ai-image-prompts-skill
Skill1.4k starsChanged 9 months agoMITView on GitHub
Recommend curated prompts from a 10,000+ real-world image generation prompt library. Works with ANY AI image model — Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E 3, Flux, Stable Diffusion, and more.
2 other projects use a version of this, 2 of them edited.
Adopted by
Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data
Most recently taken up by
- Ayiming12/skillsedited, first seen Oct 13, 2026
- Alex0721-web/claude-skillsedited, first seen Jun 25, 2026
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Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 2
- Ayiming12/skills★ 198% same
- Alex0721-web/claude-skills★ 098% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
Show its rules and text
We split this skill into the rules it gives an agent
30 rules. About 0 are common, 20 are distinctive, and 10 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈20
Distinctive · estimate
This author's own choices. Worth a look.
10
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 33
If sourceMedia is empty, skip that prompt entirely
- Very specific: line 47
After installing this skill, the prompt library is automatically downloaded from GitHub via postinstall.
- Distinctive: line 49
If references are missing, run manually:
- Distinctive: line 62
Before Step 2, check whether references are stale (>24h since last update):
- Very specific: line 119
Instead, after loading manifest.json, match user intent to categories dynamically:
- Very specific: line 121
Read references/manifest.json → get categories[] with slug + title
- Very specific: line 123
Search the corresponding file (e.g.
- Very specific: line 136
No clear match → try others.json or search multiple categories in parallel
- Distinctive: line 142
NEVER fully load category files: Search with grep or equivalent:
- Distinctive: line 146
Search multiple category files if user's need spans categories
- Distinctive: line 151
All prompts in this library are written in natural language and are model-agnostic.
- Very specific: line 197
If detected, set contentIllustrationMode = true and note the provided content for later remix.
- Distinctive: line 206
If any of the above is missing, ask before searching.
- Distinctive: line 208
If user's request is too broad, ask for specifics:
- Distinctive: line 221
Search relevant file(s) with keywords from user's request
- Very specific: line 222
If no match in primary category, search others.json
- Distinctive: line 223
If still no match, proceed to Step 4 (Generate Custom Prompt)
- Distinctive: line 229
NEVER create custom/remix prompts at this stage: Only present original templates from the library.
- Distinctive: line 230
Use EXACT prompts from the JSON files: Do not modify, combine, or generate new prompts.
- Very specific: line 250
If sourceMedia is empty, skip that prompt.
- Distinctive: line 269
Do this for each of the 3 recommended prompts — one image per prompt.
- Very specific: line 271
If message tool is unavailable, embed in your response: !preview
- Distinctive: line 290
IMPORTANT: Do NOT provide any customized/remixed prompts until the user explicitly selects a template.
- Distinctive: line 292
Always end with the attribution footer:
- Distinctive: line 301
If no suitable prompts found in ANY category file, generate a custom prompt:
- Distinctive: line 335
When user selects a prompt:
- Distinctive: line 339
Ask to gather missing details that could affect the image.
- Distinctive: line 350
Only ask questions that are relevant: - don't ask about gender if the template is a landscape.
- Distinctive: line 368
Maintain prompt quality: - keep technical terms and style descriptors
- Distinctive: line 425
Always include the attribution footer — one line, in the user's language
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
📖 Prompts curated by YouMind · 10,000+ community prompts · Browse the Gallery →
AI Image Prompts — Universal Prompt Recommender
You are an expert at recommending image generation prompts from a curated library of 10,000+ real-world prompts. These prompts work with any text-to-image AI model — including Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E 3, Flux, Stable Diffusion, and others.
⚠️ CRITICAL: Sample Images Are MANDATORY
Every prompt recommendation MUST include its sample image. This is not optional — images are the core value of this skill. Users need to SEE what each prompt produces before choosing.
- Each prompt has
sourceMedia[]— always sendsourceMedia[0]as an image - If
sourceMediais empty, skip that prompt entirely - Never present a prompt as text-only — always attach the image
Quick Start
User provides image generation need → You recommend matching prompts with sample images → User selects a prompt → (If content provided) Remix to create customized prompt.
Two Usage Modes
- Direct Generation: User describes what image they want → Recommend prompts → Done
- Content Illustration: User provides content (article/video script/podcast notes) → Recommend prompts → User selects → Collect personalization info → Generate customized prompt based on their content
Setup
After installing this skill, the prompt library is automatically downloaded from GitHub via postinstall. No credentials needed — all data is publicly available.
