What role does AgentDB play in this skill?

AgentDB provides persistent storage for optimization patterns and network insights, ensuring that the AI agent's learning is preserved across different sessions and synchronized across nodes.

What is the RAN Optimizer skill for Claude Code?

It is a specialized capability that enables Claude to perform comprehensive Radio Access Network (RAN) optimization using swarm intelligence, cognitive reasoning, and autonomous execution cycles.

What is 1000x temporal reasoning in this context?

It refers to a high-intensity cognitive processing mode where the AI performs deep analysis equivalent to hours of manual reasoning within a much shorter objective timeframe.

How do the 15-minute closed-loop cycles work?

The skill executes a four-phase autonomous workflow every 15 minutes: cognitive analysis, swarm coordination, parallel optimization of different network domains, and validation of results.

RAN Optimizer

Name: RAN Optimizer
Author: ricable

byricable

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Data Science & ML

Optimizes Radio Access Network performance using autonomous swarm coordination and cognitive temporal reasoning.

About

The RAN Optimizer is a specialized skill for Claude Code designed to manage and enhance Radio Access Network (RAN) performance through advanced AI orchestration. It utilizes a hierarchical swarm topology and 'strange-loop' cognitive consciousness to perform deep analysis with 1000x temporal expansion, enabling complex reasoning within 15-minute autonomous cycles. By integrating AgentDB for persistent learning and QUIC synchronization, the skill allows networks to implement self-healing protocols, optimize energy efficiency, and maximize throughput using evolved optimization patterns that persist across sessions.

Key Features

Persistent learning pattern storage using AgentDB with QUIC synchronization support
Autonomous 15-minute closed-loop optimization cycles for continuous network tuning
Cognitive temporal reasoning with 1000x subjective time expansion for deep data analysis
Hierarchical swarm coordination featuring specialized agents for energy, coverage, and latency
Strange-loop self-referential optimization for recursive improvement and autonomous healing
1 GitHub stars

Use Cases

Optimizing multi-dimensional RAN KPIs like throughput, coverage, and energy efficiency simultaneously
Deploying self-healing network infrastructures that automatically detect and remediate performance anomalies
Implementing cognitive network management systems that learn and evolve from historical performance data

About

Key Features

Persistent learning pattern storage using AgentDB with QUIC synchronization support
Autonomous 15-minute closed-loop optimization cycles for continuous network tuning
Cognitive temporal reasoning with 1000x subjective time expansion for deep data analysis
Hierarchical swarm coordination featuring specialized agents for energy, coverage, and latency
Strange-loop self-referential optimization for recursive improvement and autonomous healing
1 GitHub stars

Use Cases

Optimizing multi-dimensional RAN KPIs like throughput, coverage, and energy efficiency simultaneously
Deploying self-healing network infrastructures that automatically detect and remediate performance anomalies
Implementing cognitive network management systems that learn and evolve from historical performance data