Eyebrow Style Quad-Grid Design

Problem

Choosing an eyebrow shape from a front-facing portrait is not just about drawing a line. It requires comparing face shape, feature proportions, skin tone, skin texture, and the existing brow shape, then producing several visually comparable options. Plain prompts can drift, while a single generated image often lacks side-by-side comparison and style rationale.

How It Works

This skill takes one front-facing photo and generates a 2x2 quad-grid eyebrow design image. The workflow includes:

• Compliance check: validates the input image and prompt against content rules.
• Facial feature analysis: extracts face shape, feature ratios, skin tone and texture, and existing brow characteristics, then creates English consistency keywords.
• Style design: proposes four differentiated brow styles, such as standard, willow-leaf, sword, and wild brow, with corresponding prompts.
• Layout and consistency synthesis: combines the four proposals into one quad-grid generation instruction with Simplified Chinese labels.
• Generation and polling: calls an image model and retrieves the final image URL through an async task.

Real runs usually require a ShowAPI appKey. Submission returns only a task_id; the client must poll until it receives the quad-grid image, facial analysis, and style summaries.

Boundaries and Notes

It fits personal grooming, makeup styling, and preliminary communication for brow embroidery or similar image-design references. The img field is required and accepts a URL or standard Base64; a clear front-facing photo is recommended. Real tasks are asynchronous only. Mock runs without an appKey are previews and should not be treated as official generated output.