domino-app-init

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.

Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.

No description in the SKILL.md.

Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

tasteskill 2.5.1, the anti-slop frontend skill. Builds professional company websites at the level of figma.com and x.ai: plain copy in a voice of its own, one system, one section per screen, equal edges, real photographs and real product UI (licensed stand-in photographs when the client has none; a product render only when the brief asks for 3D), a hero entrance and one or two dealt moments on every page, shader effects and scenes only on tech, gaming and software pages, quiet micro-interactions, and a checker and finish gate that enforce it in a real browser. Use this whenever you build, design, redesign, restyle, polish or review a landing page, company, product, shop, cafe, hotel, studio, SaaS, AI, developer-tool or game site, as static HTML/CSS or inside an existing React, Next.js, Vue, Svelte, Astro or Tailwind project, or when the user says a page looks generic, templated or "AI-made".

React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.

UI/UX design intelligence for web, mobile, and desktop. This skill should be used when designing, building, reviewing, or fixing interfaces, including pages, components, design systems, accessibility, interaction, responsive layout, typography, color, charts, and stack-specific UI implementation. Searchable local data: 80 searchable styles (51 active), 192 product palettes and reasoning profiles, 74 font pairings, 120 UX guidelines, 105 icons, 17 GSAP presets, 25 chart types, and 22 stacks.