wechat-cover-maker

Turn an article title, topic, summary, or reference image into a WeChat Official Account cover, WeChat article cover, article hero image, post cover, headline image, or supporting article visual. This AI cover generator and article cover maker distills one clear visual hook, then creates either a rendered headline or a text-free headline-safe area. Use logos, portraits, products, and brand references to shape a brand cover image, and refine composition, focal point, color, thumbnail clarity, and crop resilience for a publish-ready WeChat cover design.

WeChat Official Account Cover Maker

Create one focused article cover from an idea, a set of visual references, 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

• Create from an idea: when no source image exists, get the article title or topic, extract one visual hook, and use beatra.images.generate. A Xiaohongshu 3:4 feed cover is a different job: route it to rednote-cover-maker or zhongcao-cover-maker. Do not invent a headline to start generation.
• Compose from references: with one to four logos, portraits, products, or brand images, preserve their declared order and use beatra.images.transform. A title or topic is needed only when it changes the composition or message.
• Refine an accepted draft: use beatra.images.edit with the draft as images[0]. The accepted draft and requested change are sufficient to begin.

Follow intent and routing for the precise branch and canvas and cover craft when turning the brief into a visual specification.

Shape one useful brief

Reuse the user's article context, visual references, dimensions, title choice, and must-keep details. Ask only when a missing decision materially changes the result. The article title or topic is a hard input: do not invent a viral headline and then generate. For a standard WeChat article cover, propose 900 × 383 as the working canvas and reserve a headline-safe area; otherwise prefer the user's stated dimensions or an accepted draft's existing ratio. Confirm a different destination only when it changes the composition.

Build the brief around:

• one message and one visual hook that remains clear at thumbnail size;
• one focal subject, with supporting details kept subordinate;
• one explicit canvas;
• either the user's exact short headline in the image or a text-free, high-contrast headline-safe area;
• ordered visual references and must-keep people, products, logos, colors, or composition details.

If the user has already chosen the headline treatment, reuse it. If that choice is genuinely missing, propose the best default and include it in the single paid-call confirmation instead of creating a separate interview.

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.

• With no source image, call beatra.images.generate with an explicit canvas.
• With one to four ordered references, upload local files through the bundled client helpers, then call beatra.images.transform with an explicit canvas.
• For an accepted draft, call beatra.images.edit. Use at most two normalized edit_regions on image_index=0 for local work; 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, headline treatment, 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, 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 recognition, hierarchy, focal point, headline legibility or safe-area contrast, canvas fit, crop risk, and must-keep details. 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.

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:

bash

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.