---

name: Hive Task Router slug: hive-task-router version: 1.0.0 description: Intelligent task routing system that identifies task types (web/code/data/doc/chat) and routes to optimal models with appropriate execution mode (subagent/main session). Supports any AI model provider via environment variables. author: qiongcao requires:

• openclaw-cli
• bash os:
• darwin
• linux
• windows-wsl tags:
• routing
• task-management
• subagent
• productivity
• multi-provider

---

Hive Task Router

Smart task distribution system for OpenClaw — Automatically routes tasks to optimal models and execution modes based on task type recognition.

Universal Model Support — Works with any AI model provider (Bailian, OpenAI, Anthropic, etc.) via environment variable configuration.

---

When to Use

Automatic Trigger Rules (For AI Agents)

Use this skill automatically when the user's message contains ANY of these patterns:

Trigger Pattern	Keywords	Action
Code tasks	代码、编程、脚本、函数、nodejs、react、vue、typescript、javascript、html、css、前端、后端、api、接口、调试、bug、优化、重构	Run router.sh → Execute with code model + subagent
Web research	搜索、查找、调研、研究、github、项目、趋势、报告、分析、对比、评测、最新、2026、新闻、动态	Run router.sh → Execute with web model + subagent
Data tasks	数据、分析、统计、图表、可视化、excel、csv、json、处理、转换	Run router.sh → Execute with data model + subagent
Documentation	文档、说明、教程、指南、手册、readme、wiki、注释、文档化	Run router.sh → Execute with doc model + subagent
Batch tasks	多个、批量、同时、并行、一起	Run router.sh for each → Execute in parallel

Do NOT use this skill when:

• Simple chat/conversation (你好、谢谢、安排、计划、汇报、总结)
• Quick questions (what is, how to, explain)
• Tasks already in progress
• User explicitly says "don't use subagent" or "just answer directly"

Manual Trigger (For Users)

Users can explicitly trigger this skill by:

• Running router.sh "task description" directly
• Saying "use hive router" or "analyze this task"
• Asking "which model should I use for this task"

Decision Flow for AI Agents

Receive user message
    ↓
Contains specific trigger keywords? (code/web/data/doc/batch)
    ↓
YES → Run router.sh to analyze
    ↓
Get recommended model + execution mode
    ↓
Execute with recommended configuration
    ↓
Report result to user
    ↓
NO → Check for vague task keywords? (任务、帮忙、处理、搞定、完成)
    ↓
YES → Ask clarifying question (see "Vague Task Handling")
    ↓
User clarifies → Re-analyze with new info
    ↓
NO → Handle directly (no routing needed)

Vague Task Handling

When user message is vague (e.g., "做个任务", "帮忙处理一下", "搞定这件事"):

Step 1: Acknowledge and ask

好的主人，请问是什么类型的任务？

💻 写代码/脚本？
   - 例如："写个 Python 脚本"、"开发一个 API"

🔍 搜索调研？
   - 例如："搜索最新趋势"、"调研竞品"

📊 数据处理？
   - 例如："分析 Excel 数据"、"转换 JSON 格式"

📄 写文档？
   - 例如："写 API 文档"、"编写教程"

💬 还是只是聊天？
   - 例如："今天有什么安排"、"帮我总结一下"

或者您直接告诉我具体内容，我来判断！

Step 2: User clarifies

User: "写个脚本处理数据"
  ↓
Now contains: "脚本" (code) + "数据" (data)
  ↓
Priority: code > data
  ↓
Execute with: qwen3-coder-plus + subagent

Vague Keywords (trigger clarification):

• 任务、帮忙、处理、搞定、完成、做一下、弄一下
• 这个、那个、一件事、一个东西

Specific Keywords (trigger automatic routing):

• code: 脚本、代码、编程、函数、nodejs、react...
• web: 搜索、调研、分析、趋势、报告、对比...
• data: 数据、统计、图表、excel、csv、json...
• doc: 文档、教程、指南、readme、wiki...

