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muapi-youtube-thumbnail

@samuraigpt · 收录于 1 周前 · 上游提交 3 周前

Design a high-CTR YouTube thumbnail — striking imagery, bold text placement, and emotional face/subject if needed.

适合你,如果你需要制作吸引眼球的视频封面来提升点击率

/ 下载安装
muapi-youtube-thumbnail.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add samuraigpt/generative-media-skills/muapi-youtube-thumbnail
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- samuraigpt/generative-media-skills/muapi-youtube-thumbnail
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify samuraigpt/generative-media-skills/muapi-youtube-thumbnail
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 a1c4c98 · 表述以原文为准
它做什么

安装后,Claude 能根据视频标题和频道风格,生成一张高点击率的 YouTube 缩略图(16:9),并给出文字叠加建议。

什么时候触发

当用户要求设计 YouTube 缩略图,或提到“youtube thumbnail”、“thumbnail”等关键词时触发。

装好后可以这样说
Claude 会生成缩略图并给出文字建议。
Claude 会根据频道风格生成对应缩略图。
技能原文 SKILL.md作者撰写 · MIT · a1c4c98

YouTube Thumbnail

Design a high-CTR YouTube thumbnail — striking imagery, bold text placement, and emotional face/subject if needed.

Inputs

| Name | Type | Required | Default | Description | |:---|:---|:---|:---|:---| | title | text | yes | — | The video title or topic (e.g. "I tried 7 AI tools in 24 hours — here's what happened"). | | channel_style | text | no | bold, high contrast, bright colors, clean design, YouTube tech aesthetic | Channel brand style (e.g. "dark moody gaming", "bright educational", "minimal corporate"). | | subject_description | text | no | — | Optional description of the person or subject to feature (e.g. "a surprised young man in a hoodie"). |

Steps

Thumbnails are the #1 factor in YouTube CTR. Generate a single, maximum-impact 16:9 image.

Phase A — Plan the composition

Before generating, briefly reason about the best thumbnail formula for this topic:

  • Emotion-first: shocked/curious face if relevant + bold text = high CTR
  • Text overlay: 3–5 words max, high-contrast (white/yellow on dark, or vice-versa)
  • Contrast & saturation: thumbnails compete in a grid — they must pop
Phase B — Generate the thumbnail
  1. Build the image generation prompt:
  2. Subject: {{subject_description}} if provided, otherwise design an object/scene that dramatizes the topic.
  3. Mood: derives from {{channel_style}}.
  4. Composition: rule-of-thirds, subject on left or right with empty space for text.
  5. Style tags: {{channel_style}}, youtube thumbnail composition, ultra detailed, vibrant, high contrast, 16:9.
  6. Call muapi image generate (model=gpt-image-2-text-to-image, aspect_ratio=16:9).
Phase C — Text overlay guidance

After generation, return:

  • Suggested overlay text: 3–5 bold words that complement the title {{title}}.
  • Text placement: where on the canvas to position text (e.g. "bold yellow text, top-right third").
  • Font recommendation: style suggestion (e.g. "Impact-style all-caps with black outline").
Notes
  • Never put too much text in the prompt — text rendering in image models is unreliable. Guide the user on adding text in post-production (Canva, Photoshop).
  • If the user already has a channel image or face photo in the session, use muapi image edit to incorporate it.
  • Suggest A/B variants only if the user asks.
Trigger Keywords

youtube thumbnail, yt thumbnail, thumbnail, video thumbnail, youtube cover


Notes for the Executing Agent
  • This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
  • For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
  • Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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