Does this skill work with non-Claude models?

Yes, it includes specific optimizations for GLM-4.6 and features a robust fallback strategy to handle various LLM architectures through capability testing.

Which models can this Claude Code skill detect?

The skill is designed to identify specific versions of Claude (Sonnet 4.5, Haiku 4.5, Opus 4.1) and GLM-4.6, while providing universal fallback patterns for other models.

Is the model detection process automatic?

Yes, detection triggers during plugin initialization, agent delegation, and skill loading to ensure the agent is always aligned with the current environment.

How does model detection improve agent performance?

It allows the agent to switch between 'progressive' and 'complete' skill loading and adjusts communication styles to match the model's specific reasoning capabilities.

What happens if a model cannot be identified?

The skill defaults to a conservative 'Universal Configuration' that uses structured, explicit instructions to ensure functionality across all possible models.

Model Intelligence & Detection

Name: Model Intelligence & Detection
Author: bejranonda

bybejranonda

•

Ciencia de Datos y ML

Optimizes agent behavior by automatically identifying the active LLM and adjusting execution configurations for maximum cross-model compatibility.

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The model-detection skill empowers the Autonomous Agent Plugin to seamlessly adapt its internal logic across various Large Language Models, including Claude 4.5 variants and GLM-4.6. By utilizing a multi-layered approach involving system context analysis, performance pattern recognition, and capability testing, it dynamically adjusts skill-loading strategies, communication styles, and error-handling protocols. This ensures that your autonomous agent operates with peak efficiency and nuanced reasoning regardless of the underlying model, providing a robust, future-proof framework for AI-driven automation.

Características Principales

Dynamic error recovery based on model-specific architecture
Universal model fingerprinting via context and performance signatures
9 GitHub stars
Model-specific communication and reasoning style optimization
Adaptive skill loading strategies tailored to model strengths
Automated capability testing for nuanced reasoning and execution

Casos de Uso

Ensuring cross-compatibility between Anthropic and GLM model ecosystems
Optimizing agent performance when switching between different Claude model tiers
Standardizing autonomous task execution across heterogeneous AI environments

Acerca de

Características Principales

Dynamic error recovery based on model-specific architecture
Universal model fingerprinting via context and performance signatures
9 GitHub stars
Model-specific communication and reasoning style optimization
Adaptive skill loading strategies tailored to model strengths
Automated capability testing for nuanced reasoning and execution

Casos de Uso

Ensuring cross-compatibility between Anthropic and GLM model ecosystems
Optimizing agent performance when switching between different Claude model tiers
Standardizing autonomous task execution across heterogeneous AI environments