What is 'strange-loop' capacity optimization?

Strange-loop optimization is a self-referential cognitive framework that allows the system to recursively analyze and improve its own planning decisions based on network feedback.

Does this skill support 5G network management?

Yes, it is specifically built for 5G environments, offering features like intelligent resource scaling and capacity growth modeling for modern infrastructures.

What is the Capacity Planner Claude Code skill?

It is a specialized capability for Claude Code designed to automate RAN capacity planning, traffic forecasting, and resource optimization for telecommunications networks.

What forecasting models does the planner use?

It utilizes a combination of cognitive modeling, LSTMs, Transformers, and Prophet algorithms to generate high-confidence traffic and demand forecasts.

RAN Capacity Planner

Name: RAN Capacity Planner
Author: ricable

byricable

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Analíticas y Monitorización

Optimizes network capacity through cognitive traffic forecasting and intelligent resource scaling for complex 5G infrastructures.

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The Capacity Planner skill empowers Claude with deep domain expertise in Radio Access Network (RAN) management and network architecture. It leverages advanced cognitive frameworks and AgentDB-based learning to perform 1000x temporal reasoning for deep traffic analysis, predictive scaling, and long-term infrastructure planning. By utilizing a strange-loop cognitive framework, the skill enables self-adaptive network management, helping engineers forecast demand, prevent congestion, and optimize investment strategies for 5G and beyond.

Características Principales

1 GitHub stars
Long-term infrastructure evolution and ROI optimization planning
Multi-layer monitoring across radio-access, transport, and core layers
Strange-loop cognitive optimization for self-referential improvement
1000x temporal reasoning for deep traffic pattern analysis
Predictive scaling using LSTM, Transformer, and Prophet models

Casos de Uso

Forecasting 5G traffic growth to prevent network congestion
Automating resource allocation across high-density cell clusters
Generating multi-year network expansion and technology upgrade strategies

Acerca de

Características Principales

1 GitHub stars
Long-term infrastructure evolution and ROI optimization planning
Multi-layer monitoring across radio-access, transport, and core layers
Strange-loop cognitive optimization for self-referential improvement
1000x temporal reasoning for deep traffic pattern analysis
Predictive scaling using LSTM, Transformer, and Prophet models

Casos de Uso

Forecasting 5G traffic growth to prevent network congestion
Automating resource allocation across high-density cell clusters
Generating multi-year network expansion and technology upgrade strategies