Ideal scenarios:

• Handling multiple concurrent tasks
• Technical development requiring code-specialized models
• Research tasks needing web search capabilities
• Mixed workloads with varying complexity
• Multi-provider environments (Bailian + OpenAI + Anthropic)

---

Quick Reference

Task Type Recognition Keywords

Type	Keywords (Chinese)	Keywords (English)	Priority
web 🔍	搜索、查找、调研、研究、github、项目、趋势、报告、分析、对比、评测、最新、2026、新闻、动态	search, research, github, project, trend, report, analysis, comparison, latest, news	1 (Highest)
code 💻	代码、编程、脚本、函数、nodejs、react、vue、typescript、javascript、html、css、前端、后端、api、接口、调试、bug、优化、重构	code, programming, script, function, nodejs, react, vue, typescript, javascript, html, css, frontend, backend, api, debug, bug, optimize, refactor	2
data 📊	数据、分析、统计、图表、可视化、excel、csv、json、处理、转换	data, analysis, statistics, chart, visualization, excel, csv, json, processing, conversion	3
doc 📄	文档、说明、教程、指南、手册、readme、wiki、注释、文档化	documentation, guide, tutorial, manual, readme, wiki, comment, document	4
chat 💬	你好、谢谢、再见、今天、明天、安排、计划、汇报、总结、提醒、备忘	hello, thanks, goodbye, today, tomorrow, plan, schedule, summary, reminder, memo	5 (Default)

Model Selection Rules

Note: Model IDs are configurable via environment variables. Replace provider/ with your actual model provider (e.g., bailian/, openai/, anthropic/).

Task Type	Default Model	Environment Variable	Reason
code	provider/qwen3-coder-plus	HIVE_MODEL_CODE	Specialized in code generation and debugging
web	provider/qwen3-max	HIVE_MODEL_WEB	Strong search and reasoning capabilities
data	provider/qwen3-coder-plus	HIVE_MODEL_DATA	Code-based data processing
doc	provider/qwen3.5-plus	HIVE_MODEL_DOC	Good text generation, cost-effective
chat	provider/qwen3.5-plus	HIVE_MODEL_CHAT	Best for casual conversation, cost-effective

Model Configuration Examples

Bailian (通义千问):

export HIVE_MODEL_CODE="bailian/qwen3-coder-plus"
export HIVE_MODEL_WEB="bailian/qwen3-max-2026-01-23"
export HIVE_MODEL_CHAT="bailian/qwen3.5-plus"
export HIVE_MODEL_DOC="bailian/qwen3.5-plus"
export HIVE_MODEL_DATA="bailian/qwen3-coder-plus"

Model Validation Modes

Automatic Model Detection (Recommended):

# Auto-detect available models from OpenClaw
export HIVE_VALIDATE_MODEL=auto

First run: Detects models and caches configuration
Subsequent runs: Uses cached config (24h TTL)
Benefit: No manual configuration needed!

Manual Validation Modes:

Mode	Environment Variable	Behavior	Use Case
Auto (Recommended)	export HIVE_VALIDATE_MODEL=auto	Auto-detect + cache 24h	Best for most users
Cache	export HIVE_VALIDATE_MODEL=cache	Validate once, cache 24h	Manual config, stable
Always	export HIVE_VALIDATE_MODEL=1	Validate every execution	Debugging, changes
Never	export HIVE_VALIDATE_MODEL=0	Skip validation	Production, known config

Cache Configuration:

# Cache directory (default: ~/.hive-task-router)
export HIVE_CACHE_DIR="$HOME/.hive-task-router"

# Cache TTL in seconds (default: 86400 = 24 hours)
export HIVE_CACHE_TTL=86400

Validation Behavior:

• ✅ Checks if model IDs contain provider/ placeholder
• ✅ Warns if placeholder detected
• ✅ Caches validation result (configurable TTL)
• ✅ Non-blocking (warnings only, doesn't stop execution)

