RAN DSPy Mobility Optimizer
DSPy-based mobility optimization with temporal patterns, handover management, and 15% improvement target. Uses program synthesis and LLM reasoning for proactive mobility optimization and intelligent handover decision-making.
npx skills add majiayu000/claude-skill-registry --skill ran-dspy-mobility-optimizer-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 mobility optimization using DSPy (Dynamic Synthesis for Python) with temporal pattern analysis and proactive handover management. Combines program synthesis, LLM reasoning, and AgentDB memory patterns to achieve 15% mobility optimization improvement and 20% reduction in handover failures. Uses temporal reasoning to predict user movement patterns and optimize handover decisions in real-time.
Performance: <500ms mobility decisions, 95% handover prediction accuracy, 15% mobility improvement with DSPy program synthesis.
How it works
- Initializes a DSPy mobility environment and AgentDB database to store and retrieve mobility patterns.
- Creates DSPy programs for mobility optimization and handover prediction using a chain-of-thought and ReAct reasoning pattern.
- During optimization, loads similar mobility patterns from AgentDB via embedding-based retrieval, then synthesizes an optimization strategy with DSPy using current state, historical patterns, and a target of 15% improvement.
- Parses DSPy output into a MobilityOptimization structure, extracting: recommendedAction, handoverDecision (targetCell, executionTiming, expectedBenefits, risks), parameterAdjustments (e.g., tx_power, beamwidth, thresholds, delays), expectedImprovement, confidence, reasoning, and temporalConsiderations.
- Derives actionable details from the DSPy result, such as which action to take (early_handover, delay_handover, adjust_beamforming, etc.), and identifies handover timing, benefits, and risks.
- Stores optimization results in AgentDB, embedding the full record for future retrieval and learning.
When to use it
- When real-time mobility optimization is needed with an emphasis on proactive handover decisions and temporal pattern analysis.
- When you want to target a specific improvement metric (e.g., 15% improvement) and reduce handover failures using DSPy-based synthesis.
What it can touch
- Tools: claude-code
- Files touched: initializes and uses AgentDB, DSPy, and mobility-related data structures; interacts with an embedding model via computeEmbedding; stores patterns via AgentDB insert.
Caveats
- Requires Node.js 18+, AgentDB v1.0.7+, understanding of DSPy concepts, and RAN mobility knowledge.
- Success and accuracy depend on DSPy reasoning output quality and pattern data in AgentDB.
- License noted as MIT in repository metadata.
# RAN DSPy Mobility Optimizer ## What This Skill Does Advanced mobility optimization using DSPy (Dynamic Synthesis for Python) with temporal pattern analysis and proactive handover management. Combines program synthesis, LLM reasoning, and AgentDB memory patterns to achieve 15% mobility optimization improvement and 20% reduction in handover failures. Uses temporal reasoning to predict user movement patterns and optimize handover decisions in real-time. **Performance**: <500ms mobility decisions, 95% handover prediction accuracy, 15% mobility improvement with DSPy program synthesis. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow) - Understanding of DSPy concepts (program synthesis, chain of thought, tool augmentation) - RAN mobility management knowledge (handover procedures, mobility robustness optimization) - Temporal pattern analysis and time series forecasting --- ## Progressive Disclosure Architecture ### Level 1: Foundation (Getting Started) #### 1.1 Initialize DSPy Mobility Environment ```bash # Create RAN DSPy mobility workspace mkdir -p ran-dspy-mobility/{programs,patterns,models,experiments} cd ran-dspy-mobility # Initialize AgentDB for mobility patterns
- What This Skill Does
- Prerequisites
- Progressive Disclosure Architecture
- Level 1: Foundation (Getting Started)
- Level 2: Advanced DSPy Program Synthesis (Intermediate)
- Level 3: Production DSPy Mobility System (Advanced)
- Usage Examples
- Basic DSPy Mobility Optimization
- Temporal Pattern Learning
- Handover Prediction
- Environment Configuration
- Troubleshooting
- Issue: DSPy program execution timeout
- Issue: Low prediction accuracy
Create RAN DSPy mobility workspace
mkdir -p ran-dspy-mobility/{programs,patterns,models,experiments}
cd ran-dspy-mobility
Initialize AgentDB for mobility patterns
npx agentdb@latest init ./.agentdb/ran-dspy-mobility.db --dimension 1536
Install DSPy and mobility packages
npm init -y
npm install agentdb
npm install dspy-ai
npm install @tensorflow/tfjs-nodeWhat does the RAN DSPy Mobility Optimizer skill do?
DSPy-based mobility optimization with temporal patterns, handover management, and 15% improvement target. Uses program synthesis and LLM reasoning for proactive mobility optimization and intelligent handover decision-making.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ran-dspy-mobility-optimizer-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.