Stats
Actions
Available In
Tags
Plugin
machine-learning-ops
Audit ML pipelines for data leakage, reproducibility, and evaluation rigor. Debug training failures, enforce deterministic runs, and generate stakeholder-ready evaluation reports with confusion matrices and segment analysis.
What's Inside
Slash Commands4
Evaluation Report Command
/evaluation-report
Generate a comprehensive evaluation report for a trained model, highlighting performance, baselines, and subgroup metrics.
Experiment Preflight Command
/experiment-preflight
Audit an ML training script or pipeline for reproducibility, tracking, and basic hygiene before a long run.
Leakage Check Command
/leakage-check
Audit data preprocessing and splitting logic for target leakage.
ml-run-review
/ml-run-review
Review a machine learning training run or experiment configuration for soundness, reproducibility, and evaluation rigor.
Agents4
leakage-auditor
/leakage-auditor
Specialized agent for detecting data leakage, train-test contamination, and temporal leakage in ML pipelines.
ml-experiment-reviewer
/ml-experiment-reviewer
Evaluates ML experiment setups for rigorous baseline comparisons, proper metric tracking, and reproducibility.
Model Evaluation Analyst
/model-evaluation-analyst
Audits evaluation code to ensure test sets are clean, metrics are appropriate for the problem, and confidence intervals are computed.
training-pipeline-debugger
/training-pipeline-debugger
Diagnoses and resolves training loop bottlenecks, memory leaks, vanishing gradients, and hardware underutilization.
Skills7
baseline-comparison-review
/baseline-comparison-review
Use when a new or more complex ML model is proposed and its value over simple baselines is not yet demonstrated -- before approving a new architecture or replacing an existing heuristic.
dataset-split-review
/dataset-split-review
Use when reviewing how data is split into train/validation/test sets -- especially with time-series data, repeated entities (users, patients, sessions), or imbalanced targets.
experiment-tracking-review
/experiment-tracking-review
Use when reviewing ML training code to confirm a run could be reconstructed later -- hyperparameters, metrics, data references, and artifacts logged, not just printed to stdout.
ml-leakage-check
/ml-leakage-check
Use when reviewing ML preprocessing or feature pipelines for target leakage -- validation metrics look suspiciously good, transformers are fitted before splitting, or features may not exist at prediction time.
model-evaluation-reporting
/model-evaluation-reporting
Use when writing or reviewing a model evaluation report -- accuracy is being quoted alone, or stakeholders need failure modes, segment performance, and business trade-offs made explicit.
Stats
Version0.6.3
Released
LanguageTypeScript
Stars6
Forks2
MaintenanceExcellent
LicenseApache-2.0
Last Commit
Added
'%20stop-opacity%3D'0.16'%2F%3E%3Cstop%20offset%3D'1'%20stop-color%3D'rgb(200%2C90%2C60)'%20stop-opacity%3D'0.03'%2F%3E%3C%2FlinearGradient%3E%3C%2Fdefs%3E%3Crect%20width%3D'320'%20height%3D'200'%20fill%3D'url(%23g)'%2F%3E%3Ccircle%20cx%3D'250'%20cy%3D'56'%20r%3D'92'%20fill%3D'rgb(200%2C90%2C60)'%20fill-opacity%3D'0.06'%2F%3E%3Ccircle%20cx%3D'64'%20cy%3D'172'%20r%3D'58'%20fill%3D'rgb(200%2C90%2C60)'%20fill-opacity%3D'0.05'%2F%3E%3C%2Fsvg%3E)
Available In
Safety Signals
Caution
Uses power tools
Uses Bash, Write, or Edit tools
