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Social Cover Director

CommunityPopular
Jamailar
social-cover-director

Use when the user wants to plan or generate a social media cover image for a Xiaohongshu note, Douyin/TikTok photo post, Reels/Shorts cover, WeChat/Video Account post, Bilibili/Weibo post, Pinterest pin, Instagram cover, Facebook/Meta social post, or similar creator/social content. Handles platform-native cover routing, aspect ratio, scroll-stopping hook, exact on-image copy, reference-image roles, and image.generate prompt construction for one cover or a small variant set.

Overview

PublisherJamailar
RepositoryBeav
Skill namesocial-cover-director
Stars
1.7K
Forks
225
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Jamailar on GitHub. Read the source before you install it.

Installation

Install the Social Cover Director AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Jamailar/Beav.git /tmp/Beav
mkdir -p .claude/skills
cp -r /tmp/Beav/desktop/builtin-skills/social-cover-director .claude/skills/social-cover-director
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Social Cover Director in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Social Cover Director on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Social Cover Director is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Social Cover Director

Use this skill to make one social-media cover image, or a small set of cover variants, for creator content.

It adapts the social-promo planning flow to RedBox:

text
content/topic + platform/surface
-> reader promise + scroll-stopping hook
-> cover type + aspect ratio
-> attention mechanic + visual style pack
-> exact on-image copy
-> reference-image role map
-> image.generate prompt
-> text/identity/social-native QA

Core Stance

  • The cover must stop the scroll before it explains the whole content.
  • Prefer a strong human-readable promise, contrast, clear subject, clean title zone, and platform-native composition.
  • Do not make a catalog poster unless the user explicitly wants a product-commerce cover.
  • Do not force a product, person, or logo into the cover if the stronger hook is a mood, problem scene, cultural moment, checklist, or visual metaphor.
  • Exact on-image copy is a hard boundary. Use only confirmed cover text; do not let the image model invent extra labels.
  • If the user wants a full carousel, multi-card note, ecommerce image set, or detail-page images, route to image-director instead.

Required Inputs

Useful inputs:

  • Content source: draft note, topic, title, outline, pasted article, video idea, product facts, or campaign brief.
  • Platform/surface: 小红书笔记, 抖音图文, Douyin/TikTok/Reels/Shorts cover, 微信朋友圈, 视频号, 微博, B站动态, Instagram, Pinterest, Facebook/Meta.
  • Market and on-image language, when not obvious.
  • Reference images, when available.

If platform is missing and the content is Chinese creator content, default to 小红书笔记. If content itself is missing, ask for the topic/draft first. If only market/language is missing, infer from the user language unless the final usage seems overseas.

Reference Loading

These references are bundled with this skill and may be used directly:

  • references/cover-routing.md: platform, surface, aspect ratio, and variant count.
  • references/attention-design-router.md: attention mechanics and style packs.
  • references/image-generate-prompt-contract.md: RedBox image.generate prompt contract.

Workflow

  1. Extract the content promise:
    • target reader
    • core tension or curiosity
    • desired click reason
    • one thing the cover must make obvious
  2. Route platform and surface.
  3. Pick cover type:
    • note_cover
    • short_video_cover
    • community_post_cover
    • article_cover
    • pin_cover
    • ad_cover
    • cover_variant_set
  4. Choose aspect ratio and count:
    • If user asks for one cover, produce one.
    • If user asks for variants, use 2-4 variants unless a platform/test brief justifies more.
    • If the current context says noSecondConfirmation: true, plan silently and generate after self-check.
  5. Map references:
    • subject_identity: must preserve person/product/place identity.
    • style_reference: learn composition, typography, color, mood.
    • base_image: transform this image into a cover.
    • content_context: use only for understanding, not visual copying.
  6. Choose one main attention mechanic and optional secondary mechanic.
  7. Draft exact on-image copy:
    • one main headline
    • optional subtitle
    • optional badge/label
    • optional short proof/CTA
  8. In normal chat/redclaw flow, show the Phase 1 plan and wait for confirmation before generation.
  9. After confirmation, call Operate(resource="image", operation="generate", input={ ... }).
  10. After generation, run QA and flag rerun candidates.

Phase 1 Output

Before generation, output in Chinese:

  • 封面路由判断: platform, surface, cover type, aspect ratio, count, language.
  • 内容钩子: target reader, core tension, click promise.
  • 视觉策略: attention mechanic, style pack, subject/reference use, title safe area.
  • 封面文案确认: exact text that may appear on the image.

Use this table for one cover or variants:

markdown
|| 角色 | 吸睛机制 | 画面策略 | 图片文字(待确认) |
|---|---|---|---|---|
| 1 | primary_cover | oversized_headline + human_gaze | ... | `主标题`<br>`副标题` |

End normal Phase 1 with:

text
你确认或改完这些封面文字、张数和画面方向后,我再开始生成。

Do not ask for confirmation when the runtime context explicitly says noSecondConfirmation: true, backgroundExecution: true, or mustNotAskForConfirmation: true. In that case, do the same planning internally, self-critique, then generate.

Generation Contract

For one cover, call image.generate with a single compiledPrompt or prompt.

For multiple distinct cover variants, call image.generate once with imagePlanItems; each item must have its own compiledPrompt.

Payload requirements:

  • prompt: overall cover brief or the single final prompt.
  • compiledPrompt: required for a single cover when the prompt is already final.
  • count: number of covers or variants.
  • aspectRatio: required. Use the routed platform ratio.
  • quality: use current app default unless the user asks for high quality.
  • referenceImages: include only relevant references.
  • planConfirmed: true after user confirmation, or when explicit runtime context allows no second confirmation.
  • imagePlanItems: use for variant sets; title is internal only.

Never put planning labels, image numbers, role names, table headers, or hidden reasoning in visible text.

Post-Generation QA

Check:

  • confirmed text only; no extra text or pseudo text
  • headline readable at mobile feed size
  • cover has a strong first-glance hook
  • subject identity preserved when references require it
  • product/person absence is intentional, not accidental
  • image does not look like a generic stock poster or marketplace product card unless requested

Output a compact QA table:

markdown
|| QA | 建议 |
|---|---|---|
| 1 | 通过 / 需复核:... | 保留 / 建议重跑 |

Then ask which image numbers to rerun, if any.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Social Cover Director AI skill do?

Use when the user wants to plan or generate a social media cover image for a Xiaohongshu note, Douyin/TikTok photo post, Reels/Shorts cover, WeChat/Video Account post, Bilibili/Weibo post, Pinterest pin, Instagram cover, Facebook/Meta social post, or similar creator/social content. Handles platform-native cover routing, aspect ratio, scroll-stopping hook, exact on-image copy, reference-image roles, and image.generate prompt construction for one cover or a small variant set.

Why use Social Cover Director on TypingMind?

Because you install it once and use it with any model. Social Cover Director is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Social Cover Director in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jamailar/Beav/tree/main/desktop/builtin-skills/social-cover-director. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Social Cover Director?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Social Cover Director?

As many as you like. As long as a model supports skills, you can use Social Cover Director with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Social Cover Director AI skill free?

It is published on GitHub by Jamailar. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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