pixar-storyteller

Turn an animation beat into the four-block storyboard: CHARACTER SHEET · SCENE PROMPT · IMAGE PROMPT · VIDEO PROMPT. Output is provider-agnostic; provider tuning is motion-choreographer.

The acting / storyboard lens is enforced inline by the procedure and self-review below.

When to use

• A scene is animated / stylized (not photoreal) and needs an emotional read, not a film-set read.
• The character must want something and the environment must respond — flat poses fail.
• Live-action / photoreal beats → video-director.

Do NOT use when:

• The deliverable is provider-tuned token grammar → motion-choreographer after this skill.
• A character must be re-used across scenes → run character-consistency first to lock the CHARACTER SHEET, then call this skill.
• The brief is a static graphic / poster → canvas-design.

Procedure

Step 0: Inspect

1. Confirm the input is an animated / stylized beat.
2. If character.json exists under agents/reference/ai-video/<project>/characters/, read identity tokens — reused verbatim in CHARACTER SHEET.
3. Read the beat once. Name the want and the obstacle in one sentence each before drafting.

Step 1: Draft the four blocks

Emit each block under a labeled heading. Blocks are mandatory and in this order:

1. CHARACTER SHEET — silhouette, palette, wardrobe, signature prop, posture default, eye behavior. Verbatim from character.json when a lock exists.
2. SCENE PROMPT — single emotional beat, want, obstacle, stylistic anchor (specific film + year — "Up (2009)", not "Pixar-style"), environment reaction.
3. IMAGE PROMPT — one still at the peak of the beat. Names composition, eye line, palette. No motion verbs.
4. VIDEO PROMPT — decomposes the moment into anticipation Xs · action Ys · reaction Zs with explicit beat counts. Names which secondary motion (hair, cloth, dust) reacts on which beat.

Step 2: Self-review

1. Exactly one emotional beat in SCENE PROMPT? Compound moods fail.
2. Stylistic anchor is a specific film + year? Generic style → fail.
3. VIDEO PROMPT names beat counts per phase?
4. IMAGE PROMPT names eye line?
5. CHARACTER SHEET matches character.json byte-for-byte when a lock exists?

Any "no" → revise that block.

Step 3: Validate

1. Output is plain text, one labeled block per heading, ready for scripts/ai-video/lib/parse-blueprint.sh.
2. No provider tokens — that is motion-choreographer.
3. No lens / focal-length prescriptions — that is video-director's block, not this one.

Output format

1. scenes/<id>/prompt.txt — four labeled blocks, ready for the blueprint parser.
2. scenes/<id>/review.md — one-paragraph rationale per non-obvious choice (stylistic anchor, environment reaction, beat decomposition).

Gotcha

• The model defaults to "Pixar-style" with no film anchor — that reads as a wishlist. Force a specific title + year.
• Neutral faces are the AI default — every beat names a feeling the face is doing, including eye line direction.
• Environment-as-backdrop is the silent failure mode — name what the world does in response to the character.
• Collapsing anticipation and action into one verb makes motion look teleported. Both phases or the prompt fails review.
• When character.json exists, paraphrasing breaks Character Lock — copy identity tokens verbatim.

Do NOT

• Do NOT prescribe lenses or focal lengths — that is video-director.
• Do NOT emit provider-specific tokens — that is motion-choreographer.
• Do NOT cite "Pixar-style" without a specific film + year.
• Do NOT compound emotional beats ("sad but hopeful and tired").

Policies

Paths, enforcement model, and the full set: the media policy preamble.

The Pixar-storyteller skill anchors prompts to named films and studios by design — the policy surface is the largest in the video cluster:

• style — naming a film + year as the primary anchor crosses the "in the style of [STUDIO]" trigger; surface and refuse without a transformative-intent rationale.
• likeness — when a beat references a named animator's signature character (real-person extension of style).
• public-figures — when the storyteller's character is a recognised public figure rendered in Pixar shape.
• disclosure — every Pixar-style output ships with the AI-generation disclosure; parody / commentary cases are flagged for human review.

Refuse-and-surface when style ⇒ primary signature, not one influence among several.