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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