domino-claude-plugin

Manage model risk governance in Domino using policies, bundles, and evidence. Covers creating governance bundles, attaching model artifacts and MLflow results as evidence, progressing through policy stages, and documenting findings. Use when the user mentions governance, compliance, bundles, policies, model risk management, SR 11-7, NIST AI RMF, or audit trails.

Use the domino-data Python SDK (dominodatalab-data) for programmatic data access in Domino. Covers DataSourceClient for SQL queries and object storage, DatasetClient for dataset files, TrainingSets for ML data versioning, Feature Store, and VectorDB (Pinecone) integration. Use when querying data sources, downloading datasets, managing training sets, or working with vector databases in Domino.

Access external LLM providers through Domino AI Gateway - a secure proxy with centralized API key management, usage monitoring, and compliance. Supports OpenAI, AWS Bedrock, Azure OpenAI, Anthropic, and more. Use when calling LLMs from Domino, configuring AI Gateway endpoints, or monitoring LLM usage and costs.

Build Domino-styled web applications matching the Domino Design System. Use when creating or styling Domino apps (Dash, Streamlit, Flask, FastAPI+HTML), generating UI components, reviewing UX, or when the user mentions "Domino app", "Domino UI", "Domino dashboard", or "Domino design system". Covers the full stack — FastAPI backend, Ant Design 5.x frontend via CDN, Domino theme tokens (colors, typography, spacing), chart styling with Highcharts, Domino API authentication, proxy-safe routing, UX writing, error handling, empty states, form design, table patterns, and layout best practices. Also use for UX reviews of existing Domino app screenshots.

Connect Domino workloads to external data sources including AWS S3 (via Mountpoint CSI driver), credential propagation with AWS IRSA and Azure Entra ID, and External Data Volumes. Use when configuring S3 access, setting up credential propagation, or connecting to cloud data sources from Domino.

Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters. Covers cluster configuration, on-demand clusters, choosing between frameworks, PySpark usage, and scaling workloads. Use when processing large datasets, parallel ML training, or running distributed compute jobs.

Create and customize Domino Compute Environments - Docker containers defining tools, packages, and configurations. Covers Dockerfile customization, package installation, IDE configuration, DSE (Domino Standard Environments), and troubleshooting build failures. Use when installing dependencies, customizing environments, or fixing environment issues.

Work with Domino Datasets - high-performance, versioned filesystem storage. Covers dataset creation, snapshots for versioning, sharing across projects, mounting paths (/domino/datasets/), and performance optimization. Use when managing data storage, creating reproducible data versions, or sharing data between projects.