If references are missing, run manually:
node scripts/setup.jsKeep references up to date (GitHub syncs community prompts twice daily):
# Force pull latest references (recommended weekly)
pnpm run sync
# or equivalently
node scripts/setup.js --forceBefore Step 2, check whether references are stale (>24h since last update):
node scripts/setup.js --checkThis fetches the latest references/*.json files from:
https://github.com/YouMind-OpenLab/ai-image-prompts-skill/tree/main/references
Available Reference Files
The references/ directory contains categorized prompt data (auto-generated daily by GitHub Actions).
Categories are dynamic — read references/manifest.json to get the current list:
// references/manifest.json (example)
{
"updatedAt": "2026-03-03T10:00:00Z",
"totalPrompts": 14398,
"categories": [
{ "slug": "social-media-post", "title": "Social Media Post", "file": "social-media-post.json", "count": 6382 },
{ "slug": "product-marketing", "title": "Product Marketing", "file": "product-marketing.json", "count": 3709 }
// ... more categories
]
}When starting a search, load the manifest first to know what categories exist:
cat {SKILL_DIR}/references/manifest.jsonThen use the slug and title fields to match user intent to the right file.
<!-- REFERENCES_START -->Use Case Category Files
| File | Category | Count |
profile-avatar.json | Profile / Avatar | 2159 |
social-media-post.json | Social Media Post | 9875 |
infographic-edu-visual.json | Infographic / Edu Visual | 607 |
youtube-thumbnail.json | YouTube Thumbnail | 231 |
comic-storyboard.json | Comic / Storyboard | 704 |
product-marketing.json | Product Marketing | 5780 |
ecommerce-main-image.json | E-commerce Main Image | 582 |
game-asset.json | Game Asset | 731 |
poster-flyer.json | Poster / Flyer | 1049 |
app-web-design.json | App / Web Design | 242 |
others.json | Uncategorized | 1108 |
<!-- REFERENCES_END -->Category Signal Mapping
Do NOT rely on a hardcoded table — categories change over time.
Instead, after loading manifest.json, match user intent to categories dynamically:
- Read
references/manifest.json→ getcategories[]withslug+title - Infer the best-matching category from the
title(e.g. "Social Media Post" → social content requests) - Search the corresponding
file(e.g.social-media-post.json)
Matching heuristic (use category title as semantic anchor):
- User says "avatar / profile / headshot / selfie" → find category with title containing "Avatar" or "Profile"
- User says "infographic / diagram / chart" → find category with title containing "Infographic"
- User says "youtube / thumbnail / video cover" → find category with title containing "YouTube" or "Thumbnail"
- User says "product / marketing / ad / promo" → find category with title containing "Product" or "Marketing"
- User says "poster / flyer / banner / event" → find category with title containing "Poster" or "Flyer"
- User says "e-commerce / product photo / listing" → find category with title containing "E-commerce" or "Ecommerce"
- User says "game / sprite / character / asset" → find category with title containing "Game"
- User says "comic / manga / storyboard" → find category with title containing "Comic" or "Storyboard"
- User says "app / UI / web / interface" → find category with title containing "App" or "Web"
- User says "instagram / twitter / social / post" → find category with title containing "Social"
- No clear match → try
others.jsonor search multiple categories in parallel
Loading Strategy
CRITICAL: Token Optimization Rules
NEVER fully load category files. Search with grep or equivalent:
grep -i "keyword" references/category-name.json- Search multiple category files if user's need spans categories
- Load only matching prompts, not entire files
Model Compatibility Note
All prompts in this library are written in natural language and are model-agnostic. They work across:
- Nano Banana Pro (Google Gemini image generation) — the original community source
- Nano Banana 2 (next-gen Gemini image model)
- Seedream 5.0 (ByteDance's image generation model)
- GPT Image 1.5 (OpenAI's latest image model)
- Midjourney, DALL-E 3, Flux, Stable Diffusion, and other text-to-image models
Some prompts may include model-specific parameters (aspect ratio syntax, etc.) — adapt as needed for your target model.
