AI Headshot Studio

The Problem Being Solved

A professional headshot is a cornerstone of personal branding in the workplace. Whether for updating a LinkedIn profile, a company team page, or business cards, its quality directly impacts the first impression. However, the traditional path to obtaining a studio-grade headshot has clear pain points:

• Cost and Time: The process of booking a photography studio, traveling, and post-production editing is cumbersome.
• Scene Limitations: Most users only have casual selfies at hand, lacking a formal photo taken in a specific professional context (e.g., a modern office, a lab).
• Consistency Challenges: When fine-tuning lighting or background on an approved photo, users often need to resubmit a new request, making precise, iterative edits difficult.

Core Capabilities and Workflow

This skill aims to transform ordinary photos into professionally规范的 headshots using AI image generation. It is structured around three clear technical routes:

1. Selfie-to-Professional Headshot: Using a user's selfie as input, the beatra.images.transform model generates a new professional portrait based on a specified industry style (e.g., corporate, tech, academic). The model preserves the person's identity features (skin tone, hairstyle, facial structure) while replacing the background, attire, and applying studio-quality lighting. This is the default path.
2. Background Reference Fusion: When the user provides both a selfie and a desired background/scene image, the skill ingests both as ordered references. The background image serves only as a scene reference, used to "place" the person into that environment while maintaining their identity.
3. Refinement of Approved Headshots: For an already generated or existing professional headshot, beatra.images.edit can be used for localized or global adjustments (e.g., modifying lighting, background, expression) without altering the person's identity or overall composition.

In terms of workflow, the skill emphasizes reusing conversational context to minimize questions. It automatically attempts to extract the user's professional background, target platform, style preferences, and visual references from prior descriptions to form a brief. It only asks a single, confirmatory question when a critical decision is missing and would substantially change the outcome. All image generation operations are communicated with remote services via the bundled mcp_client.py script, ensuring operational isolation and security.

Applicability and Important Considerations

To ensure good results and comply with billing rules, the following points should be noted before use:

• Input Requirements: The selfie must be clear and recognizable. Reference images for background or style should also have clear visual direction.
• Output Specifications: The default recommendation is a 1:1 square canvas, suitable for most online platform avatars. A vertical canvas is for resumes or print materials.
• Billing and Confirmation: Each confirmed image generation/editing operation consumes credits. Before execution, the final prompt, reference image order, canvas, model, and other parameters are displayed. Any change in parameters (e.g., modifying the prompt, swapping reference images) is considered a new, billable operation, requiring a new confirmation and a new request identifier.
• Technical Limitations: The skill relies on the capabilities of models like beatra.images.transform and beatra.images.edit. Their effects may be limited for extremely complex scenes or highly stylized artistic requests. When polling task status, the designated mcp_client.py tool must be used; alternative tasks should not be created manually.
• Result Review: Upon delivery, it is recommended to perform a review across five dimensions: identity consistency, professional image appropriateness, background quality, naturalness of lighting, and canvas aspect ratio to ensure the outcome meets expectations.