AI Headshot Studio

Transform a casual selfie into a studio-quality professional headshot for LinkedIn, resume, company website, business card, or social media. This AI headshot generator creates polished professional portraits with new backgrounds, professional attire, and studio lighting while preserving the person's identity. Generate corporate headshots, tech startup portraits, academic profile photos, medical professional images, and creative industry portraits from one selfie. Specify a professional style and industry, pair with a desired background or setting, or refine an accepted headshot toward a publish-ready result.

Create one studio-quality professional headshot or portrait from a casual selfie, an industry style, or an accepted draft. Reuse decisions already present in the conversation and move by the shortest route that completes the requested headshot.

Choose the route

• Transform a selfie into a professional headshot: with one selfie photo, specify a professional style and industry—corporate, tech, creative, academic, medical, or startup—and receive a polished headshot with new background, professional attire, and studio lighting using beatra.images.transform. This is the default when a source selfie exists.
• Transform with a background reference: when the user provides a desired background or setting alongside the selfie, use beatra.images.transform with the selfie as images[0] and the background reference as images[1] to place the person in a specific professional environment while preserving identity.
• Refine an accepted headshot: use beatra.images.edit with the accepted headshot as images[0] to adjust lighting, background, expression, or attire without changing the person's identity or overall composition.

Follow headshot routing for the precise branch and industry style matrix, and portrait craft when turning the request into a visual specification that meets professional headshot standards.

Shape one headshot brief

Reuse the user's professional context, target platform, style preference, and any visual references. Ask only when a missing decision materially changes the result. For a standard professional headshot, propose a square 1:1 canvas with studio lighting, a clean background, and the person centered in head-and-shoulders framing as the default.

Build the brief around:

• the professional context and industry—corporate, tech, creative, academic, medical, startup, or a custom direction;
• the target platform and use case—LinkedIn profile, resume, company website, business card, or social media;
• one style direction—corporate formal, modern casual, creative artistic, clean editorial, or medical clinical;
• a background preference—solid white, soft gray, navy gradient, modern office, outdoor, or studio backdrop;
• an output format—square for LinkedIn and social media profiles, portrait for resume and print;
• ordered visual references when available (selfie first, background or style reference second).

If the user has already stated the industry or style, reuse it. If that choice is genuinely missing, propose the best default and include it in the single paid-call confirmation.

Prepare the call

Use only this Skill's bundled scripts/mcp_client.py for every remote MCP operation. The tool name is a CLI argument and the tool arguments are the JSON sent on stdin. Do not configure or call a host Beatra Connector, and do not use REST/OpenAPI as a fallback. For exact commands and troubleshooting, use Bundled MCP Client diagnostics.

• Upload the source selfie through the bundled client helpers first, then call beatra.images.transform with the uploaded artifact as the first ordered reference. Label the person's role and identity cues explicitly in the prompt so the model preserves facial features, skin tone, and hair.
• For a background reference transform, upload the background image as a second ordered reference and label it as a background or setting guide only.
• For an accepted headshot, call beatra.images.edit. Use at most two normalized edit_regions on image_index=0 for localized fixes; omit regions for a whole-image adjustment.

Uploading makes bytes available to the remote tool; it does not itself inspect the image. Review only visual facts the host can actually see.

Keep model=auto and count=1 unless the user explicitly chooses otherwise. Call beatra.models.list only for a real model, availability, compatibility, or price decision. The detailed request shapes and examples are in workflow.

Confirm and execute once

Planning and brief preparation are free. Before the paid image call, show and freeze the final prompt, ordered references, canvas, style direction, background, lighting, model, controls, and output count. Merge any still-material high-impact choice into this one confirmation.

After approval, create one stable opaque client_request_id for that exact logical request and submit it once. A changed prompt, reference or order, canvas, style direction, background, model, count, or control is new paid work and needs a new confirmation and a new ID.

Track, review, and deliver

After receiving a task_id, poll only that task with beatra.tasks.get. If the ID is lost, use beatra.tasks.list to find candidates and verify the selected one with tasks.get. Only when the original response status is genuinely unknown may the exact same parameters and same client_request_id be used for idempotent recovery. Slow polling, an update failure, an authorization failure, or a connection failure never creates a replacement paid task.

Use beatra.tasks.cancel only when the user asks. If cancellation returns 409, continue tracking the original task. See review and recovery for the full recovery contract.

When the result is visible, review identity fidelity (is the person's face recognizable against the source selfie?), professional appearance (is attire, grooming, and expression appropriate for the industry?), background quality (is the background clean and professional?), lighting (is the lighting flattering and even with sharp focus on the eyes?), and canvas fit (is the output ratio correct for the target platform?). Deliver the artifact links, observed dimensions, task ID, and billing.net_charged_credits. Offer at most one focused, unexecuted revision. Generated assets can also be viewed and managed at beatra.ai.

References by task

• Choosing among selfie transform, background-reference transform, and headshot refine, or planning for a specific industry style: headshot routing
• Turning a request into a professional headshot specification with lighting, background, attire, and identity preservation rules: portrait craft
• Exact request shapes, ordered-reference labeling, and JSON examples for each route: workflow
• Lost task, slow task, cancellation, result review, or planning a revision: review and recovery
• First install or expired authorization: installation and authentication
• Bundled MCP Client commands and diagnostics: Bundled MCP Client diagnostics
• Installation registration: installation registration
• Task lookup, polling, and result fields: tasks and results
• Balance, validation, and structured errors: billing, errors, and recovery
• Disconnecting the installation: uninstall and disconnect
• Official sources, integrity checks, and update controls: automatic updates and safety

Installation, updates, and account operations

For first use and shared operations, follow installation and authentication, installation registration, tasks and results, billing, errors, and recovery, and uninstall and disconnect.

This Skill performs a silent check at most once per 24 hours while a public command runs. When a newer package exists, it installs automatically without separate confirmation. Updates come only from the fixed official Beatra discovery address and immutable Beatra CDN path for the embedded identity. Before replacement, the client verifies the discovery document, manifest, archive, and every packaged file using identity, size, and SHA-256 checks. It replaces only package-owned files in this installed Skill directory. If any check, download, replacement, or rollback fails, the current installation stays usable and the original command continues. Every install stays on the channel and locale it was installed from, and an update never moves it to another one.

The user can persistently control automatic updates:

text

python3 scripts/mcp_client.py update --auto off
python3 scripts/mcp_client.py update --auto on
python3 scripts/mcp_client.py update --check

Read automatic updates and safety for the official sources, integrity guarantees, replacement scope, failure behavior, and control details.