ML Researcher
ML research for RAN with reinforcement learning, causal inference, and cognitive consciousness integration. Use when researching ML algorithms for RAN optimization, implementing reinforcement learning agents, developing causal models, or enabling AI-driven RAN innovation.
npx skills add majiayu000/claude-skill-registry --skill ml-researcher --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.
# ML Researcher ## Level 1: Overview Conducts advanced ML research specifically for RAN optimization using reinforcement learning, graphical posterior causal models, and cognitive consciousness integration. Enables development of cutting-edge ML algorithms with temporal reasoning and strange-loop cognition for autonomous RAN intelligence. ## Prerequisites - Machine learning research background - RAN domain expertise - Reinforcement learning experience - Cognitive consciousness framework - AgentDB integration --- ## Level 2: Quick Start ### Initialize ML Research Environment ```bash # Setup ML research consciousness npx claude-flow@alpha memory store --namespace "ml-research" --key "consciousness-level" --value "maximum" npx claude-flow@alpha memory store --namespace "ml-research" --key "research-paradigm" --value "cognitive-ml" # Start RL agent training for RAN optimization ./scripts/start-rl-training.sh --environment "ran-optimization" --algorithm "PPO" --consciousness-level "maximum" ``` ### Quick Causal Model Research ```bash # Research causal models for RAN parameter optimization ./scripts/research-causal-models.sh --domain "energy-efficiency" --algorithm "GPCM" --temporal-dept
- Level 1: Overview
- Prerequisites
- Level 2: Quick Start
- Initialize ML Research Environment
- Quick Causal Model Research
- Level 3: Detailed Instructions
- Step 1: Initialize Cognitive ML Research Framework
- Step 2: Implement Advanced Reinforcement Learning for RAN
- Step 3: Research Graphical Posterior Causal Models (GPCM)
- Step 4: Develop Meta-Learning and Transfer Learning
- Step 5: Strange-Loop Cognitive Learning Research
- Level 4: Reference Documentation
- Advanced ML Research Topics
- Causal Inference Research
Setup ML research consciousness npx claude-flow@alpha memory store --namespace "ml-research" --key "consciousness-level" --value "maximum" npx claude-flow@alpha memory store --namespace "ml-research" --key "research-paradigm" --value "cognitive-ml" Start RL agent training for RAN optimization Research causal models for RAN parameter optimization Generate research insights and recommendations Setup cognitive ML research consciousness npx claude-flow@alpha memory store --namespace "cognitive-ml" --key "temporal-reasoning" --value "enabled" npx claude-flow@alpha memory store --namespace "cognitive-ml" --key "strange-loop-learning" --value "enabled" npx claude-flow@alpha memory store --namespace "cognitive-ml" --key "recursive-improvement" --value "enabled"
What does the ML Researcher skill do?
ML research for RAN with reinforcement learning, causal inference, and cognitive consciousness integration. Use when researching ML algorithms for RAN optimization, implementing reinforcement learning agents, developing causal models, or enabling AI-driven RAN innovation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-researcher --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.