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Auto Model Switcher

Name: Auto Model Switcher
Author: mrcuo

作者 mrcuo · GitHub ↗ · v1.1.0 · MIT-0

cross-platform ✓ 安全检测通过

198

总下载

当前安装

版本数

在 OpenClaw 中安装

/install auto-model-switcher

功能描述

Automatically selects the best model based on task type and requirements. Use when: (1) Task requires specific capabilities (coding, analysis, multimodal, wr...

使用说明 (SKILL.md)

Auto Model Switcher

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

Primary model selection based on task type
If primary model fails, fallback to qwen3.5-plus
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

安全使用建议

This skill appears to do what it says: automatically pick a model from your pre-configured list. Before installing: (1) confirm your openclaw.json lists only provider models you trust, (2) verify where model-selection logs are written or sent (local vs remote) and whether that meets your privacy needs, (3) ensure the agent already has the provider credentials/configuration needed to check model availability, and (4) test the skill in a limited environment to confirm it preserves session context and respects manual overrides.

功能分析

Type: OpenClaw Skill Name: auto-model-switcher Version: 1.1.0 The skill bundle contains only metadata and instructional markdown (SKILL.md) designed to guide an AI agent in selecting appropriate LLM models based on task types (e.g., coding, analysis, research). There is no executable code, no network activity, and no evidence of malicious prompt injection or data exfiltration logic.

能力评估

✓ Purpose & Capability

Name and description match the instructions: the SKILL.md contains model lists, selection logic by task type, fallback rules, manual overrides, and notes to validate against openclaw.json. No unrelated env vars, binaries, or installs are requested.

ℹ Instruction Scope

Instructions remain within the stated purpose (detect task type, pick a configured model, preserve session). Two ambiguous items: (1) 'Log model selections for learning and optimization' does not specify where logs are stored or sent; (2) 'Always check if target model is available' implies reading openclaw.json or querying provider status — that may require the agent to access config or provider endpoints that are outside the skill text but are reasonable operational requirements.

✓ Install Mechanism

Instruction-only skill with no install spec and no code files — minimal install risk because nothing is fetched or written by the skill itself.

✓ Credentials

No environment variables, credentials, or config paths are required by the skill. The SKILL.md explicitly restricts switching to pre-configured models in openclaw.json, which is proportionate. Note: actual model availability checks or provider interactions will rely on existing agent configuration and credentials outside this skill.

✓ Persistence & Privilege

The skill is not always-enabled and does not request elevated persistence. There is no instruction to modify other skills or system-wide agent settings. Autonomous invocation is allowed by platform default but is not combined with other high-risk flags.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install auto-model-switcher
安装完成后，直接呼叫该 Skill 的名称或使用 /auto-model-switcher 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.1.0

Add support for writing and research tasks

v1.0.0

Initial release of auto-model-switcher. - Automatically selects the most suitable model based on task type (coding, analysis, multimodal, context length). - Optimizes for performance and cost without manual intervention. - Supports fallback and manual override for flexibility and reliability. - Ensures safe switching by only allowing pre-configured models. - Tracks key performance metrics for ongoing improvement.

元数据

Slug auto-model-switcher

版本 1.1.0

许可证 MIT-0

累计安装 2

当前安装数 2

历史版本数 2

常见问题