These skills help an agent prepare ML workflows and communicate their results. Members cover Feast feature stores, ML engineering, cluster evaluation, vision tools, Plotly, scientific slides, and forecasting.
12 skills
Guide for working with Feast (Feature Store) — defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines.
Internals of the Feast codebase — how each component works, where the key abstractions live, and the data flow through the system.
How to test and debug Feast — running targeted tests, writing unit tests for new components, debugging registry and online store issues, and inspecting live feature store state.
Forecast univariate time series with Google TimesFM, including uncertainty estimates and optional covariates.
Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference…
Apply practical large-model training and operations guidance, and evaluate GPU cluster performance and readiness.
Evaluates an ML GPU cluster for a cloud trial or acceptance test: environment dump, isolated newest PyTorch, matmul FLOPS (MAMF/MSMF) on every GPU while the others compute, intra-node all-reduce bandwidth and per-call latency of every collective, the same inter-node on every node you were given (omit those sections if there is only one node), fio on local disk and shared FS, dated markdown report.
Use engineering or feature-store entries for pipelines, evaluation skills for model behavior, and visualization or slide entries when results need to be explained.