innotech-poe-api（innotech POE API 搜索）

Poe API Orchestration Skill

Purpose: Enable OpenClaw to intelligently query multiple AI models via Poe API Version: 1.0.0 Last Updated: 2026-03-03

What This Skill Does

This skill provides:

1. Intelligent Model Selection - Automatically choose the right AI model for each task
2. Poe API Integration - Simple interface to query 300+ AI models
3. Task-Based Routing - Route requests to the best model based on task type
4. Cost Optimization - Use appropriate models to balance quality and cost

When OpenClaw Should Use This Skill

Use this skill when you need to:

• ✅ Query AI models for text generation, reasoning, or analysis
• ✅ Generate images, videos, or audio
• ✅ Perform web searches with AI assistance
• ✅ Access specialized models for specific tasks
• ✅ Need to choose between multiple AI models

Do NOT use this skill for:

• Simple string operations (use built-in functions)
• Local file operations
• System commands

Quick Start

1. Read the Model Selection Guide

CRITICAL: Before using this skill, read:

MODEL_SELECTION_GUIDE.md

This guide teaches you:

• Which model to use for each task type
• When to use Claude, GPT, Gemini, or other models
• How to balance quality, speed, and cost

2. Use the Client

from skills.poe_api.scripts.poe_client import PoeClient

# Initialize
client = PoeClient()

# Simple query (auto-selects best model)
result = client.query_for_task(
    task_type="programming",
    message="Write a Python function to sort a list"
)

# Specific model
result = client.query("claude-sonnet-4.6", "Your prompt")

# Web search
result = client.web_search("Latest AI developments")

# Generate image
result = client.generate_image("A sunset over mountains")

# Generate video
result = client.generate_video("A cat playing piano")

Task Types and Model Selection

Text/Reasoning Tasks

Web Search Tasks

Image Generation

Video Generation

Audio Generation

Key Principles

1. Read MODEL_SELECTION_GUIDE.md First

This guide contains:

• Detailed decision trees
• Model capabilities and strengths
• When to use each model
• Cost/quality tradeoffs

2. Default to claude-sonnet-4.6

When in doubt, use claude-sonnet-4.6:

• Best all-around performance
• 983K token context
• Excellent at most tasks
• Good balance of speed and quality

3. Use Task-Based Methods

Instead of manually selecting models, use:

# Automatic model selection
client.query_for_task(task_type="programming", message="...")
client.query_for_task(task_type="ui_design", message="...")
client.query_for_task(task_type="data_analysis", message="...")

4. Consider Context Size

• < 200K tokens: Claude-Sonnet, Claude-Opus, GPT models
• > 200K tokens: Gemini-3.1-Pro (1M context)

5. Balance Quality vs Speed

• Highest Quality: claude-opus-4.6, imagen-4-ultra, veo-3.1
• Balanced: claude-sonnet-4.6, nano-banana-2
• Fast/Cheap: claude-haiku-4.5, perplexity-search

Model Capabilities

Text Models

Claude Family (Anthropic)

• claude-opus-4.6: 983K context, deepest reasoning
• claude-sonnet-4.6: 983K context, best all-around
• claude-haiku-4.5: 192K context, fastest

Strengths:

• Excellent reasoning and coding
• Great at following complex instructions
• Strong safety and reliability
• Very large context windows

GPT Family (OpenAI)

• gpt-5.3-codex: 400K context, code-focused
• gpt-5.2: 400K context, general purpose

Strengths:

• Great at code completion
• Good instruction following
• Large context

Gemini Family (Google)

• gemini-3.1-pro: 1M context, multimodal

Strengths:

• Massive 1M token context
• Multimodal input (text, image, video, audio)
• Great for design systems

Search Models (Perplexity)

• perplexity-search: Simple web search
• perplexity-sonar-pro: Complex queries with citations
• perplexity-sonar-rsn-pro: Reasoning + search

Strengths:

• Real-time web access
• Citations included
• Great for research

Image Models

• imagen-4-ultra: Best quality
• nano-banana-2: Latest, fast, 4K
• gpt-image-1.5: Professional editing

Video Models

• veo-3.1: Best quality + native audio
• sora-2-pro: Cinematic (OpenAI)
• kling-o3: Most versatile (4 workflows)

Audio Models

• elevenlabs-v3: Most realistic speech
• hailuo-music-v1.5: Music generation

Common Use Cases

Programming Tasks

# General coding
result = client.query_for_task(
    task_type="programming",
    message="Write a REST API in Python"
)

# Code review
result = client.query_for_task(
    task_type="programming",
    message=f"Review this code: {code}"
)

# Debugging
result = client.query_for_task(
    task_type="programming",
    message=f"Debug this error: {error}"
)

UI/UX Design

# Design system
result = client.query_for_task(
    task_type="ui_design",
    message="Create a design system for a fintech app"
)

# User research
result = client.query_for_task(
    task_type="ui_design",
    message="Analyze user flow for checkout process"
)

Data Analysis

# Analyze data
result = client.query_for_task(
    task_type="data_analysis",
    message=f"Analyze this dataset: {data}"
)

# Generate insights
result = client.query_for_task(
    task_type="data_analysis",
    message="What trends do you see in this data?"
)

Web Search

# Quick search
result = client.web_search("Latest AI developments 2026")

# Deep research
result = client.deep_search(
    "Impact of AI on job markets",
    model="o3-deep-research"
)

Content Creation

# Generate image
result = client.generate_image(
    "Modern dashboard UI with dark theme"
)

# Generate video
result = client.generate_video(
    "A drone shot of city skyline at sunset"
)

# Generate audio
result = client.generate_audio(
    "[whispers] Welcome to our podcast",
    voice_model="elevenlabs-v3"
)

Decision Framework

Step 1: Identify Task Type

Ask yourself:

• Is this programming? → programming
• Is this design? → ui_design
• Is this analysis? → data_analysis
• Is this search? → web_search
• Is this creative? → image/video/audio

Step 2: Check Context Size

• < 200K tokens: Any Claude/GPT model
• > 200K tokens: Must use gemini-3.1-pro

Step 3: Balance Quality vs Speed

• Need best quality? → Use Pro/Ultra models
• Need fast? → Use Haiku/Flash models
• Need balanced? → Use Sonnet/Standard models

Step 4: Use Task-Based Methods

# Let the skill choose the model
result = client.query_for_task(
    task_type="programming",
    message="Your task",
    complexity="medium"  # low, medium, high
)

Important Notes

Token Limits

• Managed by Poe API - No need to specify
• Different models have different limits
• Poe will automatically handle limits

Cost Management

• Use max_calls_per_task to limit API calls
• Use cheaper models for simple tasks
• Reserve expensive models for complex work

Error Handling

• Always check result["success"]
• Implement retry logic
• Use fallback models if primary fails

Examples

See examples/ directory for:

• Basic usage examples
• Advanced workflows
• Error handling patterns
• Multi-step tasks

Troubleshooting

Model Not Available

Error: Model not found

Solution: Model names are case-sensitive. Use lowercase:

• ✅ claude-sonnet-4.6
• ❌ Claude-Sonnet-4.6

Rate Limited

Error: Rate limit exceeded

Solution: Wait and retry, or use fallback model

Context Too Large

Error: Context exceeds limit

Solution: Use gemini-3.1-pro (1M context)

Next Steps

1. ✅ Read MODEL_SELECTION_GUIDE.md for detailed model information
2. ✅ Check examples/ for usage patterns
3. ✅ Use query_for_task() for automatic model selection
4. ✅ When in doubt, use claude-sonnet-4.6

Remember: The key to using this skill effectively is understanding which model to use for which task. Read MODEL_SELECTION_GUIDE.md carefully! 🎯