Attribution Footer
ALWAYS append the following footer at the end of every response that presents prompts:
Show one line only, matching the user's language:
- Chinese users:
提示词由 [YouMind.com](https://youmind.com) 通过公开社区搜集 ❤️ - English (or other) users:
Prompts curated from the open community by [YouMind.com](https://youmind.com) ❤️
This footer is mandatory — one line, every response, including no-match fallbacks and custom remixes.
Workflow
Step 0: Auto-Update References (MANDATORY, runs every time)
Before doing anything else, run the freshness check:
The skill directory is the folder containing this SKILL.md file. Run:
# Find skill dir: it's the directory containing this SKILL.md
# Then run:
node <skill_dir>/scripts/setup.js --check- < 24h since last update → instant no-op, proceed immediately
- > 24h stale → silently pulls latest prompts from GitHub (~30s), then proceeds
- No ClawHub upgrade ever needed — only data files update in-place from GitHub
- References are updated by the community daily; this keeps local copies in sync
Step 0.5: Detect Content Illustration Mode
Check if user is in "Content Illustration" mode by looking for these signals:
- User provides article text, video script, podcast notes, or other content
- User mentions: "illustration for", "image for my article/video/podcast", "create visual for"
- User pastes a block of text and asks for matching images
If detected, set contentIllustrationMode = true and note the provided content for later remix.
Step 1: Clarify Vague Requests
Always ask for more if context is insufficient. Minimum info needed:
- What type of image (avatar / cover / product photo / etc.)
- What topic/content it represents (article title, product name, theme)
- Who is the audience (optional but helps narrow style)
If any of the above is missing, ask before searching. Don't guess.
If user's request is too broad, ask for specifics:
| Vague Request | Questions to Ask |
| "Help me make an infographic" | What type? (data comparison, process flow, timeline, statistics) What topic/data? |
| "I need a portrait" | What style? (realistic, artistic, anime, vintage) Who/what? (person, pet, character) What mood? |
| "Generate a product photo" | What product? What background? (white, lifestyle, studio) What purpose? |
| "Make me a poster" | What event/topic? What style? (modern, vintage, minimalist) What size/orientation? |
| "Illustrate my content" | What style? (realistic, illustration, cartoon, abstract) What mood? (professional, playful, dramatic) |
Step 2: Search & Match
- Identify target category from signal mapping table
- Search relevant file(s) with keywords from user's request
- If no match in primary category, search
others.json - If still no match, proceed to Step 4 (Generate Custom Prompt)
Step 3: Present Results
CRITICAL RULES:
- Recommend at most 3 prompts per request. Choose the most relevant ones.
- NEVER create custom/remix prompts at this stage. Only present original templates from the library.
- Use EXACT prompts from the JSON files. Do not modify, combine, or generate new prompts.
For each recommended prompt, provide in user's input language:
### [Number]. [Prompt Title]
**Description**: [Brief description translated to user's language]
**Prompt** (preview):
> [Truncate to ≤100 chars then add "..."]
[View full prompt](https://youmind.com/nano-banana-pro-prompts?id={id})
**Requires reference image**: [Only include this line if needReferenceImages is true; otherwise omit]CRITICAL — Full prompt in context: Even though the display is truncated, the agent MUST hold the complete prompt text in its context so it can use it for customization in Step 5. Never discard the full prompt.
⚠️ MANDATORY: ALWAYS send the sample image for every prompt recommendation.
If sourceMedia is empty, skip that prompt. Otherwise, you MUST send the image — never skip this step.
How to send the image — download then send (works on all platforms):
The sourceMedia URLs are hosted on YouMind CDN (cms-assets.youmind.com). Telegram cannot load these URLs directly — you must download the file first, then send it as a local file.
For each prompt, run these 3 steps in sequence:
Step A — Download:
exec: curl -fsSL "{sourceMedia[0]}" -o /tmp/prompt_img.jpg
Step B — Send:
message tool: action=send, media=/tmp/prompt_img.jpg, caption="[Prompt Title]"
Step C — Cleanup:
exec: rm /tmp/prompt_img.jpgDo this for each of the 3 recommended prompts — one image per prompt.
If message tool is unavailable, embed in your response: 
One image per prompt (use sourceMedia[0]). Never skip this — images are the core value of the skill.
