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.
Scanned 9/6/2026
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---
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):**
```bash
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):**
```bash
# 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:**
```bash
# 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:**
```bash
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):**
```bash
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:**
```bash
# 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`:
```markdown
## 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.
```bash
# 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
```bash
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
```bash
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)
```bash
openclaw agent \
--session-id agent:main:chat \
--model bailian/qwen3.5-plus \
--message "今天有什么安排"
```
#### Data Processing Tasks
```bash
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
```bash
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:
```bash
# 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:**
```bash
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:**
```bash
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:**
```bash
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:**
```bash
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:**
```bash
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:**
```bash
# 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
```bash
clawhub install qiongcao/hive-task-router
```
### Manual Installation
1. Clone or download this skill folder
2. Copy to your OpenClaw skills directory:
```bash
cp -r hive-task-router ~/.openclaw/workspace/skills/
```
3. Make router script executable:
```bash
chmod +x ~/.openclaw/workspace/skills/hive-task-router/router.sh
```
4. Configure models for your provider:
```bash
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:
```bash
# 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
```bash
# 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
```bash
# Make sure script is executable
chmod +x router.sh
# Run with full path
bash /path/to/router.sh "task"
```
### Issue 2: Model not available
```bash
# 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
```bash
# Add more specific keywords to router.sh
# Edit CODE_KEYWORDS, WEB_KEYWORDS, etc.
```
### Issue 4: Wrong model used
```bash
# 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*
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