domino-experiment-setup

Deliver a Domino model outside a Domino endpoint. Covers batch scoring Jobs (a scoring script run once or on a schedule, results, notifications, the async endpoint alternative), the Model Export API (buildModelImage, exportImageToRegistry, build and export status and logs, .modelignore, running the exported image on your own Kubernetes), and the export targets NVIDIA Fleet Command (exportImageForNvidia) and Snowflake (Snowpark UDF), and SageMaker. Use when the user wants to batch-score a dataset or table with a trained model, run scoring on a schedule, export a model image out of Domino to a container registry from GitHub Actions, Jenkins or another CI/CD pipeline, push a model to NVIDIA Fleet Command or Snowflake, deploy a registered model to a SageMaker external endpoint, or asks about the Exports page. Not for deploying or updating a Domino-hosted endpoint, from CI/CD or otherwise; that belongs to domino-model-serving (REST) and domino-model-endpoints (UI).

Enable AI-assisted model development (vibe modeling) in Domino. The assistant writes the data-analysis, model and training code and uses the bundled Domino MCP server to run it as Domino Jobs, wait for and read their output, and read, upload and sync files (text or binary) for DFS projects, so work stays reproducible, governed and tracked in Experiment Manager. Use when setting up an AI coding assistant to work with Domino, configuring or troubleshooting the Domino MCP server, or when the user asks to analyze a dataset, train or compare models, or run scripts as Domino Jobs from a coding assistant.

Automate Domino endpoints (Model APIs) and registered models over REST. Covers the /api/modelServing/v1 lifecycle (create an endpoint from a file or a registered model, add versions, poll deployment status, list, update replicas or hardware tier, archive, build and instance logs), registered models v1 vs v2 routes, MLflow tracking vs the registry API, management URL vs prediction URL, and sync Basic vs async Bearer prediction auth. Use when scripting deployments from CI, a Job or a notebook, registering a model version by API, or debugging stop, archive and delete behaviour. For UI publishing, the predict function, scaling and health settings use domino-model-endpoints; for drift and quality monitoring use domino-model-monitoring; for exporting a model out of Domino (image to your registry, NVIDIA Fleet Command, Snowflake UDF, SageMaker) or batch scoring Jobs use domino-model-delivery.

Monitor production models in Domino for data drift and model quality. Covers the two workflows (integrated monitoring for Domino endpoints with prediction capture, and Model Monitor for models served elsewhere), the DataCaptureClient prediction-capture library, registering training sets, prediction data and ground truth with the Monitoring Config JSON, drift tests and thresholds, scheduled checks and notifications, where captured predictions live, remediation, and the Model Monitoring REST API including custom metrics. Use when setting up monitoring for an endpoint, registering an external model, ingesting ground truth, reading drift or quality results by API, or automating retraining on drift.

Publish and call Domino endpoints (model APIs) for synchronous or asynchronous predictions. Covers the prediction function and request JSON, how the endpoint image is built (environment, required packages, no requirements.txt), sync Basic-auth vs async Bearer invocation, versions and routing, instance counts and endpoint hardware tiers, health checks, metrics and logs. Use when deploying a model as an API from the UI, writing predict code, calling an endpoint, or debugging a slow or failing endpoint. For scripted lifecycle automation over REST (create, version, archive, logs) use domino-model-serving; for drift and model-quality monitoring use domino-model-monitoring; for batch scoring Jobs and for exporting a model out of Domino (an image to your registry, NVIDIA Fleet Command, a Snowflake UDF, SageMaker) use domino-model-delivery.

Decide and implement how code running in Domino reaches a Large Language Model, LLM Gateway 2.0 first where deployed (routed to domino-llm-gateway); otherwise Option A, an external provider (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Vertex AI) with the API key stored as a Domino environment variable, or Option B, a Domino-hosted model endpoint (vLLM, OpenAI-compatible) created from a Hugging Face model or a registered model and called with an OpenAI-style client and the in-run access token, including the Host an LLM UI and REST API (/api/gen-ai/beta), endpoint metrics and logs. Covers the legacy AI Gateway. Use when a user asks how to call an LLM from a Workspace, Job, App or agent, which LLM path to use, where to put an LLM API key, how to get an endpoint base_url or token, how to host, deploy or serve an LLM with vLLM, or how to use or migrate off the legacy AI Gateway.

Create and run Domino Launchers - self-service web forms that pass text, select, multi-select, date, checkbox and uploaded-file inputs to a script that runs as a Domino Job. Covers the launcher wizard, the command placeholder syntax, how arguments reach the script, result files and email delivery, roles, and finding launcher runs through the Jobs API. Use when building self-service reports, batch scoring forms, or data-product UIs for non-technical users.

Work with Domino Projects, covering Git-based vs Domino File System (DFS) projects, creating projects (UI and API), Git credentials and repositories, file paths in runs, syncing and branches, collaborator roles and visibility, project settings (default environment, hardware tier, environment variables), copying, importing, archiving, goals and status. Use when creating or configuring a project, connecting a Git repository, adding collaborators, finding where code, data and artifacts are mounted, or automating project operations over REST or python-domino.

The HyperFrames CLI by job - scaffold, check, look at frames, edit the timeline, preview, render locally or in the cloud, publish, project history - plus reading its JSON and recovering from build or render failures.

Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar third-party dashboard, or running a one-off migration or cutover. Don't invoke this for steps the agent can perform itself.

This skill should be used when the user wants to "deploy an agent", "deploy my ADK agent", "set up CI/CD", "configure secrets", "troubleshoot a deployment", or needs guidance on Agent Runtime, Cloud Run, or GKE deployment targets, or binding an agent to an Agent Gateway. Covers deployment workflows, service accounts, rollback, and production infrastructure. Applies to any framework agents-cli deploys (ADK, LangChain, ...). Part of the agents-cli skills suite. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), evaluation (use google-agents-cli-eval), or project scaffolding (use google-agents-cli-scaffold).

This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or "upgrade my project". Part of the agents-cli skills suite. Covers `agents-cli scaffold create`, `scaffold enhance`, and `scaffold upgrade` commands, template options, deployment targets, and the prototype-first workflow. Do NOT use for writing agent code (ADK projects: use google-agents-cli-adk-code) or deployment operations (use google-agents-cli-deploy).

This skill should be used when the user wants to "set up tracing", "monitor my agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed agents, including ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).

Deploy and operate apps on Prisma Compute with the Prisma 8 CLI. Use whenever the user mentions Prisma Compute, deploying or hosting a Prisma app, `prisma deploy`, `prisma dev module.ts`, Prisma Composer apps (`module.ts`, `compute()`, the `composer` section of `prisma.config.ts`), preview stages, deploy on push (`prisma git connect`, `prisma/cloud-deploy-action`), `prisma service` logs, versions, rollback, or custom domains, `project env`, `PRISMA_SERVICE_TOKEN`, regions, 504s, sleeping instances, or `waitUntil`; also when a project still uses the removed `@prisma/cli app deploy`, `prisma.compute.ts`, `defineComputeConfig`, `compute:deploy`, or `build logs` surface.

Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation/switching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.