菜单
AI-powered resource optimization skill with intelligent resource allocation, ML-based scaling recommendations, and predictive resource management. Use when optimizing cloud resource utilization with advanced AI capabilities for cost reduction and performance improvement.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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Advanced deep learning automation for complex infrastructure pattern recognition, intelligent optimization, and self-healing systems. Use when implementing sophisticated AI capabilities with neural networks, deep learning models, and advanced pattern recognition.
| name | optimize-resources |
| description | AI-powered resource optimization skill with intelligent resource allocation, ML-based scaling recommendations, and predictive resource management. Use when optimizing cloud resource utilization with advanced AI capabilities for cost reduction and performance improvement. |
| license | AGPLv3 |
| metadata | {"author":"agentic-reconciliation-engine","version":"2.0","category":"enterprise","risk_level":"low","autonomy":"conditional","layer":"temporal"} |
| compatibility | Requires Python 3.8+, cloud provider CLI tools (AWS CLI, Azure CLI, gcloud), and access to multi-cloud monitoring systems |
| allowed-tools | Bash Read Write Grep |
Enterprise-grade automation solution for resource optimization operations across AWS, Azure, GCP, and on-premise environments with advanced AI capabilities including intelligent resource allocation, ML-based scaling recommendations, and predictive resource management to maximize cost efficiency while maintaining performance and reliability standards.
aws|azure|gcp|onprem|all (optional, default: all)production)true)false)core/scripts/automation/resource-optimizer.py: Main AI-powered optimization implementationcore/scripts/automation/resource_optimizer_handler.py: Cloud-specific operationscore/scripts/automation/multi_cloud_orchestrator.py: Cross-provider coordinationresource, optimization, ai, ml, predictive, allocation, scaling, intelligent, cost, efficiency, performance, multi-cloud, aws, azure, gcp, onprem