Deprecated: moved to dominodatalab/domino-ai-plugin
Install
npx skills add dominodatalab/domino-claude-plugingh skill install dominodatalab/domino-claude-plugingit clone --depth 1 https://github.com/dominodatalab/domino-claude-pluginSkills in this collection
- Domino Ai GatewayDomino Data LabAccess 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.
- Domino API IntroDomino Data LabIntroduction 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.
- Domino AppsDomino Data LabDeploy 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.
- Domino Data ConnectivityDomino Data LabConnect 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.
- Domino Data SdkDomino Data LabUse 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.
- Domino DatasetsDomino Data LabWork 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.
- Domino Distributed ComputingDomino Data LabWork 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.
- Domino EnvironmentsDomino Data LabCreate 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.
- Domino Experiment TrackingDomino Data LabTrack 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.
- Domino ExtensionsDomino Data LabBuild 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.
- Domino FlowsDomino Data LabOrchestrate 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.
- Domino Genai TracingDomino Data LabTrace 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.
- Domino GovernanceDomino Data LabManage 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.
- Domino JobsDomino Data LabCreate, 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.
- Domino LaunchersDomino Data LabCreate 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.
- Domino Model EndpointsDomino Data LabDeploy 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.
- Domino Model MonitoringDomino Data LabMonitor 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.
- Domino Model ServingDomino Data LabDeploy, 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.
- Domino Modeling AssistantDomino Data LabEnable 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.
- Domino ProjectsDomino Data LabWork 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.
- Domino Python SdkDomino Data LabProgrammatically interact with Domino using python-domino SDK and REST APIs. Covers authentication, running jobs, managing projects, file operations, model deployment, and automation. Use when automating Domino workflows, integrating with CI/CD, or building custom tooling around Domino.
- Domino UI BootstrapDomino Data LabBootstrap or retrofit a Vite + React 18 + TypeScript project so it uses the Domino design system (`@dominodatalab/extensions-tools`). Scoped to projects that are (or will be) a **fully standalone SPA frontend for a Domino application or extension** — not snippets, library additions, or work inside an existing Domino monorepo package.
- Domino UI DesignDomino Data LabBuild 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".
- Domino WorkspacesDomino Data LabWork with Domino Workspaces - interactive development environments including Jupyter, JupyterLab, VS Code, and RStudio. Covers launching workspaces, configuring hardware tiers, environment selection, volume mounting, SSH access, and package installation. Use when setting up development environments, configuring workspace settings, or troubleshooting IDE issues.
- Netapp VolumesDomino Data LabWork with Domino Volumes for NetApp ONTAP - enterprise-grade, multi-terabyte storage with near-instant snapshots. Covers volume creation, snapshot versioning with commit messages, cross-project sharing, mount paths (/mnt/netapp-volumes/ or /domino/netapp-volumes/), and the NetApp Volumes REST API.
- Tags And PropertiesDomino Data LabManage Domino tags and properties via the Taxonomy API. Covers tags/namespaces (create, list, update, delete; tag entities — project, model, dataset, app, project_template, netapp_volume; query by tag; autocomplete; merge; CSV import/export) AND properties (typed metadata fields — text, number, date, boolean, url, user, organization, select, multi_select — with per-entity values, groups, and…