584 skills · sorted by relevance
Helps you build similarity search with FAISS to find nearby vectors, numerical representations of data. Covers index selection, saving and loading, GPU use, and tuning search speed, accuracy, and memory consumption for large datasets.
Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.
Find and download public cancer imaging data from NCI Imaging Data Commons. Select CT, MR, PET, or pathology datasets using metadata, view images in a browser, and check licenses and citations before using the data.
Plan and audit HypoGeniC workflows that use language models to propose patterns from labeled text. Check configurations, dataset splits, cost limits, and saved predictions while keeping candidate hypotheses separate from scientific confirmation.
Prepare small image tiles from whole-slide histology scans with Histolab. Inspect tissue regions, choose random, grid, or score-based extraction, standardize H&E staining, and organize tile datasets with patient-level splits and visual checks.
Query public single-cell and spatial gene expression data by tissue, disease, or cell type. Retrieve selected expression values, cell metadata, and summary counts for analysis and comparisons across datasets without first downloading entire collections.
Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery workflows.
Implement .NET workflows for Azure AI Foundry projects, including agent creation, resource connections, dataset uploads, model deployment inspection, search index management, and evaluations. Examples also cover authentication, asynchronous operations, and resource cleanup.
Build TypeScript code for Azure AI Foundry projects using its SDK, a library for accessing project services. Create and run agents, inspect connections and deployed models, and manage datasets and search indexes.
Implement Java workflows for Azure AI Foundry projects using SDK examples for authentication, connected resources, datasets, search indexes, and model evaluations. The skill also covers client setup, paginated resource lists, and error handling.
Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.
Improve slow Apache Spark jobs by reviewing data partitions, caching, memory settings, and shuffles, which move data between workers. Use the guidance to investigate bottlenecks and tune pipelines for larger datasets.
Create shareable links that open specific Arize resources, including traces, datasets, and evaluators. Use IDs from logs or exported data, with suitable time ranges, to direct teammates to the relevant view.
Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.
Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.
Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.
Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.
Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.
Query documents and conversations stored in cognee to retrieve answers, supporting passages, or context for an AI model. This skill guides search method selection, source filtering, result interpretation, and troubleshooting empty or unexpected results.
Analyze single-cell data with scvi-tools, using deep learning to combine datasets, correct differences between experiments, and transfer cell labels. Model guides and reusable scripts cover RNA, protein, chromatin accessibility, spatial data, and downstream analysis.