openclaw
os
["linux", "macos"]
requires
bins
["python3"]
env
["VENICE_API_KEY"]
primaryEnv
VENICE_API_KEY
optionalEnv
notes
Python 3.8+ (stdlib only, no pip dependencies). All scripts bundled under scripts/. Source: https://github.com/PlusOne/venice.ai-router-openclaw
cliHelp
python3 venice-router.py --help\
usage
venice-router.py [-h] [--prompt PROMPT] [--tier {cheap,budget,budget-medium,mid,high,premium}] [--model MODEL] [--classify CLASSIFY] [--list-models] [--stream] [--temperature TEMPERATURE] [--max-tokens MAX_TOKENS] [--system SYSTEM] [--prefer-anon] [--uncensored] [--private-only] [--web-search] [--character CHARACTER] [--json] [--thinking] [--conversation CONVERSATION] [--tools TOOLS] [--tool-choice TOOL_CHOICE] [--budget-status] [--session-id SESSION_ID]\
Examples
--prompt \"What is 2+2?\" | --tier mid --prompt \"Explain recursion\" | --stream --prompt \"Write a haiku\" | --web-search --prompt \"Latest AI news\" | --uncensored --prompt \"Creative fiction\" | --private-only --prompt \"Sensitive data\" | --thinking --prompt \"Prove the halting problem\" | --conversation history.json --prompt \"continue\" | --tools tools.json --prompt \"Get weather\" | --budget-status | --classify \"Design a microservices architecture\" | --list-models
install
kind
brew
formula
python
bins
["python3"]
label
Install Python (brew)

Venice.ai Supreme Router

Smart, cost-optimized model routing for Venice.ai — the AI platform for people who don't want Big Tech watching over their shoulder.

Unlike OpenAI, Anthropic, and Google — where every prompt is logged, analyzed, and potentially used to train future models — Venice offers true privacy with zero data retention on private models. Your conversations stay yours. Venice is also uncensored: no content filters, no refusals, no "I can't help with that."

Setup

  1. Get a Venice.ai API key from venice.ai/settings/api
  2. Set the environment variable:
export VENICE_API_KEY="your-key-here"

Or configure in ~/.openclaw/openclaw.json:

{
  "skills": {
    "entries": {
      "venice-router": {
        "enabled": true,
        "apiKey": "YOUR_VENICE_API_KEY"
      }
    }
  }
}

Usage

Route a prompt (auto-selects model)

python3 {baseDir}/scripts/venice-router.py --prompt "What is 2+2?"

Force a specific tier

python3 {baseDir}/scripts/venice-router.py --tier cheap --prompt "Tell me a joke"
python3 {baseDir}/scripts/venice-router.py --tier budget-medium --prompt "Write a Python function"
python3 {baseDir}/scripts/venice-router.py --tier mid --prompt "Explain quantum computing"
python3 {baseDir}/scripts/venice-router.py --tier premium --prompt "Write a distributed systems architecture"

Stream output

python3 {baseDir}/scripts/venice-router.py --stream --prompt "Write a poem about lobsters"

Web search (LLM searches the web and cites sources)

python3 {baseDir}/scripts/venice-router.py --web-search --prompt "Latest news on AI regulation"

Uncensored mode (prefer models with no content filters)

python3 {baseDir}/scripts/venice-router.py --uncensored --prompt "Write edgy creative fiction"

Private-only mode (zero data retention, no Big Tech proxying)

python3 {baseDir}/scripts/venice-router.py --private-only --prompt "Analyze this confidential contract"

Conversation-aware routing (multi-turn context)

# Save conversation history as JSON, then route follow-ups with context
python3 {baseDir}/scripts/venice-router.py --conversation history.json --prompt "Can you add tests too?"

The router analyzes conversation history to keep context: trivial follow-ups ("thanks") go cheap, while follow-ups in complex code discussions stay at the right tier.

Function calling (tool use)

# Define tools in a JSON file (OpenAI tools format)
python3 {baseDir}/scripts/venice-router.py --tools tools.json --prompt "What's the weather in NYC?"
python3 {baseDir}/scripts/venice-router.py --tools tools.json --tool-choice auto --prompt "Search for latest AI news"

Tool definitions use the standard OpenAI format. The router auto-bumps to mid tier minimum for function calling since it requires capable models.

Cost budget tracking

# Show current spending
python3 {baseDir}/scripts/venice-router.py --budget-status

# Track per-session costs
python3 {baseDir}/scripts/venice-router.py --session-id my-project --prompt "help me code"

Set VENICE_DAILY_BUDGET and/or VENICE_SESSION_BUDGET to enforce spending limits. The router auto-downgrades tiers as you approach budget limits.

