AI Headshot Studio

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](references/review-and-recovery.md) 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.

If the tool returns insufficient credits: give the user the tool message in
full. Do not change the URL. You may translate the other words. Do not replace
it with your own top-up copy. Point the user to pay using that message.

When the user asks how many credits remain, call beatra.wallet.get. When they
ask what was charged, call beatra.wallet.ledger. Both are read-only. Do not
invent an account-balance or top-up tool. Do not make wallet.get a required
step before every paid submit.

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](references/installation-and-auth.md), [installation
registration](references/installation-registration.md), [tasks and
results](references/tasks-and-results.md), [billing, errors, and
recovery](references/billing-errors-and-recovery.md), and [uninstall and
disconnect](references/uninstall-and-disconnect.md).

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:

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.