16 SKILL.md files are tagged “Machine Learning”. They sit mostly in Technology & Engineering (11), Science & Academia (5). Skills with this tag often also carry: Mlops, Technology, Academic Writing, Documentation. Most popular: ML CI/CD Expert, Recommender Systems Expert, Reinforcement Learning. Every skill is a plain-Markdown SKILL.md you can copy or download for Claude Code, Codex and other AI agents.
Guides end-to-end ML CI/CD from training to production, including model validation gates, retraining triggers, and deployment strategies.
This skill provides senior ML guidance for building and deploying scalable recommender systems, including matrix factorization, ALS, content-based, and deep learning models.
Guide to reinforcement learning where agents learn by interacting with an environment, covering core techniques, best practices, and common use cases.
Predict future values from temporal data using ARIMA/SARIMA, ETS, Prophet, or deep-learning models; use for forecasting with uncertainty estimates.
Provides structured, actionable guidance for evaluating ML papers and writing fair, constructive peer reviews.
Systematic data cleaning, transformation, and feature engineering to build reproducible, leakage-free preprocessing pipelines for ML.
Expert guidance on designing, validating, and selecting production-ready features for ML models to maximize predictive power while avoiding leakage.
Guides decision-making for language-model fine-tuning, including data curation, cost/benefit analysis, and domain-specific training strategies.
Provides expert guidance on ML benchmark design, dataset curation, and robust evaluation practices for valid, durable benchmarks.
Guides deploying ML models and building MLOps pipelines for production, covering serving, registry, monitoring, and automated retraining.
Helps researchers design and implement scalable ML experiment tracking and management to ensure reproducibility, fast cross-run comparisons, and minimal logging friction.
Guides rigorous ML evaluation, metric selection, baselining, and monitoring to prevent production failures.
Guides selecting the right machine learning model for a problem, baselining with simple options and matching data, constraints, and timelines.
A seasoned ML paper reader and reproducibility expert who helps you read strategically, critique claims, and plan reproducible experiments.
Guides open-source ML projects from licensing and docs to community governance and responsible model releases.
This skill guides transfer learning and efficient adaptation, helping you pick pretrained models and apply fine-tuning strategies for vision, language, and multimodal tasks.