Machine Learning Claude Skills
1 780 machine learning skills from 282 authors. They train and fine-tune models, build embeddings, run RAG and measure quality. Half of them fit into 2 252 tokens or less — that is what one costs your context window when the agent loads it. 424 ship runnable scripts rather than instructions alone. 10 of them cannot work without an MCP server, most often rube. We also found 397 copies of these same skills sitting in other people's repositories — counted once here, not 397 times.
1 780 unique 282 authors 5 updated this month 189 from vendors
Help users objectively assess where their product stands on the PMF spectrum by triangulating qualitative feedback, quantitative retention benchmarks, and organic growth signals.
Diagnose common DGX Station GB300 issues — CUDA crashes, wrong-GPU targeting, vLLM/SGLang container bugs, MIG state problems, NVLink/Fabric Manager errors, X/Vulkan failures, HuggingFace auth, and port conflicts. Use when the user reports a GPU error, inference server crash, MIG problem, or any unexplained DGX Station failure.
Resolve, tune, preflight, launch, verify, inspect, and stop exact-model inference on NVIDIA DGX Station through dgx-assist. Use for vLLM or SGLang container selection, NGC versus upstream, GPU memory utilization, CPU or KV offload, HBM fit, KV-cache sizing, ISL or context length, prefix caching, chunked prefill, batching, concurrency, performance tuning, serving or deploying a named model, an OpenAI-compatible endpoint, Station recipe models, or an owned inference service. Require an exact model ID for recipe resolution or model-specific tuning, and never recommend or substitute a different model.
Deploy a vLLM inference server on an NVIDIA DGX Station GB300 with validated container, GPU targeting, and tuning parameters. Use when the user asks to serve a model with vLLM, start a vLLM endpoint, or set up OpenAI-compatible inference on DGX Station.
Deploy an SGLang inference server on an NVIDIA DGX Station GB300 with the cu130 container, RadixAttention prefix caching, and structured JSON output support. Use when the user asks to serve a model with SGLang, start an SGLang endpoint, or needs structured-output inference on DGX Station.
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.
Apply approved Xcode build optimization changes following best practices, then re-benchmark to verify improvement. Use when a developer has an approved optimization plan from xcode-build-orchestrator, wants to apply specific build fixes, needs help implementing build setting changes, script phase guards, source-level compilation fixes, or SPM restructuring that was recommended by an analysis skill.
Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.
Prepares and validates GPT-RAG umbrella releases across component repositories and the AI Landing Zone. Use for manifest pins, changelog entries, release branches, tags, and GitHub Release notes.
Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
Iteratively inspect an agent repository and optional user-provided traces, interview the user, and create, run, and audit Harbor evals one at a time. Use for agent evals, Harbor tasks, benchmark cases, verifier design, or controlled agent environments.
INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).
> Create and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. Do NOT use for fetching data with existing sources, model inference, or installation tasks.
> Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.
> Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.
> Find Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.
> Guide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.
>- Adversarially review a diff, patch, or plan for memtier_benchmark using the real review standards of the project's senior maintainers (Yossi Gottlieb / yossigo, Oran Agra / oranagra, Paulo Sousa / paulorsousa). Use when asked to "adversarially review", "review like the maintainers", "find what a reviewer would block on", or before opening/merging a PR. Emits skeptical, evidence- backed findings; assumes a problem is real until it can be refuted.
Find Japanese NLP GitHub repositories and Hugging Face models/datasets that are NOT yet in awesome-japanese-nlp-resources. Suggests candidates to add for a given topic using WebSearch + WebFetch, then outputs contribution-ready markdown.
Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language.
Given a Japanese NLP GitHub repo or Hugging Face model/dataset (URL / owner/repo / tool name), find repositories or models/datasets that do the same or related processing. Mines the bundled dataset for content-similar items, then expands via web research across both GitHub and Hugging Face, then merges and re-ranks.
Iteratively optimizes a cuDF RapidsUDF implementation for GPU performance. Use after testing and benchmarking with udf-benchmark. Runs a loop of profiling, optimizing, testing, and benchmarking until performance converges or the iteration budget is exhausted.
