Auto Model Switcher

Automatically selects the best model based on task type and requirements. Use when: (1) Task requires specific capabilities (coding, analysis, multimodal, writing, research), (2) Need optimal performance/cost balance, (3) Working with long context or complex reasoning.

Intelligently selects the optimal model from available providers based on task characteristics.

When to Use

• Task requires specific capabilities (coding, analysis, multimodal, writing, research)
• Need optimal performance/cost balance
• Working with long context or complex reasoning
• User doesn't specify a model preference

Available Models Analysis

Qwen Series (bailian provider)

Model	Context	Multimodal	Best For	Cost
qwen3.5-plus	1M	✅ Text+Image	General tasks, creative writing, balanced performance	Low
qwen3-max	262K	❌ Text only	Complex reasoning, deep analysis, research	High
qwen3-coder-plus	1M	❌ Text only	Code generation, debugging	Medium

Third-party Models (bailian provider)

Model	Context	Multimodal	Best For
glm-5	1M	✅ Text+Image	Multimodal tasks, Chinese optimization
kimi-k2.5	200K	✅ Text+Image	Multimodal, research-oriented
MiniMax-M2.5	1M	✅ Text+Image	Long context multimodal

Selection Logic

Task Type Detection

Code Tasks → bailian/qwen3-coder-plus

• Keywords: code, programming, debug, fix, implement, develop, coding, script
• File extensions: .py, .js, .ts, .java, .cpp, etc.
• Commands: git, npm, docker, build, compile

Complex Analysis → bailian/qwen3-max

• Keywords: analyze, research, compare, evaluate, strategy, deep dive, business analysis
• Tasks requiring multi-step reasoning
• Financial/strategic analysis

Research Tasks → bailian/qwen3-max

• Keywords: research, investigate, study, survey, academic, literature review
• Complex information synthesis
• Multi-source analysis and comparison

Writing/Copywriting Tasks → bailian/qwen3.5-plus

• Keywords: write, draft, copywriting, content, article, blog, email, proposal, creative
• Marketing copy, social media content
• Creative writing and storytelling

Multimodal Tasks → bailian/glm-5

• Image analysis, OCR, visual understanding
• Audio processing (when supported)
• Mixed text+image inputs

Long Context → bailian/qwen3.5-plus

• Document processing > 200K tokens
• Summarization of large documents
• Historical context analysis

General Tasks → bailian/qwen3.5-plus (default)

• Chat, simple queries, basic tasks
• When no specific requirements detected

Fallback Strategy

1. Primary model selection based on task type
2. If primary model fails, fallback to qwen3.5-plus
3. If still failing, use current session model

Usage Examples

Automatic Selection

User: Help me debug this Python code
→ Model: bailian/qwen3-coder-plus

User: Analyze our Q4 financial performance vs competitors  
→ Model: bailian/qwen3-max

User: Research the latest AI trends in marketing
→ Model: bailian/qwen3-max

User: Write a compelling product description for our new service
→ Model: bailian/qwen3.5-plus

User: What's in this image?
→ Model: bailian/glm-5

User: Summarize this 500-page document
→ Model: bailian/qwen3.5-plus

Manual Override

Users can always specify models directly:

• /model bailian/qwen3-max
• Use coder model for this task

Implementation Notes

• Always check if target model is available before switching
• Preserve current session context when switching
• Log model selections for learning and optimization
• Respect user's explicit model preferences

Security Considerations

• Only switch between pre-configured models in openclaw.json
• Never attempt to use unconfigured or unknown models
• Validate model names against available list before switching

Performance Metrics

Track these metrics for continuous improvement:

• Task completion success rate by model
• Response time by model and task type
• User satisfaction feedback
• Cost per task type

This skill enables intelligent model routing without user intervention while maintaining full control when needed.

Iteration Support

• Skills can be updated via clawhub sync --all
• Version updates maintain backward compatibility
• New task types can be added without breaking existing functionality