Photo Restoration & Colorize Director

The problem

Restoring old or damaged photos with AI often leads to over-processed, "plastic" skin, or the fabrication of historical details. Developers and designers need a structured, non-destructive logic to repair assets without losing the original's soul or authenticity.

What it does

• Analyzes photos to diagnose structural damage, color casts, noise, and resolution issues in priority order.
• Generates a step-by-step restoration pipeline that sequences repairs before enhancements to avoid amplifying artifacts.
• Specifies exact tool types and settings for generative filling, HSL adjustments, and face recovery.
• Enforces strict faithfulness guardrails to prevent the AI from inventing people or objects not present in the original.
• Calculates technical requirements for specific end-uses, such as 300 DPI print-ready upscaling.

Why this beats prompting it yourself

Generic prompts often result in the AI "hallucinating" facial features or adding modern elements to vintage scenes. This skill implements a professional restoration artist's logic, ensuring damage repair happens before upscaling and that colorization remains period-accurate rather than stylized.

Use cases

• Restoring physically damaged family archives with tears, creases, or missing corners.
• Colorizing black-and-white historical photography using plausible, era-appropriate palettes.
• Enhancing low-resolution scans for high-quality physical printing at specific dimensions.
• Recovering faded or yellowed photographs while maintaining original grain and texture.

Known limitations

This skill provides the technical plan and tool direction but does not perform the direct pixel manipulation itself. It requires a user to have access to image editing software with generative fill and adjustment capabilities.