Assists with benchmarking and profiling the performance of an Apache Spark UDF on the GPU. This is step 3 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and a RapidsUDF or SQL implementation, and need to benchmark the performance of the CPU UDF against the GPU implementation.
Update the third_party/kineto submodule in PyTorch to the latest commit from this kineto repo and commit the change. Use when updating the kineto submodule hash for a release.
Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot or tool-calling feature, running an LLM on Apple Silicon, converting or compressing a Python model for Core ML, or comparing on-device inference backends. For Swift Core ML loading and prediction code, use the coreml skill.
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS apps.
Add drawings, shapes, and a consistent markup experience using PaperKit. Use when integrating PaperMarkupViewController for markup editing, adding shape recognition, working with PaperMarkup data models, embedding markup tools in document editors, or building annotation features that need the system-standard markup toolbar. New in iOS 26.
Bridges UIKit and SwiftUI with UIViewRepresentable, UIViewControllerRepresentable, UIHostingController, UIHostingConfiguration, coordinator delegates, and UIKit automatic observation tracking for shared @Observable state. Use when wrapping UIKit-only or third-party UIKit views/controllers in SwiftUI, embedding SwiftUI in UIKit, integrating mail/share/document/PDF/text-view surfaces, or migrating UIKit apps to SwiftUI incrementally.
Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; do not use for plain PyTorch, other frameworks, deployment, POC/production lifecycle, or experiment workflows.
Convert existing PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; do not use for other frameworks, deployment, POC/production lifecycle, or experiment workflows.
Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; do not use for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.
Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.
Route open-ended or ambiguous NVFLARE requests by inspecting the local project and recommending one specific workflow skill without editing files; explicit conversions normally route directly to a converter, except when inspection reports unresolved Trainer ownership or active Lightning and Hugging Face Trainer entrypoints.
> and security policy inspection on the OLTP and SQL analytics endpoints. For schema changes see the sqldb-authoring-cli skill. "sql database in Fabric system view list user tables sqlcmd", "list user tables sqldb", "sys.tables sqldb", "explore sqldb schema", "vector similarity sqldb", "RAG embedding sqldb", "row level security sqldb inspect", "audit log sqldb inspect", "chat with sqldb", "export sqldb rows", "temporal as of sqldb", "json openrowset sqldb".
> Migrate Synapse Data Factory pipeline artifacts to Microsoft Fabric Data Factory. pipeline activities, global parameters → Variable Libraries, SynapseNotebook activities → TridentNotebook. SSIS, SHIR-only, and Databricks activities are parked. (2) converting SynapseNotebook activities to TridentNotebook, (3) translating linked services to Fabric connections, (4) converting global parameters to Fabric Variable Libraries, (5) inlining dataset definitions into Fabric pipeline activities. "tridentnotebook pipeline activity", "global parameters to variable library", "linked service to fabric connection", "inline dataset fabric pipeline", "pipeline migration from synapse".
Address CI failures and unresolved review comments on a Helion pull request. Auto-activate when the user mentions a URL like https://github.com/pytorch/helion/pull/<number>.
Guide users through curating the library knowledge index — reviewing block categories, marking common/important blocks, and improving block descriptions via agent inference for better agent block selection.
> Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
> Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).
> Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
> Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects. Use when importing .pt2 exported programs, traced .pt files, .onnx models, or Keras 3 models via matlabsaver. Covers importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromKeras, importNetworkFromTensorFlow, torch.export.export, PyTorchInputSizes, InputDataFormats, matlabsaver, tf_keras downgrade, numeric validation against PyTorch or ONNX Runtime, and placeholder/custom layer implementation. Applies when user mentions any of these functions, file formats, or encounters import errors, unsupported operator warnings, 0 learnables, or uninitialized networks.
> Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.
> Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. This skill currently documents the PyTorch ExportedProgram (.pt2) workflow via loadPyTorchExportedProgram; LiteRT is already supported by the product (loadLiteRTModel, R2026a+) but detailed guidance has not yet been added to this skill. invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib.