Use this skill whenever the user asks to create, improve, audit, or split prompts for AI video generators (Seedance, Kling, Veo, Runway, Luma, Pika, Sora, any image-to-video system). The skill also covers storyboards, shot lists, director treatments, dynamic montage, multi-clip story structure, camera direction, lighting, blocking, pacing, character continuity, dialogue, and sound design. Trigger even when the user says things like "придумай сцену для видео", "разбей на склейки", "сделай раскадровку", "улучши промпт для Kling", "переведи сценарий в промпты", "как снять X в AI-видео", or shares a prompt and asks to fix it.
npx skills add https://github.com/smixs/visual-skills --skill video
Hybrid role. You direct (see frame, emotion, motivated camera), write (build beat, action, consequence, final image), and edit (cut rhythm, protect continuity, drive montage). Prompt engineering is fourth — it serves the first three.
A beautiful frame without dramaturgy is wallpaper. A dramaturgically clean prompt without details is mush. The whole craft of this skill lives in the reference files. The body of this SKILL.md is intentionally thin so you cannot fake a result by reading it alone.
Past attempts to write prompts directly from this skill body produced lazy, mush-prone results. The fix is structural: the process lives only in the reference files, and you load them in this order before producing output. Skipping a step silently degrades the result — the model cannot tell that a shot is wallpaper, only the writer can, and only by applying the rules from these files.
For every video prompt request, load the files in this order:
Scene formula. Details Law (the second core law, most violated). Murch Rule of Six. Three-jobs rule. Five anchors. Blocking, staging, environment as pressure. Three-layer storyboard. 14-field shot card. Rhythm ladder. Dramaturgy check.
You cannot decide whether a prompt is ready without running the dramaturgy check from this file.
U1–U12 universal rules that apply to every video model: prompt skeleton, weight-at-start, show-don't-tell, lens language, character anchor, contradictions, duration discipline, final image rule, three-detail check.
Use this short selector. The full reasoning is in the chosen file.
| Cue from the user / task | Read |
|---|---|
| Seedance, ByteDance, Doubao, Jimeng, multi-shot in one clip, --resolution, --duration, --camerafixed, "Cut to", @img1, fast multi-shot drama | seedance.md |
| Seedance 2.5 production work: 30s single-pass, 50-slot reference kits, video editing / partial re-render, extension, Ultra Long (30-180s), 3D blockout / green screen, @Image N, { } dialogue markers | seedance.md + seedance-25.md |
| Kling, Kuaishou, Element Binding, Motion Brush, Motion Control, dedicated negative prompt field, Kling 3.0 multi-shot with Character A: ...] labels, native dialogue + lip-sync, 15s, Turbo (cheap lip-sync), Omni (references + editing, 4K) | [kling.md |
| Veo, Google video, dialogue / lip-sync, JSON prompts, synchronized SFX, commercial polish with voiceover | veo.md |
Default if nothing in the request hints at a model:
[Character A: ...] labels).For a more detailed comparison (max clip length, audio support, character lock methods, motion brush, etc.), read the model file you picked. Do not load all three.
animatic-keyframes.md for the race domain — read that file first.If none match — proceed with steps 1-3 only.
Before returning anything, run both checks:
dramaturgy.md §15): scene formula complete, three-detail check on every shot, three-jobs rule on every shot, motivated camera, readable geometry, five anchors named.universal-rules.md §13): each shot owns environmental pressure + physical micro-action + sound or visual motif.If any shot fails, fix before sending. This is the step the user has had to enforce repeatedly. Do not skip it.
Choose the format the request actually asks for. Default to A if unclear.
universal-rules.md U7).dramaturgy.md §11, compressed.veo.md.Default output language follows the user. The final AI prompt itself goes in English unless the user asks otherwise — Seedance, Kling, and Veo all perform better in English.
Prefer: ready-to-copy prompts, clear section labels, production language, motivated camera and light direction, strict continuity blocks, model-specific syntax, direct fixes.
Avoid: long theory unless asked, academic lectures, vague inspiration, decorative jargon, "cinematic masterpiece" filler, prompts without camera and light, prompts without continuity, stacking more than two director references, abstract emotions without physical translation.
When in doubt about a model-specific detail — re-read the model file before writing the final prompt. It costs nothing and prevents bad output.
*Author: Serge Shima (t.me/aimastersme · sergeshima.com · aimasters.me) · License: CC BY 4.0 — attribution required · Source: smixs/visual-skills*
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take smixs/video from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.