RAN Causal Inference Specialist
Causal inference and discovery for RAN optimization with Graphical Posterior Causal Models (GPCM), intervention effect prediction, and causal relationship learning. Discovers causal patterns in RAN data and enables intelligent optimization through causal reasoning.
npx skills add majiayu000/claude-skill-registry --skill ran-causal-inference-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 causal inference for Radio Access Network (RAN) optimization using Graphical Posterior Causal Models (GPCM). Discovers causal relationships between network parameters, predicts intervention effects, and enables intelligent optimization through causal reasoning. Claims 95% accuracy in causal relationship identification and 3-5x speed improvement in root cause analysis.
How it works
- Initializes a causal inference environment with a RAN-specific workspace and AgentDB integration.
- Performs basic causal discovery by combining correlation analysis and temporal precedence (Granger-related) to infer causal relationships, then stores discovered relationships with embeddings via computeEmbedding and AgentDB.
- Provides a simple intervention predictor that maps intervention types (increase_power, adjust_beamforming, optimize_handover) to estimated effects on KPIs, and computes a predicted state, confidence, causal path, and expected improvement.
- Level 2 introduces a Graphical Posterior Causal Model (GPCM) framework:
- Defines a directed graph of causal relations among RAN variables (signalStrength, throughput, latency, interference, energyConsumption, etc.).
- Constructs a posterior network for each parent->child relation using a neural network, intending to learn P(child | parent, context).
- Trains posterior networks on RAN observations, generating training pairs for each causal edge and context extraction.
When to use it
- When you need to identify potential causal relationships in RAN data and prioritize them by strength for debugging or optimization.
- When evaluating the likely impact of interventions (e.g., power increase, beamforming adjustments, handover optimization) on key performance indicators.
- When building a GPCM-based model to predict how changes in one parameter affect others and to guide optimization decisions.
What it can touch
- Tools and platforms referenced: AgentDB via agentic-flow, computeEmbedding, and an array of causal inference packages (e.g., causal-graph, bayesian-network) as part of setup.
- Code touches TypeScript/TS elements for RANCausalInference, RANInterventionPredictor, and RANGPCM; uses TensorFlow.js for posterior networks (tfjs-node).
- Interacts with AgentDB to store discovered causal relationships with embedding data and metadata.
Caveats
- The description contains performance claims (e.g., 95% accuracy, <2s inference) that depend on real-world data and validation not provided here.
- Uses simplified/heuristic methods for Granger-like causality and a basic, illustrative neural network setup for GPCM that may require substantial domain validation and tuning.
- Requires prerequisites including Node.js 18+, AgentDB v1.0.7+, understanding of causal inference concepts, RAN domain knowledge, and Bayesian/logical modeling background.
- The GN model and training data generation are shown as scaffolding; real-world effectiveness depends on data quality and training regime.
# RAN Causal Inference Specialist ## What This Skill Does Advanced causal inference specifically designed for Radio Access Network (RAN) optimization using Graphical Posterior Causal Models (GPCM). Discovers causal relationships between network parameters, predicts intervention effects, and enables intelligent optimization through causal reasoning rather than correlation. Achieves 95% accuracy in causal relationship identification and 3-5x improvement in root cause analysis speed. **Performance**: <2s causal inference, 90% intervention prediction accuracy, causal model learning with AgentDB integration. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow) - Understanding of causal inference concepts (do-calculus, confounding, counterfactuals) - RAN domain knowledge (network parameters, KPIs) - Statistical concepts (Bayesian inference, graphical models) --- ## Progressive Disclosure Architecture ### Level 1: Foundation (Getting Started) #### 1.1 Initialize Causal Inference Environment ```bash # Create RAN causal inference workspace mkdir -p ran-causal/{models,data,interventions,results} cd ran-causal # Initialize AgentDB for causal patterns npx agentdb@latest init ./.
- What This Skill Does
- Prerequisites
- Progressive Disclosure Architecture
- Level 1: Foundation (Getting Started)
- Level 2: Graphical Posterior Causal Models (Intermediate)
- Level 3: Production-Grade Causal RAN System (Advanced)
- Usage Examples
- Basic Causal Discovery
- Production RAN Causal Optimization
- Counterfactual Analysis
- Environment Configuration
- Troubleshooting
- Issue: Low causal discovery accuracy
- Issue: Counterfactual predictions unreliable
Create RAN causal inference workspace
mkdir -p ran-causal/{models,data,interventions,results}
cd ran-causal
Initialize AgentDB for causal patterns
npx agentdb@latest init ./.agentdb/ran-causal.db --dimension 1536
Install causal inference packages
npm init -y
npm install agentdb @tensorflow/tfjs-node
npm install causal-graph
npm install bayesian-networkWhat does the RAN Causal Inference Specialist skill do?
Causal inference and discovery for RAN optimization with Graphical Posterior Causal Models (GPCM), intervention effect prediction, and causal relationship learning. Discovers causal patterns in RAN data and enables intelligent optimization through causal reasoning.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ran-causal-inference-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.