Machine learning

Convert `make demo/img` statusline snapshots into ANSI-stripped plain text for diffing and PR embedding.

SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention.

Integrates local AI capabilities into applications using Embeddable Lemonade.

Orchestrates modular PyTorch profiler trace analysis with TraceLens: generates perf reports, prepares category data, runs system-level and compute-kernel…

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology.

Agent Platform Model Tuning.

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a…

Use when building or maintaining a self-contained personal knowledge base (an LLM wiki) in plain markdown.

Builds generative AI applications on Amazon Bedrock.

Selects, deploys, and customizes AI models on Amazon SageMaker.

Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).

获取 Hugging Face 上的模型、数据集和 Space 的统计信息

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.

Delegate work to the TaskMarket agent-worker market - browse open tasks, create tasks with explicit authorization, and track/submit work, so an agent can…

Iteratively optimize expensive black-box objectives — such as materials properties, experimental yields, or simulation outputs — by learning from past…

Quantify prediction uncertainty of MACE MLIPs using committee (ensemble) models; flag high-uncertainty structures for DFT verification.

Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.

Benchmark MLIP accuracy against a labeled dataset — compute MAE/RMSE for energy/atom and forces, and generate parity plots.

Optional experimental GPU-accelerated batched inference for MACE, MatGL (TensorNet/CHGNet), and FairChem MLIPs using NValchemi, enabling parallel static…

Train a model to predict custom properties of molecules or periodic materials using pretrained SelfConditionedDenoisingAtoms (SCD) foundation models.

Train a property predictor head on top of a Machine Learning Interatomic Potential (MLIP) backbone (MACE or MatGL) to predict custom intensive or extensive…

Unified search skill with Intelligent Auto-Routing.

train a Cluster Expansion (CE) for lattice-based Monte Carlo simulation of disordered materials.

Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.