zhongcao-cover-maker

Turn a photo, a topic idea, or an accepted draft into a scroll-stopping REDnote (Xiaohongshu) cover and post image. This AI cover generator creates vertical 3:4 Xiaohongshu note covers with clean backgrounds, bold focal composition, and text-safe areas for beauty, food, fashion, travel, and knowledge content. Generate high-click Xiaohongshu note covers, OOTD post images, food photography covers, product recommendation visuals, and lifestyle note illustrations from one photo or a topic description. Start from a real photo, compose from multiple references, or refine an accepted cover toward a publish-ready result. Optionally it reads Xiaohongshu itself — the notes already running for the topic, one page of a note's top comments, and an account's own recent notes — so Xiaohongshu research, competitor note analysis and comment analysis rest on the platform instead of on guesswork.

Create one scroll-stopping REDnote (Xiaohongshu) cover or post image from a photo, a topic idea, or an accepted draft. Reuse decisions already present in the conversation and move by the shortest route that completes the requested cover.

Choose the route

• Transform a photo into a cover: with one photo—food, product, outfit, selfie, or scene—enhance it into a polished vertical 3:4 Xiaohongshu cover with clean background, professional lighting, and a text-safe area using beatra.images.transform. This is the default when a source photo exists.
• Generate a cover concept from a topic: when no source photo exists, turn the confirmed topic or note idea into a click-worthy cover visual using beatra.images.generate. Do not enter this route on a style word alone.
• Refine an accepted cover: use beatra.images.edit with the accepted cover as images[0] to fix color, lighting, background blemishes, or composition without changing the overall layout.

Follow cover routing for the precise branch and cover craft when turning the request into a visual specification that fits Xiaohongshu's platform aesthetic.

Shape one cover brief

Reuse the user's content topic, target audience, style preference, headline copy, and any visual references. Ask only when a missing decision materially changes the result. Without a source photo, the topic is a hard input: do not invent one, and do not treat a bare style word as enough to generate. For a standard Xiaohongshu feed cover, default to a vertical 3:4 canvas.

Build the brief around:

• the note topic and category—beauty, food, fashion, travel, home, knowledge, product recommendation, or lifestyle;
• one focal subject—the dish, product, outfit, person, or scene that anchors the cover;
• one style direction—clean and bright, warm and appetizing, editorial and minimal, cozy and atmospheric, or bold and high-contrast;
• headline placement—when the user already wrote cover title copy, render it in the top or bottom third and keep it from filling the frame; when they did not, leave a clean text-safe zone (top third, center, or bottom) for a later overlay;
• ordered visual references when available (style inspiration, color reference, composition reference).

If the user has already stated the topic or style, reuse it. If style is missing but a topic or photo exists, propose the best category 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 photo through the bundled client helpers first, then call beatra.images.transform with the uploaded artifact as the first ordered reference. Label the photo's role explicitly in the prompt so the model preserves the subject.
• For a topic-only concept, call beatra.images.generate with a vertical 3:4 canvas and a prompt that captures the desired mood, color palette, and composition.
• For an accepted cover, 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, with or without the optional Xiaohongshu lookup, which is priced and approved on its own before it runs. Before the paid image call, show and freeze the final prompt, ordered references, canvas, style direction, headline copy and placement or the empty text-safe zone, model, controls, and output count. Merge any still-material high-impact choice into this one confirmation. Do not add three color-mood variations or a carousel set to this card.

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, 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 thumbnail clarity (is the focal subject recognizable at small size?), headline treatment (if title copy was rendered, is it readable at thumbnail size and confined to the top or bottom third; if not, is there clean space for a later overlay?), color and lighting (does it match the Xiaohongshu aesthetic for the category?), canvas fit (is the vertical 3:4 ratio correct?), and overall click appeal. 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.

Reading Xiaohongshu before you write

Optional, and paid. When the connection exposes Beatra's public social lookup, this Skill can read Xiaohongshu directly instead of working from what the user remembers: one page of notes matching a keyword, one specific note the user pastes, one page of that note's top comments, and an account's profile or recent notes. Six operations, Xiaohongshu only.

Every one of them costs 60 credits, and there is no cheap operation on this platform to fall back on. The same reads cost 6 on TikTok. They cost 6 on Douyin too — except Douyin's own keyword search, which is also 60, so do not say "ten times Douyin" without naming the read. A three-step read — the field, one note, that note's top comments — is 180 credits, and every further page is another 60. Say the number before offering anything, confirm each lookup on its own before it runs, and say plainly that this Skill's own deliverable arrives either way at no cost. Offer one read, not a plan of four.

The rule is the whitelist, not a list of exceptions: a platform with no operation on it cannot be looked up from here, and another platform's notes are never presented as Xiaohongshu's. A returned image URL is not a viewed image — state a visual finding only about an image the host can actually open. Every figure that reaches the work is labelled as looked up with the time it was read, or as supplied by the user, or as missing. Nothing is estimated, and nothing is carried in from what notes in this category usually do.

See reading Xiaohongshu for the operations, the argument routes, the confirmation wording, and how a result is reported and recovered.

References by task

• Choosing among photo transform, topic concept, and cover refine, or planning for a specific Xiaohongshu content category: cover routing
• Turning a request into a Xiaohongshu-style cover specification: cover 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. Canonical English installs stay on canonical/en, and SkillHub Chinese installs stay on skillhub/zh-CN.

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.