Agent skill · Audited from source

ai-image-prompts-skill

skill · youmind-openlab/ai-image-prompts-skillMIT licenceAudited 21 Oct 2026 · source 616d69e44b3c

★ 1,315pushed today
✓SAFEGrade B · 89/100 · 21 Oct 2026 · 616d69e
1,315repo stars · whole youmind-openlab/ai-image-prompts-skill

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 616d69e44b3c and observed network access and no hard-coded secrets. What it does: 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…

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Cite this page

ModelsAtlas. “ai-image-prompts-skill (youmind-openlab/ai-image-prompts-skill)”. Retrieved 21 Oct 2026. https://modelsatlas.com/skills/youmind-openlab/ai-image-prompts-skill

Trust audit

Audit 0.4.1 · source read 21 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium2 low5 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 13,985 of 14,816 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkDeclared3 observations
  • ShellNone observedNo commands run in the source
  • DependenciesNot pinnedSome versions float
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS6
L1 Static sourceSecrets, path traversal, supply chain, hidden codePASS1
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (7)

LOWInventory / provenanceinv.hidden_file · .clawhubignore
F-13 Inventory / provenanceCWE-1104Layer L0
.clawhubignore
Why
hidden member outside the usual dotfiles
Fix
review its purpose
LOWSupply chainsupply.unpinned · package.json
F-08 Supply chainCWE-829 · CWE-1357Layer L1
dotenv, qs-esm, @types/node, tsx, typescript
Why
5 dependency range(s) float
Fix
pin exact versions or ship a lockfile
INFOInventory / provenanceinv.oversize · references/comic-storyboard.json
F-13 Inventory / provenanceCWE-1104Layer L0
references/comic-storyboard.json
Why
1015568 bytes not read
Fix
—
INFOInventory / provenanceinv.oversize · references/ecommerce-main-image.json
F-13 Inventory / provenanceCWE-1104Layer L0
references/ecommerce-main-image.json
Why
1085748 bytes not read
Fix
—
INFOInventory / provenanceinv.oversize · references/game-asset.json
F-13 Inventory / provenanceCWE-1104Layer L0
references/game-asset.json
Why
1112805 bytes not read
Fix
—
INFOInventory / provenanceinv.oversize · references/others.json
F-13 Inventory / provenanceCWE-1104Layer L0
references/others.json
Why
1259248 bytes not read
Fix
—
INFOInventory / provenanceinv.oversize · references/poster-flyer.json
F-13 Inventory / provenanceCWE-1104Layer L0
references/poster-flyer.json
Why
1908486 bytes not read
Fix
—

Audit 0.4.1 · source sha 616d69e44b3c · 21 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

  • Publisheryoumind-openlab GitHub organizationOwner of the repository this skill is read from
  • Source folderyoumind-openlab/ai-image-prompts-skillThe exact folder we audited
  • LicenceMITAs the repository states it
  • Commit pinned for this verdict616d69e44b3cA new audit runs when the source changes
  • Repository statusNot archivedPushed today
  • Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

What it tells the agent

Full source on GitHub ↗

Its description: 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…

---
name: ai-image-prompts-skill
description: |
  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.

  Use this skill when users want to:
  - Find proven image generation prompts (any model)
  - Get prompt inspiration for portraits, products, social media, posters, etc.
  - Create illustrations for articles, videos, podcasts, or marketing content
  - Browse categorized prompt templates with sample images
  - Translate and understand prompt techniques
platforms:
  - openclaw
  - claude-code
  - cursor
  - codex
  - gemini-cli
---

> 📖 Prompts curated by [YouMind](https://youmind.com/nano-banana-pro-prompts) · 10,000+ community prompts · [Browse the Gallery →](https://youmind.com/nano-banana-pro-prompts)

# 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 send `sourceMedia[0]` as an image
- If `sourceMedia` is 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

1. **Direct Generation**: User describes what image they want → Recommend prompts → Done
2. **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:
```bash
node scripts/setup.js
```

**Keep references up to date** (GitHub syncs community prompts twice daily):
```bash
# Force pull latest references (recommended weekly)
pnpm run sync
# or equivalently
node scripts/setup.js --force
```

Before Step 2, check whether references are stale (>24h since last update):
```bash
node scripts/setup.js --check
```

This 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:

```json
// 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:
```bash
cat {SKILL_DIR}/references/manifest.json
```
Then 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 | 2143 |
| `social-media-post.json` | Social Media Post | 9836 |
| `infographic-edu-visual.json` | Infographic / Edu Visual | 606 |
| `youtube-thumbnail.json` | YouTube Thumbnail | 228 |
| `comic-storyboard.json` | Comic / Storyboard | 703 |
| `product-marketing.json` | Product Marketing | 5758 |
| `ecommerce-main-image.json` | E-commerce Main Image | 579 |
| `game-asset.json` | Game Asset | 728 |
| `poster-flyer.json` | Poster / Flyer | 1041 |
| `app-web-design.json` | App / Web Design | 242 |
| `others.json` | Uncategorized | 1107 |

<!-- 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:

1. Read `references/manifest.json` → get `categories[]` with `slug` + `title`
2. Infer the best-matching category from the `title` (e.g. "Social Media Post" → social content requests)
3. 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"

Excerpt, first 6,001 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit 616d69e

Download folder ↗
git clone https://github.com/youmind-openlab/ai-image-prompts-skill
git -C ai-image-prompts-skill checkout 616d69e44b3c

The author documents no install command, so this uses the repository directly. Our verdict covers commit 616d69e44b3c only.

Works in which agents?

What its own text says, not a test

AgentStatusMore
Claude CodeMentioned in its text, not testedSkills for Claude Code · About Claude Code
CodexMentioned in its text, not testedSkills for Codex · About Codex
CursorMentioned in its text, not testedSkills for Cursor · About Cursor
Gemini CLIMentioned in its text, not testedSkills for Gemini CLI · About Gemini CLI
OpenClawMentioned in its text, not testedSkills for OpenClaw · About OpenClaw
WindsurfMentioned in its text, not testedSkills for Windsurf · About Windsurf
“Mentioned” means the skill’s text names the agent. We have not run it there.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 616d69e44b3c

README badge

For maintainers · shows the latest audit

ModelsAtlas: SAFE BMODELSATLASSAFE · B
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Questions about ai-image-prompts-skill

What does the ai-image-prompts-skill skill do?
AI Image Prompts — 10,000+ curated prompts for any model. Works with Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and more.
Is ai-image-prompts-skill safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can ai-image-prompts-skill access on my machine?
The audit observed that it reaches the network. Each of those is consistent with what it says it does. Secrets in the source: none found.
Which assistants does ai-image-prompts-skill work with?
Its documentation mentions claude-code, codex, cursor, gemini-cli, openclaw and windsurf. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (616d69e44b3c), read on 2026-10-21. The repository is watched, and a new audit runs when it changes — this is the first audit.

Sources

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