asset-generation

name	asset-generation
description	Use Assets for brand-safe image or video generation, human picker UI, search/list/export actions, and cross-app asset selection. Use when a visual needs to be generated, refined, found, or handed off to another app.
metadata	{"visibility":"both"}

Asset Generation

Rule

Use the Assets app when a workflow needs reusable brand media, a human picker, or generated image/video assets that another app can reference by ID and URL.

Visual authority and brief

Assets produces media; it does not invent a replacement brand language. Before generating, resolve the user's explicit subject, audience, message, format, and must-preserve constraints, then the active library, preset, or linked design system and its custom instructions, then approved Creative Context references. Impeccable-inspired guidance is a quality lens for composition, hierarchy, restraint, and finish — never a reason to override those sources.

Compile the request into a short art-direction brief: visual role, subject, composition and crop, palette or material treatment, lighting or medium, exact visible text if any, semantic constraints, and exclusions. Classify the slot as produce (new media), direct (an existing approved asset), or semantic (a UI/icon/diagram the caller should build with its own primitives). Do not generate a decorative photo where the caller needs a semantic graphic. If brand context is missing, make the result clearly exploratory rather than claiming a brand match.

Choose The Path

• Use generate-asset when a person should get newly generated, on-brand image candidates and choose the winner in the inline picker. It matches a library when libraryId is omitted, generates candidates, returns the picker filtered to those run IDs, and works in in-app chat plus external MCP hosts.
• Use open-asset-picker when a person should browse, search, or select an existing asset inside an embedded picker, or when you want the picker to handle generation itself. It still opens /library with the iframe/bridge contract. The normal human Library workspace is /library and /library/:id. Pass mediaType: "image" by default, or mediaType: "video" for video libraries.
• Use unattended actions when the agent already knows what to do: search-assets, list-assets, import-style-from-url, generate-image, generate-image-batch, generate-video, refresh-generation-run, and export-asset.
• In chat, consume composer @ references as structured generation inputs: brand-kit maps to libraryId, preset maps to presetId, and media-type chooses image generation versus video generation. If no mention is available, use view-screen, list-libraries, and list-generation-presets to choose explicit args.
• Use generation presets when the user asks for a repeatable output format like social image, blog hero, or diagram. Call list-generation-presets for the library and pass presetId through generation/refinement actions.
• Use generation sessions when another person needs to continue improving a candidate. Sessions carry the brief, preset, active asset, feedback, and run IDs without requiring the original chat thread.
• Use chat-driven restyle-image and edit-image for preserving subjects, applying library style, and making targeted changes. Do not surface separate restyle, edit, or quality-tier buttons in host UIs.
• Use browser/deep-link fallback when the host cannot render MCP Apps inline (CLIs and code editors like Claude Code and Codex). Surface the returned picker link. When the user opens it, they can either click an asset — the page auto-copies a short handoff summary for them to paste back into chat — or simply tell you which one in words (e.g. "use image A" / "the second one"). Both are first-class; don't insist on the paste-back if they just name the pick.

Image Workflows

1. Read the creative-context skill and retrieve visual references separately from factual evidence. Respect contextMode: "off", pinned packs, and the exact reuse ladder before generation: approved native asset unchanged, compose approved pieces, lightly adapt a real example, condition generation on narrow references, then net-new only when the relevant corpus is empty.
2. For human-in-the-loop generation, call generate-asset first and preserve the returned picker/candidate metadata. For unattended generation, pick or match the library with list-libraries or match-library. If the user wants a default look rather than a brand library, call list-library-presets and then create-library-from-preset; the resulting library is editable and reusable like any other library.
3. For one asset, call generate-image; for multiple independent slots, call generate-image-batch with stable slotId values.
4. Image generation actions are synchronous. After generate-image or generate-image-batch returns, use its returned images / asset fields directly; do not call get-generation-run, refresh-generation-run, or regenerate just to verify image runs.
5. For preset-backed work, pass a mentioned or selected presetId; for handoff work, pass sessionId.
6. Let the server choose a small deterministic reference set unless the user named exact assets. Canonical style anchors come from assetLibraries.settings.canonicalStyleAssetIds and assets.metadata.isStyleAnchor; they must remain subordinate to explicit library, preset, and per-run constraints rather than introducing a second visual language.
7. Pass tier: "fast" for exploration, tier: "best" for final/high-value output, or tier: "auto" when there is no clear preference.
   • Model/ratio compatibility: Gemini image models accept any aspectRatio, but gpt-image-2 supports only 1:1, 2:3, and 3:2. When the user needs another ratio (16:9, 9:16, 4:5, 21:9, …), pick a Gemini model rather than gpt-image-2 — an unsupported pairing is rejected upstream. Source of truth is supportedAspectRatiosForModel / MODEL_ASPECT_RATIOS in .

For short vague prompts, enhance conservatively with library style context while preserving the user's original prompt in run metadata. If a public website is the style source, call import-style-from-url first so the library keeps the hydrated browser-derived design brief. Use analyze-collection-style when a collection needs upgraded vision brand analysis from image references before generation. Brand QA scoring and best-of-N selection are deferred.

Generation success confirms a run and its provenance, not visual quality or brand match. Report the selected library, preset, style anchors, and whether the result used an Assets-grounded or fallback path. Claim a quality evaluator only when one actually ran.

Video Workflows

1. Call generate-video with 16:9 or 9:16 and relevant image references.
2. Poll refresh-generation-run until the run completes and returns a video asset.
3. Use export-asset when another app needs a download URL or artifact type.

Cross-App Use

• Hosted default: connect https://assets.agent-native.com/_agent-native/mcp. Do not put shared secrets in skill files.
• Local customization: run npx @agent-native/core@latest app-skill launch --local from the Assets app-skill manifest, or pass --into <path> for editable source.
• For MCP callers, generate-asset is the portable first choice because the same MCP App picker renders inline in Agent-Native chat, ChatGPT, and Claude when the host supports MCP Apps. Include exact assetId, runId, media type, and URLs in the final response so the caller can attach or embed the media. Include presetId and sessionId when present.

Don't

• Do not call image/video providers directly from another app.
• Do not treat images as the app identity; the app id is assets.
• Do not use picker UI for unattended generation when direct actions are enough.
• Do not use copyrighted screenshots or named studio/brand image sets as preset references. Use broad textual guidance and user-provided references instead.