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! 🎯

Poe Api

Poe API Orchestration Skill

What This Skill Does

When OpenClaw Should Use This Skill

Quick Start

1. Read the Model Selection Guide

2. Use the Client

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

2. Default to claude-sonnet-4.6

3. Use Task-Based Methods

4. Consider Context Size

5. Balance Quality vs Speed

Model Capabilities

Text Models

Claude Family (Anthropic)

GPT Family (OpenAI)

Gemini Family (Google)

Search Models (Perplexity)

Image Models

Video Models

Audio Models

Common Use Cases

Programming Tasks

UI/UX Design

Data Analysis

Web Search

Content Creation

Decision Framework

Step 1: Identify Task Type

Step 2: Check Context Size

Step 3: Balance Quality vs Speed

Step 4: Use Task-Based Methods

Important Notes

Token Limits

Cost Management

Error Handling

Examples

Troubleshooting

Model Not Available

Rate Limited

Context Too Large

Next Steps

Download

Skill Info