RAN AgentDB Integration Specialist
AgentDB integration specialist for RAN ML systems with vector storage, pattern recognition, and distributed training coordination. Achieves 150x faster search, <1ms QUIC sync, and 32x memory reduction for RAN optimization.
npx skills add majiayu000/claude-skill-registry --skill ran-agentdb-integration-specialist-ricable-ultimate-ai-agent --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.
What This Skill Does
Advanced AgentDB integration designed for Radio Access Network (RAN) ML systems. It enables ultra-fast vector search, sub-millisecond QUIC synchronization, and memory reduction through quantization and pattern consolidation. It supports distributed training coordination, real-time pattern recognition, and persistent memory management across RAN optimization agents, aiming for high uptime in distributed coordination.
How it works
- Initializes a RAN AgentDB workspace and installs necessary packages (AgentDB, TensorFlow web-node, QUIC, vector-search).
- Provides a Basic RAN AgentDB Adapter that can:
- Create an embedding from RAN data via createRANEmbedding, preferring a trained embedding model but falling back to a handcrafted embedding if needed.
- Store patterns with a generated pattern id, including embedding, metadata, and performance metrics, and insert into AgentDB with quantization settings.
- Cache stored patterns for ultra-fast access and compute storage time.
- Retrieve similar RAN patterns by generating a query embedding, checking the cache, and querying AgentDB with domain, k, and several options (MMR, context synthesis, filters, memory optimization).
- Post-process results with RAN-specific logic, including similarity calculations, performance comparisons, and recommendations.
- Maintain structured features for embeddings (throughput, latency, signal metrics, network state, temporal features, and several derived RAN metrics).
- Build filters from RANFilters for AgentDB querying.
- Provide utilities for generating, caching, and formatting results, including a pattern id generator and hashing helpers.
- Include multiple helper methods to compute RAN-specific factors: SINR, channel quality, load balance, mobility complexity, and domain classification.
When to use it
- When you need rapid, vector-based retrieval of RAN-related patterns and patterns’ associated metadata in a distributed RAN ML workflow.
- When you require QUIC-enabled synchronization with low latency (<1ms) and memory reduction suitable for large-scale RAN pattern stores.
- When coordinating distributed training across RAN optimization agents with high uptime requirements (up to 99.9% uptime mentioned).
What it can touch
- Files and code blocks involving: Node.js 18+, AgentDB via agentic-flow, and TypeScript adapters.
- It uses an internal in-memory cache and AgentDB storage for patterns, including embedding data and pattern metadata.
Caveats
- Requires Node.js 18+, AgentDB v1.0.7+ via agentic-flow, and knowledge of vector databases.
- Relies on a handcrafted fallback embedding if embedding model is unavailable; the fallback is not a replacement for a trained model.
- Some features assume RAN-specific data structures and domain knowledge, such as RANData and RANPerformanceMetrics types referenced in code.
# RAN AgentDB Integration Specialist ## What This Skill Does Advanced AgentDB integration specifically designed for Radio Access Network (RAN) ML systems. Provides ultra-fast vector search (150x faster), sub-millisecond QUIC synchronization, and 32x memory reduction through intelligent quantization and pattern consolidation. Enables distributed training coordination, real-time pattern recognition, and persistent memory management across RAN optimization agents. Achieves 99.9% uptime for distributed coordination. **Performance**: <1ms QUIC sync, 150x faster search, 32x memory reduction, 99.9% distributed uptime. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow) - Understanding of vector databases and similarity search - RAN domain knowledge (network parameters, KPIs) - Distributed systems concepts and coordination patterns --- ## Progressive Disclosure Architecture ### Level 1: Foundation (Getting Started) #### 1.1 Initialize RAN AgentDB Integration ```bash # Create RAN AgentDB workspace mkdir -p ran-agentdb/{adapters,coordinators,optimizers,cache} cd ran-agentdb # Initialize AgentDB for RAN systems npx agentdb@latest init ./.agentdb/ran-agentdb.db --dimension 1536
- What This Skill Does
- Prerequisites
- Progressive Disclosure Architecture
- Level 1: Foundation (Getting Started)
- Level 2: Advanced AgentDB Features (Intermediate)
- Level 3: Production-Grade AgentDB System (Advanced)
- Usage Examples
- Basic RAN Pattern Storage
- Ultra-Fast Pattern Search
- Pattern Recognition and Learning
- Distributed Training Coordination
- System Performance Monitoring
- Environment Configuration
- Troubleshooting
Create RAN AgentDB workspace
mkdir -p ran-agentdb/{adapters,coordinators,optimizers,cache}
cd ran-agentdb
Initialize AgentDB for RAN systems
npx agentdb@latest init ./.agentdb/ran-agentdb.db --dimension 1536
Install AgentDB and RAN packages
npm init -y
npm install agentdb @tensorflow/tfjs-node
npm install quic-protocol
npm install vector-searchWhat does the RAN AgentDB Integration Specialist skill do?
AgentDB integration specialist for RAN ML systems with vector storage, pattern recognition, and distributed training coordination. Achieves 150x faster search, <1ms QUIC sync, and 32x memory reduction for RAN optimization.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ran-agentdb-integration-specialist-ricable-ultimate-ai-agent --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.