Cover Performance Preflight

Specific Problem

When publishing videos, articles, or podcasts, cover images serve as the visual entry point to attract user clicks. However, many designers and creators face concrete issues during optimization: visual hooks (such as focal imagery or color contrast) are not prominent, leading to scattered attention; focus hierarchy is confusing, preventing quick comprehension of core information; titles and visuals are poorly matched, causing inconsistency; text safe zones are undefined, resulting in key text being lost during cropping or thumbnail display; and mobile thumbnail readability is poor, especially at small sizes. These problems often stem from subjective judgment or lack of experience, without systematic preflight mechanisms, and are hard to quantify, leading to unstable performance in actual deployments.

How It Works

This skill provides a structured preflight check and candidate creation workflow by uploading existing video thumbnails, social covers, article headers, or podcast covers.

• Preflight Check: First, collect the cover image, title or core information, target channel, audience, and brand elements to generate a concise check card. The card covers visual hooks, focus hierarchy, title-visual matching, text safe zones, mobile thumbnail readability, and cropping adaptation, along with prioritized modification suggestions. Users can choose a direction from different options as the basis for subsequent creation.
• Optional Baseline Query: When connections are open, the skill can query search results on platforms like YouTube, TikTok, Douyin, and Xiaohongshu for similar topics, fetching titles and public data (only YouTube can return cover images). This step is optional and paid, requiring separate price confirmation and per-query billing. The query results illustrate the competitive environment of the cover but do not constitute any click-rate prediction or score commitment.
• Candidate Creation: After user approval, use beatra.images.transform to create two to three comparable candidates (count: 2 or count: 3). For local edits, beatra.images.edit can be used, placing the original image in images[0]. The model defaults to auto; for capabilities, canvas, controls, or pricing, query beatra.models.list. All remote operations are completed exclusively via the bundled client scripts/mcp_client.py, ensuring no reliance on host Beatra Connector or other fallbacks.

Boundaries and Caveats

This skill has clear usage boundaries and operational constraints that must be understood beforehand:
- Client Restrictions: All uploads and remote operations must use the bundled client; do not configure the host Beatra Connector or use REST/OpenAPI degradation. Single WeChat covers can be forwarded to wechat-cover-maker, Xiaohongshu covers to rednote-cover-maker, and poster designs to poster-design-studio.
- Baseline Query Limitations: Baseline queries only cover seven specific whitelisted operations. Other platforms like official account article covers, Channels covers, Bilibili covers, or podcast covers have no baseline data. When queries return numbers, titles, and links, if the host cannot open the cover image, only titles and data are recorded without visual analysis. Each query requires real-time pricing and confirmation, with costs varying by platform (e.g., Douyin and Xiaohongshu are ten times more expensive than TikTok).
- Payment and Recovery: Checks and planning are free, but baseline queries and candidate creation involve payments. Each paid creation requires displaying precise prompts (including original cover, candidate direction, canvas, model, quantity, and real-time price) and obtaining explicit approval. After submission, record task_id and track with beatra.tasks.get; if the identifier is lost, search via beatra.tasks.list and replay with the identical request identifier. Any changes to cover, title, model, or canvas are considered new paid requests, requiring re-approval.
- No Performance Promises: This skill provides diagnostics and candidate creation but makes no promises about click-rate results, scores, or performance predictions unverified by real deployments. Check cards and baseline data are for reference only; final publishing decisions are the user's responsibility.