domino-claude-plugin

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.

Introduction to calling Domino platform APIs. Covers authentication (API proxy, PAT, service account), which host to use for which API, pagination limits, and common errors. Use before any other Domino API, SDK, app, extension, or governance automation skill.

Deploy web applications to Domino Data Lab with expertise in React apps (Vite) behind Domino's reverse proxy. Covers app.sh configuration, port configuration, base path handling for SPAs, CI/CD with GitHub Actions, and proxy troubleshooting. Use when deploying apps to Domino, setting up CI/CD pipelines, fixing broken routing, or configuring JavaScript frameworks for Domino's proxy.

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.

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.

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.

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.

Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing model runs, or registering models.

Build and operate Domino UI Extensions (mount Domino Apps in the shell with page context). Covers extension_manifest.json, official installer vs manual Extension API, publish and identity requirements, auth and host splits, and beta REST routes. Use when creating an Extension, wiring mount points, publishing an Extension App from a project, or automating Extension install lifecycle. Not for generic App deployment alone (see domino-apps) or SPA scaffolding (see domino-ui-bootstrap).

Orchestrate multi-step ML workflows using Domino Flows (built on Flyte). Define DAGs with typed inputs/outputs, heterogeneous environments, automatic lineage, and reproducibility. Use when building data pipelines, multi-stage training workflows, or processes requiring orchestration and monitoring.

Trace and evaluate GenAI applications including LLM calls, agents, RAG pipelines, and multi-step AI systems in Domino. Uses the Domino SDK (@add_tracing decorator, DominoRun context) with MLflow 3.2.0. Captures token usage, latency, cost, tool calls, and errors. Supports LLM-as-judge evaluators and custom metrics. Use when building agents, debugging LLM applications, or needing audit trails for GenAI systems.

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.

Create, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Covers job configuration, hardware tiers, scheduled jobs (cron), monitoring status, viewing logs, and API-driven execution. Use when running batch workloads, scheduling recurring tasks, or automating training pipelines.

Create Domino Launchers - parameterized web forms for self-service job execution. Enable business users to run analyses, generate reports, and trigger batch predictions without coding. Covers parameter types, email notifications, result delivery, and access control. Use when building self-service data products or enabling non-technical users.

Deploy and monitor model API endpoints in Domino. Covers creating prediction endpoints, version management, Grafana dashboards for latency/errors/resources, alerting, and GPU inference with NVIDIA Triton. Use when deploying models as APIs, monitoring production endpoints, or debugging endpoint issues.

Enable AI-assisted model development within Domino by writing needed model and training code and using MCP (Model Context Protocol) servers to execute domino jobs. AI coding assistants like Cursor and GitHub Copilot can execute commands as Domino jobs, maintaining security, governance, and reproducibility. Use when setting up AI code assistants to work with Domino, configuring MCP servers, or enabling vibe modeling workflows.

Monitor deployed models in Domino including drift detection, model quality tracking, and alerting. Covers data drift analysis, prediction capture, baseline comparison, alert configuration, and remediation workflows. Use when monitoring production models, detecting drift, or setting up model health alerts.

Deploy, invoke, and retire Domino model APIs and registered models via REST. Covers modelServing lifecycle, registered-models v1 vs v2 paths, MLflow tracking vs registry API, inference URLs vs management API, and GenAI endpoint vanity URLs. Use when automating model deployment, predictions, registry updates, or debugging stop/archive/delete behavior.

Work with Domino Projects including Git integration, DFS vs Git-based projects, collaboration, and version control. Covers project creation, Git provider setup (GitHub, GitLab, Bitbucket), branch management, collaborator permissions, and project settings. Use when creating projects, setting up Git repos, or managing team collaboration.