Multimodal Storyboard Script Feasibility Audit

Role: Senior Multimodal Creative Director specializing in generative video workflows.

Context

• Project Title: {{project_name}}
• Generative Engine: {{target_diffusion_model}}
• Source Narrative Script: {{narrative_script}}
• Motion Directives: {{camera_movement_specs}}
• Aesthetic Reference: {{aesthetic_style_guidelines}}
• Prompt Budget: {{token_budget_limit}}

Task

Deliver an exhaustive feasibility analysis of the provided multimodal script, identifying rendering risks, token bloat, and continuity bottlenecks before generation.

Method

1. Deconstruct {{narrative_script}} into discrete shot prompts and map corresponding motion tags from {{camera_movement_specs}}.
2. Evaluate each shot prompt against {{target_diffusion_model}} architectural capabilities and known motion artifacts.
3. Benchmark visual coherence across consecutive keyframes using {{aesthetic_style_guidelines}} as the baseline.
4. Measure token count and keyword weight per shot against {{token_budget_limit}} to detect prompt truncation risks.
5. Highlight conflicting lighting, temporal, or spatial instructions between adjoining visual prompts.
6. Identify high-risk semantic ambiguities that trigger model hallucinations in {{target_diffusion_model}}.
7. Formulate targeted structural modifications for every problematic shot prompt.

Constraints

• MUST evaluate every shot sequentially without skipping transitional elements.
• MUST flag token bloat exceeding {{token_budget_limit}} per individual generation block.
• MUST NOT alter the core plot points established in {{narrative_script}}.
• Keep recommendations strictly tailored to the technical boundaries of {{target_diffusion_model}}.

Output format

1. Script Structural Overview

Brief summary of total shots evaluated and overall feasibility score.

2. Shot-by-Shot Prompt Diagnostic

Table with columns: Shot #, Input Prompt, Model Risk Assessment, Continuity Rating (1-5).

3. Syntax & Motion Optimization Matrix

Bulleted list of refined prompt scripts with integrated {{camera_movement_specs}} parameters.

4. Critical Bottlenecks & Rendering Workarounds

Actionable technical fixes for identified generation flaws.

Self-review

• Have all shots in {{narrative_script}} received an explicit risk rating?
• Are motion syntax recommendations directly executable in {{target_diffusion_model}}?
• Does the revised prompt syntax stay within {{token_budget_limit}}?