scrollclaw-first-frame

Generate a UGC-authentic first image frame for AI video with iPhone-photo realism.

68|15|Updated Mar 24, 2026
One-click install
npx skills add https://github.com/TheMattBerman/scrollclaw --skill scrollclaw-first-frame
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: scrollclaw-first-frame
Source: https://github.com/TheMattBerman/scrollclaw/tree/main/first-frame
Command: npx skills add https://github.com/TheMattBerman/scrollclaw --skill scrollclaw-first-frame

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Produces an iPhone-photo-looking first frame that wins the 3–5 second stay-or-scroll decision by locking composition, realism, and visual “anti-polish” before animation starts.

Core Features & Use Cases

  • Canonical first-frame generation: Creates the creator’s believable, phone-captured face image that acts as the visual design gate.
  • Three-layer prompting for UGC realism: Combines a fixed photorealism pre-prompt, a color-reference JSON, and a precise scene description with strong negative constraints (e.g., no text/letters).
  • Visual reference chaining for multi-frame formats: Enforces sequential generation where later frames must reference frame 1 to prevent face drift across settings.
  • Validation-oriented iteration loop: Guides repeated attempts (typically 2–4) using quality checks like skin texture, identity preservation, practical lighting, and real-world clutter.

Quick Start

Run the first-frame generation for your campaign so the canonical frame1.png is created and ready to chain into /animate and /b-roll.

Frequently Asked Questions about scrollclaw-first-frame

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a UGC-style first frame for AI video?

To generate a UGC-style first frame, use a three-layer prompting structure that combines a fixed photorealism pre-prompt, color-reference JSON, and a strict negative prompt to enforce iPhone-photo realism and practical lighting.

Why does face drift happen in multi-frame AI video generation?

Face drift happens when subsequent frames lack a locked visual reference. Applying visual reference chaining ensures later frames strictly reference the canonical frame 1, preventing identity loss across different settings.

What is the best way to stop scroll-stopping AI video from looking too polished?

The best way to prevent over-polished AI video is to enforce visual anti-polish by validating for real-world clutter, practical lighting, and natural skin texture through a 2 to 4 iteration loop.

Can I use a negative prompt to remove text from AI video first frames?

Yes, you can use a strict negative prompt specifying no text or letters to ensure the generated first frame maintains authentic phone-captured realism without artificial graphic overlays.

How to maintain color consistency across multiple AI video settings?

To maintain color consistency, apply a color-reference JSON layer during the initial first frame generation, which locks the color grading for all chained frames and logs it to the campaign output.