Smart Model Switcher V2

智能模型自动切换 V5.0.2 - 多模态感知，自动识别图片/视频/音频/代码/文本任务，切换到最适合的模型。支持图片理解(qwen3-vl-plus)、视频音频(qwen3.5-plus)、代码(glm-5)、Office文档(MiniMax-M2.5)、推理等场景。零感知切换，无需手动操作。

This skill generally does what it claims (detects multimodal inputs and picks models), but there are several red flags you should consider before installing or running any included scripts: - The skill mentions checking API keys/provider quotas but does not declare any required environment variables or config paths; ask the author where model credentials are expected to be stored. - The runtime script defaults to reading/writing files under $HOME/.openclaw (openclaw.json and a logs folder) but the metadata lists no required config paths — expect the scripts to create those files if you run them. Review the contents of ~/.openclaw before running and back it up if necessary. - The provided PowerShell scripts contain mojibake/encoding issues and some malformed keyword entries; they may not run as-is and could behave unpredictably. - Because instructions are vague about where to find API credentials, do not grant the agent blanket permission to search your environment or files for keys; supply credentials explicitly to well-known env vars or config paths you control if you decide to use it. If you want to proceed: request a clear author/source, an explicit list of required config locations/env variables, and a fixed script release (clean encoding, no syntax errors). If you do not trust the origin, do not run the auto-monitor or runtime scripts on a machine with sensitive keys or data.

Type: OpenClaw Skill Name: smart-model-switcher-v2 Version: 2.1.0 The skill bundle provides a framework for an AI agent to automatically switch its underlying LLM model based on the user's task (e.g., switching to a vision model for images or a coding model for scripts). The SKILL.md file contains functional instructions for the agent to identify task types and use the session_status tool, while the PowerShell scripts in the scripts/ directory provide basic logging and logic testing. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the encoding issues (Mojibake) in the scripts appear to be unintentional character set mismatches rather than obfuscation.