cinematic-image-prompting

name	cinematic-image-prompting
description	Generate cinematic image prompts for characters, locations, props, costumes, keyframes, posters, concept art, reference sheets, and scene stills using creative intent plus camera, lighting, texture, and continuity anchors.

cinematic-image-prompting

When to use

• The user wants still image prompts, character sheets, location references, prop/costume concepts, keyframes, posters, thumbnails, concept art, or scene stills.
• The user needs image prompts before video generation to lock identity, composition, or style.
• The task requires photorealism, cinematic language, texture, camera/lens terms, or negative/positive constraints.

When not to use

• The user asks for moving video prompts only.
• The user asks to adapt a finished prompt to a named model only.
• The user wants actual image generation rather than prompt writing, unless paired with image generation tools.

Required inputs

• Subject or asset type
• Purpose of image
• Creative intent
• Continuity anchors
• Style/genre/tone

Optional inputs

• Target model
• Aspect ratio
• Reference images
• Text/logo needs
• Photorealism level
• Negative constraints

Workflow

1. Start with creative intent: who/what, why this image exists, emotional read, and audience impression.
2. Build cinematic execution: subject specificity, action/pose, environment, framing, lens feel, lighting direction/quality, color, texture, and realism level.
3. Add continuity anchors from the bible: face, wardrobe, prop state, location geography, palette, and design rules.
4. Select format: natural prose, priority stack, shot card, or model-specific export through model-adaptation.
5. Handle exclusions according to model behavior: true negative field where supported, positive restatement where not.
6. Create variants only by changing one meaningful variable at a time.
7. Add quality checklist and revision hooks.

Decision logic

• If character consistency matters, create a clean reference sheet before dramatic scene stills.
• If the image contains text, route to model-adaptation and choose a model/profile that supports text better.
• If the style is too broad, ask for or infer one strong visual anchor plus specific execution details.
• If the target model is unknown, output a universal prompt brief and flag model-dependent fields.

Output formats

• Universal image prompt brief
• Cinematic still prompt
• Character/location/prop reference prompt
• Negative or positive constraint block
• Prompt variants
• Model-adaptation handoff

Quality checks

• Prompt has a clear subject, action/pose, environment, composition, lighting, style, texture, and output purpose.
• The first sentence front-loads the most important identity and scene elements.
• No contradictory style or lighting instructions.
• Continuity anchors are explicit and not overlong.
• Model-specific claims are not guessed.

Anti-patterns

• Keyword soup for natural-language models
• Overloaded prompts with many competing references
• Negative prompts in models that lack negative support
• Vague words like epic or cinematic without camera/lighting specifics
• Using copyrighted or trademarked specifics when a generic visual description is safer.

Exit criteria

• The prompt is ready for universal use or handoff to model-adaptation for a named image model.

Supporting files

Read only the supporting file needed for the active task:

• references/image_prompt_components.md
• references/lighting_and_photorealism.md
• references/prompt_anatomy.md
• references/negative_prompt_patterns.md
• templates/cinematic_still_prompt.md
• templates/character_reference_sheet_prompt.md