ai-image-prompts-skill: a searchable prompt library your agent can query
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.
At a glance
- What is it?
- Ten thousand curated image prompts packaged as an installable skill, with semantic search and a remix mode that adapts a prompt to your own content. A data product with a small amount of code attached.
- Who is it for?
- The honest way to read this repository is as a retrieval index with a natural-language front end, not as prompt engineering. The curation is the product, the search is the interface, and the three named models in the description are marketing that the model-agnostic design technically supports.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 1 day ago.
- What is it written in?
- Mainly TypeScript, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on October 11, 2026, and from our analysis. They are not legal advice.
Editorial analysis
What the package actually installs
The repository is small in code and large in data. The tree holds `SKILL.md`, `_meta.json`, `package.json`, a `references/` directory, a `scripts/` directory and a `.clawhubignore`. That is the whole thing. There is no application, no server and no API.
The `package.json` at version 1.0.4 makes the mechanism explicit: the description says the library auto-downloads on install, and the scripts block confirms it with a `postinstall` hook that runs `node scripts/setup.js`. Runtime dependencies are correspondingly thin, just `dotenv` and `qs-esm`, both there to make the setup script work rather than to support any search engine. Dev dependencies are `tsx`, `typescript` and Node types, and the package manager is pinned to pnpm 9.15.9 with a full integrity hash.
So the architecture is: a small script fetches the library on install, the fetched data goes somewhere `scripts/setup.js` decides, and the agent reads it through the skill instructions. That is a meaningfully different shape from a project that vendors its dataset in git, and it has an obvious consequence. If you install this in an environment with no network access at install time, the skill installs and the library does not arrive. The README's manual sync command is the escape hatch for that case.
Installing it across four kinds of assistant
The README documents four installation routes, and the differences between them are worth reading rather than skipping to the first one. OpenClaw is the recommended path and has its own registry command:
clawhub install ai-image-promptsClaude Code installs through the `skills` runner:
npx skills i YouMind-OpenLab/ai-image-prompts-skillCursor, Codex, Gemini CLI and Windsurf use what the README calls a universal installer that auto-detects your assistant, and it happens to be the same command. A fourth path exists for anything the auto-detection misses:
npx openskills install YouMind-OpenLab/ai-image-prompts-skillTwo more details are easy to miss. The OpenClaw route also works by asking in chat, since the README suggests typing that you want the ai image prompts skill from clawhub, which tells you the registry is designed to be driven conversationally. And `.clawhubignore` exists at the repository root, which is how you keep fetched data out of a ClawHub publish.
Two modes of use, and what a good answer looks like
Mode 1 is direct search. You describe an image in a sentence, such as asking for a cyberpunk-style avatar prompt or a product photo on a white background, and you get back up to three recommendations. The shape of a recommendation is specific: a translated title and description in your language, a truncated prompt preview with a link to the full one, a sample image showing the result, and an option to customize it.
That sample image is the whole point of the design, and it is the reason this is not just a text search. A prompt is only useful if you know what it produces, and the README states that every prompt in the library includes the actual generated image. Seeing the output before copying the prompt inverts the usual workflow, where you paste a prompt into a model and spend a minute of GPU time finding out it was not what you wanted.
Mode 2 is content illustration. You paste an article or a script and the skill analyses theme, tone and audience, searches for matching templates, offers styles to pick from, then remixes the prompt with your specifics. That is the mode with real leverage, because describing an image well is a skill and most people are worse at it than they think.
The category table is the most useful thing in the README
Prompts are organised into 11 use-case categories with counts, and the distribution tells you exactly what this library is good at and where it is thin:
| Category | Count | |---|---| | Social Media Post | 6,382 | | Product Marketing | 3,709 | | Profile / Avatar | 1,064 | | Poster / Flyer | 485 | | Infographic / Edu Visual | 458 | | E-commerce Main Image | 382 | | Game Asset | 378 | | Comic / Storyboard | 290 | | YouTube Thumbnail | 173 | | App / Web Design | 167 | | Uncategorized | 910+ |
Two thirds of the library is social posts and product marketing. If your need is a game asset, a comic panel or a UI mockup, you are working with 167 to 378 entries rather than 10,000, and the promise of a broad library does not apply to you. The Uncategorized bucket is worth noting too: roughly 910 entries the curators did not place, which is a reasonable admission and a reminder that the classification is not exhaustive.