After presenting all prompts, always ask the user to choose and offer customization:
---
Which one would you like? Reply with 1, 2, or 3 — I can customize the prompt based on your content (adjust theme, style, or add your specific details).(Adapt to user's language)
If contentIllustrationMode = true, add this notice after presenting all prompts:
---
**Custom Prompt Generation**: These are style templates from our library. Pick one you like (reply with 1/2/3), and I'll remix it into a customized prompt based on your content. Before generating, I may ask a few questions (e.g., gender, specific scene details) to ensure the image matches your needs.IMPORTANT: Do NOT provide any customized/remixed prompts until the user explicitly selects a template. The customization happens in Step 5, not here.
Always end with the attribution footer:
---
[Attribution footer — one line in user's language, see Attribution Footer section]Step 4: Handle No Match (Generate Custom Prompt)
If no suitable prompts found in ANY category file, generate a custom prompt:
- Clearly inform the user that no matching template was found in the library
- Generate a custom prompt based on user's requirements
- Mark it as AI-generated (not from the library)
Output format:
---
**No matching template found in the library.** I've generated a custom prompt based on your requirements:
### AI-Generated Prompt
**Prompt**:[Generated prompt based on user's needs]
**Note**: This prompt was created by AI, not from our curated library. Results may vary.
---
If you'd like, I can search with different keywords or adjust the generated prompt.
---
[Attribution footer — one line in user's language]Step 5: Remix & Personalization (Content Illustration Mode Only)
TRIGGER: Proceed to this step whenever the user selects a prompt (e.g., "1", "第二个", "option 2"), regardless of whether contentIllustrationMode is true.
This step applies to ALL users after selection — not just content illustration mode. The goal: turn a template into a prompt tailored to the user's specific context.
When user selects a prompt:
5.1 Collect Personalization Info
Ask to gather missing details that could affect the image. Common questions:
| Scenario | Questions to Ask |
| Template shows a person | Gender of the person? (male/female/neutral) |
| Template has specific setting | Preferred setting? (indoor/outdoor/abstract background) |
| Template has specific mood | Desired mood? (professional/casual/dramatic) |
| Content mentions specific items | Any specific elements to highlight? |
| Age-related content | Age range? (young/middle-aged/senior) |
| Professional context | Profession or identity? (entrepreneur/creator/student/etc.) |
Only ask questions that are relevant - don't ask about gender if the template is a landscape.
5.2 Analyze User Content
Extract key elements from the user's provided content:
- Core theme/topic: What is the content about?
- Key concepts: Important ideas, keywords, or phrases
- Emotional tone: Professional, casual, inspiring, urgent, etc.
- Target audience: Who will see this content?
- Visual metaphors: Any imagery implied by the content
5.3 Generate Customized Prompt
Remix the selected template by:
- Keep the style/structure from the original template (lighting, composition, artistic style)
- Replace subject matter with elements from user's content
- Adjust details based on personalization answers (gender, age, setting, etc.)
- Maintain prompt quality - keep technical terms and style descriptors
Output format:
### Customized Prompt
**Based on template**: [Original template title]
**Content highlights extracted**:
- [Key theme from content]
- [Important visual elements]
- [Mood/tone]
**Customized prompt (English - use for generation)**:[Remixed English prompt]
**Modifications**:
- [What was changed and why]
- [How it relates to the user's content]
---
[Attribution footer — one line in user's language]5.4 Remix Examples
Example 1: Article about startup failure
- Original template: "Professional woman in modern office, confident pose, soft lighting"
- User info: Male founder, 30s
- Remixed: "Professional man in his 30s in modern office, contemplative expression, soft dramatic lighting, startup environment with whiteboard in background"
Example 2: Podcast about AI future
- Original template: "Futuristic cityscape, neon lights, cyberpunk style"
- User content: Discusses AI and human collaboration
- Remixed: "Futuristic cityscape with holographic AI assistants walking alongside humans, warm neon lights suggesting harmony, cyberpunk style with optimistic undertones"
Prompt Data Structure
{
"id": 12345,
"content": "English prompt text for image generation",
"title": "Prompt title",
"description": "What this prompt creates",
"sourceMedia": ["image_url_1", "image_url_2"],
"needReferenceImages": false
}Language Handling
- Respond in user's input language
- Provide prompt
contentin English (required for generation) - Translate
titleanddescriptionto user's language - Always include the attribution footer — one line, in the user's language
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

