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:

Intelligent Model Selection - Automatically choose the right AI model for each task
Poe API Integration - Simple interface to query 300+ AI models
Task-Based Routing - Route requests to the best model based on task type
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

Task Type	Primary Model	When to Use
Programming	claude-sonnet-4.6	General coding, algorithms
Complex Problems	claude-opus-4.6	Deep reasoning, architecture
Fast/Cheap	claude-haiku-4.5	Quick tasks, simple code
Huge Context	gemini-3.1-pro	>200K tokens, design systems
Code-Focused	gpt-5.3-codex	Debugging, code completion
UI/UX Design	gemini-3.1-pro	Design systems, UX research
Requirements	claude-sonnet-4.6	Gathering, analysis
Data Analysis	claude-sonnet-4.6	Data interpretation

Web Search Tasks

Task Type	Model	When to Use
Simple Search	perplexity-search	Quick lookups
Complex Queries	perplexity-sonar-pro	In-depth research
Reasoning + Search	perplexity-sonar-rsn-pro	Analysis with sources
Deep Research	o3-deep-research	Extensive research
Budget Research	o4-mini-deep-research	Cost-conscious

Image Generation

Task Type	Model	When to Use
Best Quality	imagen-4-ultra	Professional graphics
Fast + Good	nano-banana-2	Quick iterations
Text in Images	nano-banana-pro	Banners, signs
General Purpose	nano-banana	Standard generation
Professional Editing	gpt-image-1.5	Complex edits
Asian Aesthetics	seedream-4.0	Specific style

Video Generation

Task Type	Model	When to Use
Best + Audio	veo-3.1	Cinematic with sound
Cinematic	sora-2-pro	High-fidelity
Versatile	kling-o3	Multiple workflows
Standard	sora-2	Good quality
Storytelling	wan-2.6	Multi-scene
Fast	seedance-1.0-pro	Quick generation

Audio Generation

Task Type	Model	When to Use
Realistic Speech	elevenlabs-v3	Audiobooks, podcasts
Fast TTS	gemini-2.5-flash-tts	Quick conversion
Controlled Speech	hailuo-speech-02	Fine-grained control
Music	hailuo-music-v1.5	Song 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

✅ Read MODEL_SELECTION_GUIDE.md for detailed model information
✅ Check examples/ for usage patterns
✅ Use query_for_task() for automatic model selection
✅ 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! 🎯