The counts also sum to well over the 10,000 headline figure in the title and description, which is consistent with a library that has grown since the copy was written. The README also claims twice-daily updates through GitHub Actions, syncing with community contributions on YouMind.com.
Keeping the library current
The README says the skill checks for updates on each use, which is a reasonable default for data that changes twice a day. Two manual commands exist for when you want to control the timing:
node scripts/setup.js --checkThe check is described as silent if the library is fresh, so it is safe to run on a schedule without generating output. Forcing a full re-sync uses the package script rather than the script directly, which is the same thing by another route:
pnpm run syncThe `package.json` confirms what sync actually is: a `sync` script defined as `node scripts/setup.js --force`. There is also a separate `generate` script running `tsx scripts/generate-references.ts`, which is how the TypeScript source under `scripts/` produces the reference files the agent reads. That separation is sensible: TypeScript for generation, plain Node for the install-time path that has to work before anything is built.
A `typecheck` script running `tsc --noEmit` is also declared, though the project has no runtime dependencies worth type checking, which suggests the discipline is there for the generator script rather than the shipped code.
Where this approach stops being useful
The central limitation is that a library curated from community posts has no style coherence. If you are building a visual identity for a product, the wrong prompt is worse than no prompt, because a beautiful sample image in someone else's aesthetic invites you to adopt a look that is not yours. Three top matches means three different visual styles, and choosing between them is the work the library refuses to do for you.
The second limitation is provenance. The prompts are sourced from creators posting results on social media, and the MIT licence covers the prompt text as the project presents it. Prompts that reproduce a named artist's style, or that encode a specific existing character's look, raise questions the README does not address. That is a genuine consideration for commercial use even though nothing about it is technical.
The third is the model-agnostic claim. The description names Nano Banana Pro, Nano Banana 2, Seedream 5.0 and GPT Image 1.5, and the README adds Midjourney, DALL-E 3, Flux and Stable Diffusion. These models interpret prompt language differently, and a prompt tuned for one is not automatically the best prompt for another. Claiming compatibility is fair; claiming optimality for all eight would not be. The last push was on 2026-09-20, and the repository has no GitHub releases, so version history lives only in the `package.json` version field.
Editorial conclusion
The honest way to read this repository is as a retrieval index with a natural-language front end, not as prompt engineering. The curation is the product, the search is the interface, and the three named models in the description are marketing that the model-agnostic design technically supports. Two things make it more honest than a typical prompt dump: the library is refreshed twice a day from a live site rather than committed once, and the category table admits an Uncategorized bucket of 910 entries, which tells you the curation is real work with real gaps. It is not a tool for building a repeatable in-house visual system, because a library assembled from social media posts has no style coherence by construction. Install it, ask it for three prompts in a category you already know well, and judge the sample images. If those are not better than what you would write yourself, the library is not adding anything.
Frequently asked questions
How many prompts are in the library and what categories do they cover?
The README headline says 10,000 or more across 11 use-case categories. The category table is weighted toward social media posts at 6,382 and product marketing at 3,709, with much smaller counts for game assets at 378, app and web design at 167, and a further 910 or more uncategorized.
Which AI assistants can I install this skill on?
The README documents OpenClaw through clawhub install ai-image-prompts, Claude Code through npx skills i, and a universal npx skills i command that auto-detects Cursor, Codex, Gemini CLI and Windsurf. A further fallback uses npx openskills install for anything auto-detection misses.
Does the skill work with Midjourney and Stable Diffusion?
It is described as model-agnostic and names Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E 3, Flux and Stable Diffusion. The prompts are stored as text without model-specific formatting, so the same prompt can be pasted into any of them.
How often does the prompt library update?
The README says the library is updated twice daily through GitHub Actions, syncing with community contributions on YouMind.com, and that the skill checks for updates on each use. For manual control there is a check command that stays silent when fresh and a sync command that forces a full refresh.
Official sources
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