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Found 34 Skills
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.
Comprehensive MLOps workflows for the complete ML lifecycle - experiment tracking, model registry, deployment patterns, monitoring, A/B testing, and production best practices with MLflow
MLflow, model versioning, experiment tracking, model registry, and production ML systems
Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.
Track which optimization experiment was best. Use when you've run multiple optimization passes, need to compare experiments, want to reproduce past results, need to pick the best prompt configuration, track experiment costs, manage optimization artifacts, decide which optimized program to deploy, or justify your choice to stakeholders. Covers experiment logging, comparison, and promotion to production.
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
Self-directed iterative improvement system for Codex that cycles through modify, verify, retain/discard indefinitely
Create, modify, run, inspect, analyze, and report Python experiments that use liblaf.cherries. Use when Codex needs to work under exp/YYYY/mm/dd/group-name/, write or edit numbered scripts in src/, run them with CHERRIES_NAME and CHERRIES_TAGS, inspect Cherries/Comet logs and generated assets, or write Markdown reports in docs/.