Cinematic video prompting
Skill 0xhughs/director-skills/skills/cinematic-video-prompting
Agent skills for AI filmmaking, cinematic prompting, and model-specific prompt export.
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Generate text-to-video and image-to-video prompts with shot timing, subject motion, camera movement, lensing, lighting, audio cues, continuity anchors, and model-aware constraints.
SKILL.md
3.5 KB, 731 tokens by cl100k_base, as published. Nobody here has run it
cinematic-video-prompting
When to use
- The user wants text-to-video, image-to-video, first/last-frame, video extension, multi-shot, audio-aware, or motion-control prompts.
- The user has a shot list or still image and wants to animate it.
- The task requires duration math, camera movement, subject motion, physical consequence, lip sync, or video-specific failure prevention.
When not to use
- The user only needs a still image prompt.
- The user is still developing story with no visual beat.
- The user wants editing of a real video file rather than prompt design.
Required inputs
- Scene or shot goal
- Target duration
- Subject and action
- Camera/framing
- Continuity anchors
Optional inputs
- Reference image/video/audio roles
- Target model
- Aspect ratio/resolution
- Dialogue line
- Audio design
- Negative constraints
Workflow
- Identify the shot purpose and emotional turn.
- Respect the clock: choose one dominant action and one dominant camera behavior for short clips unless the model explicitly supports multi-shot.
- Write time blocks or beat blocks when the model benefits from sequential structure.
- Pair every action with physical consequence: weight, friction, follow-through, environmental reaction, or sound.
- Specify camera movement, lens feel, framing, lighting, palette, texture, and realism guardrails.
- For I2V, preserve source composition and describe only motion, camera, lighting shift, and audio.
- Add audio layers where supported: dialogue, ambience, foley/SFX, and music mood.
- Add edit/post notes when this prompt belongs to a larger AI short-film package: take count, cut point, color-grain unification, and audio bed.
- Route to model-adaptation when a named model is involved.
Decision logic
- If a prompt contains two or more big actions, split into shots.
- If identity drift is likely, generate/reference a character sheet first.
- If hands, mirrors, text, or logos are central, redesign framing or use a more suitable model.
- If the model supports negative prompts, cap constraints to the few failures that would ruin the clip.
Output formats
- Universal video prompt brief
- Text-to-video prompt
- Image-to-video prompt
- Time-blocked prompt
- Audio cue block
- Continuity guardrails
- Model-adaptation handoff
Quality checks
- Duration, shot count, camera movement, and action complexity are compatible.
- Motion is concrete and continuous, not a vague mood.
- Subject and camera motion do not conflict.
- Continuity anchors protect identity, wardrobe, props, and location state.
- Known failure modes are addressed without over-constraining.
Anti-patterns
- Trying to direct a full film in one prompt
- Multiple camera moves in one short clip
- Long dialogue in an 8-second clip
- Describing an uploaded image again instead of animating it
- Using negative prompts as a dumping ground for every fear.
Exit criteria
- The video prompt is ready for the target workflow or for model-specific export.
Supporting files
Read only the supporting file needed for the active task:
references/video_prompt_components.mdreferences/camera_lens_movement.mdreferences/audio_motion_timing.mdreferences/ai_film_production_pipeline.mdreferences/prompt_anatomy.mdtemplates/text_to_video_prompt.mdtemplates/image_to_video_prompt.md