Classify only (no API call)

python3 {baseDir}/scripts/venice-router.py --classify "Explain the Riemann hypothesis"

List available models and tiers

python3 {baseDir}/scripts/venice-router.py --list-models

Override model directly

python3 {baseDir}/scripts/venice-router.py --model deepseek-v3.2 --prompt "Hello"

Tiers

TierModelsCost (input/output per 1M tokens)Best For
cheapVenice Small (qwen3-4b), GLM 4.7 Flash, GPT OSS 120B, Llama 3.2 3B$0.05–$0.15 / $0.15–$0.60Simple Q&A, greetings, math, lookups
budgetQwen 3 235B, Venice Uncensored, GLM 4.7 Flash Heretic$0.14–$0.20 / $0.75–$0.90Moderate questions, summaries, translations
budget-mediumGrok Code Fast, DeepSeek V3.2, MiniMax M2.1$0.25–$0.40 / $1.00–$1.87Moderate-to-complex tasks, code snippets, structured output
midDeepSeek V3.2, MiniMax M2.1/M2.5, Qwen3 Thinking 235B, Venice Medium, Llama 3.3 70B$0.25–$0.70 / $1.00–$3.50Code generation, analysis, longer writing, reasoning
highGLM 5, Kimi K2 Thinking, Kimi K2.5, Grok 4.1 Fast, Hermes 3 405B, Gemini 3 Flash$0.50–$1.10 / $1.25–$3.75Complex reasoning, multi-step tasks, code review
premiumGPT-5.2, GPT-5.2 Codex, Gemini 3 Pro, Gemini 3.1 Pro (1M ctx), Claude Opus/Sonnet 4.5/4.6$2.19–$6.00 / $15.00–$30.00Expert-level analysis, architecture, research papers

Routing Strategy

The router classifies each prompt using keyword + heuristic analysis:

  1. Length — longer prompts suggest more complex tasks
  2. Keywords — domain-specific terms (e.g., "architecture", "optimize", "prove") signal complexity
  3. Code markers — presence of code blocks, function names, or technical syntax
  4. Instruction depth — multi-step instructions, comparisons, or "explain in detail" bump the tier
  5. Conversational simplicity — greetings, yes/no, small talk stay on the cheapest tier
  6. Conversation history — when --conversation is provided, analyzes full chat context: code in history boosts tier, trivial follow-ups ("thanks") downgrade, tool calls in history signal complexity
  7. Function calling — --tools auto-bumps to at least mid tier (capable models required)
  8. Thinking/reasoning mode — --thinking prefers chain-of-thought reasoning models (Qwen3 Thinking, Kimi K2) and bumps to at least mid tier
  9. Budget constraints — progressive tier downgrade as spending approaches daily/session limits (95% → cheap, 80% → budget, 60% → mid, 40% → high)

The classifier errs on the side of cheaper models — it only escalates when there's strong signal for complexity.

Environment Variables

VariableDescriptionDefault
VENICE_API_KEYVenice.ai API key (required)—
VENICE_DEFAULT_TIERMinimum floor tier — auto-classification never goes below this. Valid: cheap, budget, budget-medium, mid, high, premiumbudget
VENICE_MAX_TIERMaximum tier to ever use (cost cap)premium
VENICE_TEMPERATUREDefault temperature0.7
VENICE_MAX_TOKENSDefault max tokens4096
VENICE_STREAMEnable streaming by defaultfalse
VENICE_UNCENSOREDAlways prefer uncensored modelsfalse
VENICE_PRIVATE_ONLYOnly use private models (zero data retention)false
VENICE_WEB_SEARCHEnable web search by default ($10/1K calls)false
VENICE_THINKINGAlways prefer thinking/reasoning modelsfalse
VENICE_DAILY_BUDGETMax daily spend in USD (0 = unlimited)0
VENICE_SESSION_BUDGETMax per-session spend in USD (0 = unlimited)0

Why Venice.ai?

  • 🔒 Private inference — Models marked "Private" have zero data retention. Your data never trains anyone's model.
  • 🔓 Uncensored — No guardrails blocking legitimate use cases. No refusals, no filters.
  • 🔌 OpenAI-compatible — Same API format, just change the base URL. Drop-in replacement.
  • 📦 30+ models — From tiny efficient models ($0.05/M) to Claude Opus 4.6 and GPT-5.2.
  • 🌐 Built-in web search — LLMs can search the web and cite sources in a single API call.

Tips

  • Use --classify to preview which tier a prompt would hit before spending tokens
  • Set VENICE_MAX_TIER=mid to cap costs and never hit premium models
  • Use --uncensored for creative, security research, or other content mainstream AI won't touch
  • Use --private-only when processing sensitive/confidential data — zero retention guaranteed
  • Use --web-search when you need up-to-date information with cited sources
  • Use --conversation with a JSON message history for smarter multi-turn routing
  • Use --tools to enable function calling — the router auto-bumps to capable models
  • Set VENICE_DAILY_BUDGET=1.00 to cap daily spend at $1 — the router auto-downgrades tiers as you approach the limit
  • Use --budget-status to see a detailed breakdown of your spending by tier
  • Use --thinking for math proofs, logic puzzles, and multi-step reasoning — routes to Qwen3 Thinking or Kimi K2 models
  • The router prefers private (self-hosted) Venice models over anonymized ones when available at the same tier
  • When --uncensored is active, the router auto-bumps to the nearest tier with uncensored models
  • Combine with OpenClaw WebChat for a seamless chat experience routed through Venice.ai

平台分布

OpenClaw

89.93%
按下载量换算6,700

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

接触敏感数据

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。目前只有一个公开来源,建议同时核对源仓库。

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