How does this skill handle task complexity?

It uses scaling factors and complexity handling multipliers to adjust execution time and resource allocation dynamically based on the specific task requirements.

Does it support real-time performance adjustments?

Yes, the skill includes adaptive algorithms that monitor execution metrics in real-time and automatically tune parameters like timeouts and memory usage.

Which models are supported by the performance-scaling skill?

The skill includes specialized profiles for Claude Sonnet 4.5, Claude Haiku 4.5, Claude Opus 4.1, and GLM-4.6, with fallback configurations for other models.

Can I set different quality targets for different tasks?

Yes, the skill allows you to define quality targets based on task types such as simple, complex, or critical, tailored to the specific model being used.

Performance & Model Scaling

Name: Performance & Model Scaling
Author: bejranonda

bybejranonda

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生产力与工作流

Optimizes autonomous agent performance and resource allocation across multiple LLM models to ensure efficiency and quality.

关于

This skill provides a robust framework for managing the execution characteristics of autonomous agents across different Large Language Models, including Claude 4.5 and GLM series. It offers model-specific performance profiles, dynamic execution time allocation, and adaptive quality targets to ensure agents remain performant under varying workloads. By implementing intelligent memory management and real-time monitoring, the skill allows Claude to self-correct performance deviations and tune parameters automatically, making it ideal for high-scale agentic workflows that require a balance between speed, cost, and accuracy.

主要功能

Resource pooling and intelligent caching for concurrent agent tasks
Dynamic execution time and timeout scaling based on task complexity
Real-time performance monitoring with automatic parameter tuning
Adaptive quality target management for critical and simple tasks
9 GitHub stars
Model-specific performance profiling for Claude Sonnet, Haiku, and Opus

使用场景

Maintaining high-quality outputs while reducing latency in production environments
Optimizing agent execution speed when switching between different Claude models
Scaling resource allocation for complex multi-task autonomous workflows

关于

主要功能

Resource pooling and intelligent caching for concurrent agent tasks
Dynamic execution time and timeout scaling based on task complexity
Real-time performance monitoring with automatic parameter tuning
Adaptive quality target management for critical and simple tasks
9 GitHub stars
Model-specific performance profiling for Claude Sonnet, Haiku, and Opus

使用场景

Maintaining high-quality outputs while reducing latency in production environments
Optimizing agent execution speed when switching between different Claude models
Scaling resource allocation for complex multi-task autonomous workflows