domino-launchers

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, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Covers starting a Job from the UI, CLI or REST API, hardware tiers and environments, scheduled Jobs (Quartz cron, UI or API), status and logs, stopping Jobs, results and notifications. Use when running batch workloads, scheduling recurring tasks, or automating training pipelines. For the CLI itself use domino-cli; for multi-step pipelines domino-flows; for an existing Airflow or Kubeflow domino-airflow-kubeflow; for batch scoring with a model use domino-model-delivery.

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.

About 450 ready-made HyperFrames blocks and components installed as editable source - transitions, overlays, titles, kinetic type, captions, charts, map overlays, 3D, camera moves. Use it before hand-building any named visual (CRT scanlines, glitch, film grain, a chart); also for hyperframes add and catalog.

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

Deprecated compatibility name for prisma-orm-setup. Use only when a user or existing integration explicitly invokes prisma-database-setup; otherwise select prisma-orm-setup.

Deprecated compatibility name for prisma-postgres-setup. Use only when a user or existing integration explicitly invokes prisma-postgres; otherwise select prisma-postgres-setup.

Obtain or reuse a Prisma Postgres database and connect an application. Use when the user needs a database and has none configured ("I need a database", "add a Postgres database", "set up a database for this app"), runs or mentions `create-db` or `npx create-db`, or asks to set up or connect Prisma Postgres, including in v0 and the Vercel Marketplace. Hands Prisma ORM configuration to prisma-orm-setup; reuses an existing connection first and honors an explicit provider, driver, alternative ORM, or database-only choice.

Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and verification.

Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

Guides and best practices for working with Lakebase Postgres on Neon: connections, pooled vs direct, schema migrations, branching (including schema-only, reset-from-parent, and branch-per-PR workflows), autoscaling, scale-to-zero, instant restore, read replicas, IP allow lists, logical replication, egress optimization, and Lakebase Search. Use when the work is an existing DATABASE_URL, SQL, schema, inspect, or search. New backends, Auth, files, Functions, and LLM calls go to the parent `neon` skill. Live queries, reactive UIs, and realtime data go to `neon-realtime`. Also use for "@neondatabase/serverless", "@neon/neon-js", "neon inspect db", "neon postgres computes", "Neon branch", "schema-only branch", "reset branch", "branch per PR", high database bills, network transfer or egress, SELECT * optimization, query overfetching, "semantic search", "vector search", "full-text search", "BM25", or "hybrid search".