OpenAI:

export HIVE_MODEL_CODE="openai/gpt-4"
export HIVE_MODEL_WEB="openai/gpt-4-turbo"
export HIVE_MODEL_CHAT="openai/gpt-3.5-turbo"
export HIVE_MODEL_DOC="openai/gpt-3.5-turbo"
export HIVE_MODEL_DATA="openai/gpt-4"

Anthropic (Claude):

export HIVE_MODEL_CODE="anthropic/claude-3-5-sonnet"
export HIVE_MODEL_WEB="anthropic/claude-3-opus"
export HIVE_MODEL_CHAT="anthropic/claude-3-haiku"
export HIVE_MODEL_DOC="anthropic/claude-3-haiku"
export HIVE_MODEL_DATA="anthropic/claude-3-5-sonnet"

Mixed Providers:

# Use best model for each task type
export HIVE_MODEL_CODE="anthropic/claude-3-5-sonnet"  # Best for code
export HIVE_MODEL_WEB="openai/gpt-4-turbo"           # Best for search
export HIVE_MODEL_CHAT="bailian/qwen3.5-plus"        # Cost-effective

Execution Mode Rules

Task Type	Execution Mode	Reason
chat	Main Session	Quick response, no need for isolation
code/web/data/doc	Subagent	Long-running tasks, parallel execution, session isolation

Priority Rule: When multiple keywords match, use the highest priority type (web > code > data > doc > chat).

---

Usage

For AI Agents (Automatic Integration)

When installed as an OpenClaw Skill, the agent will automatically use this skill when:

1. User message contains task keywords (code/web/data/doc/batch)
2. Task is long-running (not a simple Q&A)
3. Multiple tasks need parallel execution

Agent Decision Example:

User: "帮我写一个 Python 脚本处理 Excel 数据"
  ↓
Agent checks: Contains "脚本" (code) + "数据" (data)
  ↓
Priority: code > data
  ↓
Agent executes:
openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-coder-plus \
  --task "帮我写一个 Python 脚本处理 Excel 数据"

Configuration for Agents: Add to agent's AGENTS.md or SOUL.md:

## Hive Task Router Integration

When receiving tasks:
1. Check if message contains task keywords (see SKILL.md)
2. If yes → Use hive-task-router skill
3. If no → Handle directly

---

Method 1: Router Script (Recommended)

The router script automatically analyzes tasks and outputs recommended execution commands.

# Basic usage
bash router.sh "帮我写一个 Node.js 脚本"

# Analyze research task
bash router.sh "搜索 2026 年最新的前端趋势"

# Analyze data task
bash router.sh "分析这个 JSON 数据并生成图表"

Output format:

================================
蜂巢智能任务分发系统 - 路由分析
================================

任务描述：帮我写一个 Node.js 脚本
任务类型：code
推荐模型：bailian/qwen3-coder-plus
执行方式：subagent

📦 代码任务 - 使用 qwen3-coder-plus 模型
   适合：Node.js、前端代码、脚本编写

================================
推荐执行命令:
================================

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-coder-plus \
  --task "帮我写一个 Node.js 脚本"

Method 2: Manual Commands

Code Tasks

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-coder-plus \
  --task "帮我写一个 Express API 服务"

Web Research Tasks

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-max-2026-01-23 \
  --task "调研 5 个 React UI 库"

Chat Tasks (Main Session)

openclaw agent \
  --session-id agent:main:chat \
  --model bailian/qwen3.5-plus \
  --message "今天有什么安排"

Data Processing Tasks

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-coder-plus \
  --task "处理这个 CSV 文件并生成统计报告"

Documentation Tasks

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3.5-plus \
  --task "为这个项目编写 README 文档"

Method 3: Parallel Execution

For batch tasks, use parallel subagents:

# Spawn multiple subagents concurrently
openclaw sessions spawn --mode run --runtime subagent --model bailian/qwen3-max-2026-01-23 --task "调研项目 A" &
openclaw sessions spawn --mode run --runtime subagent --model bailian/qwen3-max-2026-01-23 --task "调研项目 B" &
openclaw sessions spawn --mode run --runtime subagent --model bailian/qwen3-max-2026-01-23 --task "调研项目 C" &

# Wait for all to complete
wait

# Then collect and summarize results

---

Examples

Example 1: Code Development Task

User Input:

帮我写一个 Node.js 文件处理脚本，支持读取 CSV 和 JSON 格式

Router Analysis:

• Matched Keywords: Node.js, 脚本，文件处理
• Task Type: code
• Recommended Model: bailian/qwen3-coder-plus
• Execution Mode: subagent

Execution Command:

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-coder-plus \
  --task "帮我写一个 Node.js 文件处理脚本，支持读取 CSV 和 JSON 格式"

---

Example 2: Web Research Task

User Input:

搜索 2026 年最新的前端趋势，包括 React、Vue、Svelte 的对比

Router Analysis:

• Matched Keywords: 搜索，2026, 趋势，对比
• Task Type: web (Priority 1)
• Recommended Model: bailian/qwen3-max-2026-01-23
• Execution Mode: subagent

Execution Command:

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-max-2026-01-23 \
  --task "搜索 2026 年最新的前端趋势，包括 React、Vue、Svelte 的对比"

---

Example 3: Daily Chat Task

User Input:

今天有什么安排？帮我总结一下昨天的工作

Router Analysis:

• Matched Keywords: 今天，安排，总结
• Task Type: chat
• Recommended Model: bailian/qwen3.5-plus
• Execution Mode: main_session

Execution Command:

openclaw agent \
  --session-id agent:main:chat \
  --model bailian/qwen3.5-plus \
  --message "今天有什么安排？帮我总结一下昨天的工作"

---

Example 4: Data Analysis Task

User Input:

分析这个销售数据 Excel 文件，生成可视化图表和统计报告

Router Analysis:

• Matched Keywords: 分析，数据，Excel, 图表，统计
• Task Type: data
• Recommended Model: bailian/qwen3-coder-plus
• Execution Mode: subagent

Execution Command:

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3-coder-plus \
  --task "分析这个销售数据 Excel 文件，生成可视化图表和统计报告"

---

Example 5: Documentation Task

User Input:

为这个 Python 项目编写完整的 API 文档和使用教程

Router Analysis:

• Matched Keywords: 文档，教程
• Task Type: doc
• Recommended Model: bailian/qwen3.5-plus
• Execution Mode: subagent

Execution Command:

openclaw sessions spawn \
  --mode run \
  --runtime subagent \
  --model bailian/qwen3.5-plus \
  --task "为这个 Python 项目编写完整的 API 文档和使用教程"

---

Example 6: Mixed Task Batch (Parallel)

User Input (Multiple Tasks):

1. 写个脚本处理 JSON 数据
2. 搜索最新 AI 工具
3. 今天有什么安排

Router Analysis:

• Task 1: code → qwen3-coder-plus + subagent
• Task 2: web → qwen3-max-2026-01-23 + subagent
• Task 3: chat → qwen3.5-plus + main_session

Parallel Execution:

# Task 1 & 2 run in parallel subagents
openclaw sessions spawn --mode run --runtime subagent --model bailian/qwen3-coder-plus --task "写个脚本处理 JSON 数据" &
openclaw sessions spawn --mode run --runtime subagent --model bailian/qwen3-max-2026-01-23 --task "搜索最新 AI 工具" &

# Task 3 runs in main session (non-blocking)
openclaw agent --session-id agent:main:chat --model bailian/qwen3.5-plus --message "今天有什么安排"

# Wait for subagents
wait

Performance: 3x faster than sequential execution

---

Installation

Install via ClawHub

clawhub install qiongcao/hive-task-router

Manual Installation

1. Clone or download this skill folder
2. Copy to your OpenClaw skills directory:

   cp -r hive-task-router ~/.openclaw/workspace/skills/
   

3. Make router script executable:

   chmod +x ~/.openclaw/workspace/skills/hive-task-router/router.sh
   

4. Configure models for your provider:

   export HIVE_MODEL_CODE="bailian/qwen3-coder-plus"
   export HIVE_MODEL_WEB="bailian/qwen3-max-2026-01-23"
   export HIVE_MODEL_CHAT="bailian/qwen3.5-plus"
   

