storytelling

name	storytelling
description	Build multi-shot narrative image, video, and audio workflows with `vg generate`. Use this for storyboards, shot lists, multi-prompt video, first-frame to last-frame pipelines, social stories, brand films, and sequence continuity.

Storytelling with vg generate

Use this skill when the user wants a sequence, not a single asset. Load references as needed:

• references/shot-planning.md
• references/workflows.md
• references/examples.md

Load model-catalog alongside this skill for default endpoint choices.

The goal is to produce clear story beats and executable vg generate runs. Avoid generic inspiration copy, fake dialogue, and em dashes.

Inputs to collect

Ask only when missing information affects execution.

• Format: ad, short film, music video, documentary, tutorial, social story.
• Duration and aspect ratio.
• Number of shots or allowed range.
• Main subject, character, product, or location.
• Continuity anchors: character, product, wardrobe, environment, color.
• Source media: first frame, reference image, product shot, audio track.
• Audio needs: narration, music, sound design, transcript, no audio.
• Preferred model: model-catalog defaults unless the user names one.

Genmedia workflow

Follow the standard workflow in the generate skill: resolve endpoint → inspect schema/pricing → upload references (first frame, character, product, voiceover) → run each shot --async + status → download to ./outputs/story/{request_id}_{index}.{ext}. Default endpoint IDs are in the Model routing section below.

Then choose the sequence route:

• Highest quality video: start with Seedance 2.0 endpoints from model-catalog.
• Native multi-prompt: use if schema has shot arrays, prompt lists, or timeline fields.
• First/last frame: controlled transitions between key frames.
• Image-to-video per shot: maximum continuity from approved stills.
• Manual per-shot generation: when the model only supports one prompt.
• Audio-first: generate or upload audio, then plan visual shot lengths.
• Lip-sync or talking avatar: Fabric 1.0 or Creatify Aurora from model-catalog.

Return a shot table with endpoint, request id, prompt summary, local path, and any continuity issues. Genmedia downloads clips; it does not replace a timeline editor unless the chosen model returns a complete stitched video.

Shot planning

Plan every sequence as beats first:

1. Hook: immediate visual reason to keep watching.
2. Setup: who, what, where, and why it matters.
3. Development: movement, discovery, proof, or escalation.
4. Turn: reveal, transformation, result, or emotional change.
5. Close: final image, product memory, CTA-safe frame, or unresolved mood.

For each shot, write:

• Shot number and duration.
• Story purpose.
• Visual prompt.
• Continuity anchor.
• Input reference, if any.
• Genmedia endpoint.
• Expected output path.

Prompt build order

Use this structure for each shot:

SHOT [number], [duration]:
[story purpose]. [subject and action]. [location and time]. [camera framing].
[camera movement]. [lighting and color]. [continuity anchor]. [transition or
relationship to previous shot].

Keep one shot to one clear action unless the selected model supports multi-shot or timeline prompting.

Model routing

• Highest quality video: bytedance/seedance-2.0/text-to-video, bytedance/seedance-2.0/image-to-video, or bytedance/seedance-2.0/reference-to-video.
• Fast or lower-cost video: xai/grok-imagine-video/text-to-video or xai/grok-imagine-video/image-to-video.
• Multi-shot sequence: Seedance 2.0 first, then fal-ai/kling-video/v3/pro/text-to-video, then fal-ai/kling-video/v3/pro/image-to-video, then alibaba/happy-horse/text-to-video or alibaba/happy-horse/image-to-video.
• Text-heavy keyframes, boards, UI frames, posters, or infographics: openai/gpt-image-2 at quality=high.
• Talking avatar, native audio, or lip-sync: veed/fabric-1.0, veed/fabric-1.0/text, or fal-ai/creatify/aurora.

Quality bar

Before returning:

• Shot order has a clear narrative function.
• The first shot is strong enough for the platform.
• Continuity anchors are repeated without bloating every prompt.
• Camera motion is varied but not random.
• Durations add up to the requested runtime.
• Async request IDs and downloaded files are recorded.
• The model's actual schema, not assumptions, drove the final command.