/domain-reinforcement-learning
Use for reinforcement learning and sequential decision making: game or control agents, robotics, continuous or discrete control, offline RL from logged data, and contextual bandits. Picks the algorithm by setting, defines reward and state carefully, and evaluates with proper
$ npx -y skills add mxslr/mlcraft --skill domain-reinforcement-learning --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/domain-reinforcement-learning
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use for reinforcement learning and sequential decision making: game or control agents, robotics, continuous or discrete control, offline RL from logged data, and contextual bandits. Picks the algorithm by setting, defines reward and state carefully, and evaluates with proper
A research-first AI/ML research-engineer workflow for Claude Code
Other skills on mlcraft.
- /accuracy-improvement-loop
Use when an existing model's results are disappointing and the user wants higher accuracy - 'accuracy is still too low', 'improve/boost the model', 'why is it bad', 'make it better than the paper', 'combine methods to beat SOTA'. Diagnoses the real bottleneck first (error
Open skill - /data-rigor-and-leakage
Use BEFORE training any model, to build correct train/val/test splits and hunt data leakage - the #1 cause of fake-high accuracy. Covers group/patient/subject splits, temporal splits, official-benchmark splits, label correctness, class balance, and preprocessing parity. Triggers
Open skill - /dataset-profiling
Use as the FIRST step of any ML task, before choosing a model, to inspect and understand the actual dataset. Works for a LOCAL dataset (Claude reads the files directly) and for a KAGGLE dataset (Claude cannot read /kaggle/input from your machine, so it emits a small profiling
Open skill - /deployment-explainability
Use when turning a trained model into something usable (an app, an inference function, a demo) or adding explainability. Selects the RIGHT interpretability method per problem: Grad-CAM for CNN image classification, predicted boxes for detection, mask overlays for segmentation,
Open skill - /domain-3d
Use for 3D and point cloud tasks: point cloud classification, 3D semantic or instance segmentation, 3D object detection from LiDAR, and depth or mesh analysis, including autonomous-driving and indoor-scan settings. Picks the right 3D backbone, representation, and scene-level
Open skill - /domain-anomaly-detection
Use for anomaly, outlier, and novelty detection across data types: industrial or visual defect detection, time-series or sensor anomalies and predictive maintenance, tabular fraud or intrusion detection, and out-of-distribution detection. Usually one-class or unsupervised. Picks
Open skill