---

Configuration

Prerequisites

• OpenClaw CLI installed
• Bash shell available
• Models configured (adjust for your provider):
  • Code model (e.g., bailian/qwen3-coder-plus)
  • Web model (e.g., bailian/qwen3-max-2026-01-23)
  • Chat model (e.g., bailian/qwen3.5-plus)

Environment Variables

Optional environment variables for customization:

# Model overrides (required)
export HIVE_MODEL_CODE="bailian/qwen3-coder-plus"
export HIVE_MODEL_WEB="bailian/qwen3-max-2026-01-23"
export HIVE_MODEL_CHAT="bailian/qwen3.5-plus"
export HIVE_MODEL_DOC="bailian/qwen3.5-plus"
export HIVE_MODEL_DATA="bailian/qwen3-coder-plus"

# Optional: custom session IDs
export HIVE_SESSION_CODE="custom:code:session"
export HIVE_SESSION_WEB="custom:web:session"
export HIVE_SESSION_CHAT="custom:chat:session"

# Optional: concurrency limit
export HIVE_MAX_CONCURRENT=10

Verify Setup

# Check models
openclaw models list | grep bailian

# Test router script
bash router.sh "测试任务"

# Verify environment variables
echo $HIVE_MODEL_CODE
echo $HIVE_MODEL_WEB

---

Troubleshooting

Issue 1: Router script not found

# Make sure script is executable
chmod +x router.sh

# Run with full path
bash /path/to/router.sh "task"

Issue 2: Model not available

# Check available models
openclaw models list

# Update environment variables with available models
export HIVE_MODEL_CODE="bailian/qwen3-coder-plus"

Issue 3: Task type misidentified

# Add more specific keywords to router.sh
# Edit CODE_KEYWORDS, WEB_KEYWORDS, etc.

Issue 4: Wrong model used

# Verify environment variables are set
echo $HIVE_MODEL_CODE
echo $HIVE_MODEL_WEB

# Set them explicitly before running router.sh
export HIVE_MODEL_CODE="bailian/qwen3-coder-plus"
bash router.sh "task"

---

Best Practices

1. Task Distribution Principles

   • ✅ Short tasks → Main session
   • ✅ Long tasks → Subagent
   • ✅ Batch tasks → Multiple parallel subagents
   • ✅ Fixed types → Session isolation + specialized models

2. Model Selection

   • Use code-specialized models for programming tasks
   • Use reasoning models for research tasks
   • Use cost-effective models for chat tasks
   • Mix providers for best-in-class results

3. Concurrency Control

   • Recommended max concurrent subagents: 5-10
   • For 10+ tasks → Execute in batches
   • Monitor API quota usage

4. Environment Management

   • Set environment variables in .bashrc or .zshrc
   • Use different configs for different projects
   • Document your model choices

---

Performance

Metric	Traditional	Hive Router	Improvement
3 project research	~180s	~60s	3x ⚡
Model utilization	Single model	Multi-model	Flexible
Task routing	Manual	Automatic	Intelligent
Multi-provider	Manual switching	Auto config	Seamless

---

Provider Compatibility

Provider	Status	Notes
Bailian (通义千问)	✅ Tested	Default configuration
OpenAI (GPT)	✅ Compatible	Set HIVE_MODEL_* variables
Anthropic (Claude)	✅ Compatible	Set HIVE_MODEL_* variables
Google (Gemini)	✅ Compatible	Set HIVE_MODEL_* variables
Other OpenAI-compatible	✅ Compatible	Use provider/ prefix

---

License

MIT License - Feel free to use and modify.

---

Author: qiongcao
Version: 1.0.0
Last Updated: 2026-03-12
Universal Model Support: Yes