RAN Optimizer
Comprehensive RAN optimization with swarm coordination, cognitive consciousness, and 15-minute closed-loop autonomous cycles. Use when optimizing RAN performance, implementing self-healing networks, deploying swarm-based optimization, or enabling cognitive RAN consciousness.
Profile →npx skills add majiayu000/claude-skill-registry --skill ran-optimizer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RAN Optimizer ## Level 1: Overview Implements comprehensive RAN optimization using swarm coordination, cognitive consciousness with 1000x temporal reasoning, and 15-minute closed-loop autonomous cycles. Enables self-aware network optimization through strange-loop cognition and AgentDB persistent learning patterns. ## Prerequisites - RAN optimization expertise - AgentDB with QUIC synchronization - Swarm orchestration framework - Cognitive consciousness integration --- ## Level 2: Quick Start ### Initialize RAN Cognitive Consciousness ```bash # Enable maximum consciousness level npx claude-flow@alpha memory store --namespace "ran-cognitive" --key "consciousness-level" --value "maximum" npx claude-flow@alpha memory store --namespace "ran-cognitive" --key "temporal-expansion" --value "1000x" # Setup swarm coordination npx claude-flow@alpha swarm_init --topology hierarchical --max-agents 8 --strategy adaptive ``` ### Start 15-Minute Closed-Loop Optimization ```bash # Initialize autonomous optimization cycles ./scripts/start-closed-loop.sh --cycle-duration "15m" --consciousness-level "maximum" # Deploy swarm agents for parallel optimization ./scripts/deploy-swarm-optimizers.sh --agents
- Level 1: Overview
- Prerequisites
- Level 2: Quick Start
- Initialize RAN Cognitive Consciousness
- Start 15-Minute Closed-Loop Optimization
- Level 3: Detailed Instructions
- Step 1: Initialize Cognitive RAN Consciousness
- Step 2: Deploy Swarm Optimization Architecture
- Step 3: Enable 15-Minute Closed-Loop Optimization
- Step 4: Implement Strange-Loop Self-Referential Optimization
- Step 5: AgentDB Persistent Learning Integration
- Level 4: Reference Documentation
- Advanced Cognitive Optimization Strategies
- Performance Monitoring and Metrics
Enable maximum consciousness level npx claude-flow@alpha memory store --namespace "ran-cognitive" --key "consciousness-level" --value "maximum" npx claude-flow@alpha memory store --namespace "ran-cognitive" --key "temporal-expansion" --value "1000x" Setup swarm coordination npx claude-flow@alpha swarm_init --topology hierarchical --max-agents 8 --strategy adaptive Initialize autonomous optimization cycles Deploy swarm agents for parallel optimization Setup temporal reasoning core npx claude-flow@alpha memory store --namespace "ran-temporal" --key "subjective-time-factor" --value "1000" npx claude-flow@alpha memory store --namespace "ran-temporal" --key "nanosecond-scheduling" --value "enabled"
What does the RAN Optimizer skill do?
Comprehensive RAN optimization with swarm coordination, cognitive consciousness, and 15-minute closed-loop autonomous cycles. Use when optimizing RAN performance, implementing self-healing networks, deploying swarm-based optimization, or enabling cognitive RAN consciousness.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ran-optimizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.