wechat-channels-cover-maker

WeChat Channels Cover Maker

Create one WeChat Channels video cover from a topic, title, script, exported key-frame screenshot, portrait, product photo, visual reference, or accepted draft. Reuse the channel direction and the intended viewer context, then make one clear cover that preserves the focal subject and space for the title.

Scope and route choice

Use this Skill for a new cover image for a WeChat Channels video. It is for knowledge sharing, a personal creator update, local-business promotion, product explaining, and brand content. It does not extract a frame from an uploaded video or publish a video.

For a WeChat Official Account article cover, use wechat-cover-maker. For a video-account product clip, use wechat-channels-product-video. For a generic existing-cover review, use cover-performance-preflight; for the video itself, use beatra-ai-video-studio.

• Create from the story: use beatra.images.generate when the topic, title, or script is sufficient and no image source must be preserved.
• Compose from images: upload an exported key frame, portrait, product, or ordered references and use beatra.images.transform. If the user has only a video, ask for one exported key frame or screenshot; this image route has no video frame-reading tool.
• Refine an accepted cover: use beatra.images.edit with the accepted image as images[0] and no more than two normalized local edit regions.

Shape the cover brief

Reuse the video topic, title, opening hook, channel style, target viewer, portrait or product source, references, and must-keep details already in the conversation. Choose the canvas from the user-stated destination, source frame, or current publishing requirement. Prefer an explicit destination preset. If the user confirms a source-derived aspect for an image transform, its final ordered image anchors that aspect: put the intended canvas anchor last, disclose that role in the confirmation, and never assume the first focal source sets it. Propose a video-cover canvas only as a starting point; freeze the actual canvas before paid work.

Plan one clear focal subject, a small-size visual hook, readable hierarchy, and a title-safe area. Prefer title-safe space rather than promising exact rendered Chinese words or logos. If the user requires in-image text, freeze the exact short text and inspect it character by character only when it is actually visible.

Confirm and create one paid request

Planning, cover copy direction, and accessible-media inspection are free. Image generation, transform, and focused edit are paid. Use only this package's bundled scripts/mcp_client.py for remote Beatra operations. Do not configure or call a host Beatra Connector, and do not use REST/OpenAPI as a fallback.

Upload local images through the bundled client and keep their roles in their real input order. Uploading does not inspect media, so make visual claims only from media the host can access. Keep model: "auto" and count: 1 unless the user selects another admitted route. Read beatra.models.list before a real availability, compatibility, control, or price decision.

Before a paid request, show and freeze the route, prompt, exact canvas, image roles and order, any source-derived last-image canvas anchor, headline treatment, must-keeps, selected model and controls, count: 1, current billing basis, maximum cost, paid call count, review plan, and one fresh opaque stable client_request_id. Submit it once after explicit approval. A changed prompt, source, source order, canvas, model, count, or control is new paid work requiring a new confirmation and ID.

Track, review, and deliver

Save the task_id and poll only that task with beatra.tasks.get. If the ID is lost, use beatra.tasks.list, inspect candidates with beatra.tasks.get, and compare the retained payload before considering a retry. Replay only a byte-identical payload with the original ID when the initial creation response is genuinely unknown. Slow polling, connection, update, or authorization errors never justify another paid request.

Use beatra.tasks.cancel only when the user asks. If it returns 409, keep polling the original task and report its terminal state. When a result is visible, review focal recognition, safe-area contrast, crop risk, confirmed canvas, must-keep details, and any requested visible text. Deliver artifact links, observed dimensions, task ID, resolved model, and returned billing.net_charged_credits.

For payload shapes, live-card decisions, confirmation, recovery, and result checks, use the WeChat Channels cover workflow.

References by task

• WeChat Channels cover workflow: routes, payloads, confirmations, polling, and review.
• Installation and authentication and installation registration: first use, authorization, and package registration.
• Tasks and results and billing, errors, and recovery: task facts, artifacts, billing, and errors.
• Bundled MCP Client diagnostics: client operation and connection recovery.
• Automatic updates and safety and uninstall and disconnect: package update controls and removal.

Runtime and safe automatic updates

The bundled client silently checks for a newer release at most once every 24 hours per installation. When a higher version is available, it installs it automatically without separate confirmation. It downloads only from fixed official Beatra discovery and immutable CDN paths for this package, channel, and locale; verifies discovery data, archive, manifest, and every file before replacement; and replaces only package-owned files. Update checks, downloads, verification, replacement, and rollback fail open: the current installation remains usable and the original command continues. This setting persists for later commands.

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

--auto off disables silent checks, --auto on restores them, and --check reports the official available version without replacing files. See automatic updates and safety.