nanobanana-trending-prompts: 1,446 curated AI image prompts from X

A prompt corpus harvested from X, not written from scratch

nanobanana-trending-prompts is a data repository rather than a piece of software. The project page describes it as 1,400 plus curated AI image prompts from X and Twitter, grouped by model and ranked by engagement, with a stated compatibility list of GPT Image 2, NanoBanana Pro, Midjourney V7 and more. The primary language field is empty and the top level of the repository holds only a LICENSE file, two READMEs, an assets folder and a prompts folder, which tells you everything about the shape of the thing.

Three ways in are offered. There is a JSON download at prompts/prompts.json, a gallery at meigen.ai for browsing and filtering with a ranking view the raw file does not provide, and a system prompt in prompts/system-prompt-en.md that is a separate artifact from the dataset.

The intended user is someone producing images with a current generation model who wants a starting structure rather than a blank prompt box. The data is a snapshot, curated by hand, and the last push to the repository was on 2026-04-29, which is also the date of the most recent changelog entry. The repository publishes no GitHub releases, so a later pull of prompts.json can hand you a different set under the same version number, which is worth remembering if you cache the file.

Engagement ranking is a popularity proxy, not a quality score

The ordering is by engagement, and the entries show the numbers behind it. The top GPT Image prompt is credited to @TechieBySA with 3,567 likes and 159,772 views, and the second is credited to @mehvishs25 with 1,776 likes and 111,208 views. Each entry also carries a model label and a category list.

This is a useful signal for one narrow purpose: telling you which prompt formats circulate widely among people who publish their image outputs. It is not a quality measure, and the gap between the counts shows that. A prompt with a hundred thousand views is not necessarily better written than the entry below it, it is only more visible. If you are choosing a prompt to imitate, reading the text matters more than the rank.

The entries are attributed to individual accounts, and the credits are the reason the dataset is worth treating with some care. The prompt text is a transcription of something a person posted, and the repository is a collection of other people's prompts, which is a fact the licence section has to carry.

One JSON file, six categories and a missing search index

Everything ships as prompts/prompts.json, and the project page advertises a count of 1,446. The split by model is 1,148 NanoBanana and 298 GPT Image. Every prompt belongs to one or more of six categories, and the counts given are Photography 533, Illustration and 3D 370, Product and Brand 239, Food and Drink 156, Poster Design 146, and UI and Graphic 52.

Those numbers describe what people were actually asking image models for at the time of curation. Photography and 3D render work dominate, which fits the audience of social accounts publishing visual output, while UI and Graphic at 52 entries is the smallest slice and also the newest, added in the same changelog entry that introduced Poster Design.

The changelog shows the totals moving in both directions. The dataset reached 1,542 entries on 2026-02-26 and 1,479 on 2026-02-20, then the 2026-03-09 entry records manual curation that removed 345 low quality posts, refined 120 prompts and re-categorised 228 entries, with 119 new prompts added, for a total of 1,389. The most recent entry, dated 2026-04-29, added the Poster Design and UI and Graphic categories, reorganised tagging and grouped listings by model, landing on 1,446. So the headline count is a curated subset rather than a raw scrape, and the composition of the file changes with curation decisions rather than only with new posts.

What the JSON does not give you is a search index or a taxonomy you can query. That is the gap the gallery fills, and the reason the MCP server exists, since both add browsing on top of a static file that you would otherwise have to load and filter yourself.

The prompts are written as parameter blocks, not adjectives

Reading the top ranked entry explains the curation policy better than any description of it. The prompt is a specification for a technical infographic: a 45 degree isometric 3D perspective, a photoreal render combined with black ink technical annotations on pure white, colour coded callout boxes, transparent cutaway sections, measurements and precise scale markers, and a colour code for function arrows where red means power, blue data, orange thermal and green sensors. It ends with an output size of 1080 by 1080 described as social feed optimized.

The pattern is consistent across the set. Subject, then render style, then structure and constraints, then output format. The strength of this approach is reproducibility, since a prompt with quantities in it can be tweaked in a predictable direction. The weakness is that the same specificity makes these prompts easy to mistake for a specification of your own subject, and they are written for whatever object the original author had in mind, with a placeholder for it.

One command puts the library inside your coding tool

The project is built into MeiGen MCP, an MCP server that lets you search, browse and generate images from Claude Code, Cursor, VS Code and similar tools. The setup is a single command per editor:

# One-command setup for your tool
npx meigen init cursor      # Cursor
npx meigen init vscode      # VS Code / GitHub Copilot
npx meigen init claude      # Claude Code

After that the documented flow is a plain question such as asking for product photography inspiration, which the project says searches the entire prompt library for free with no API key needed. The important detail for anyone evaluating this is where the boundary sits. The MCP server is a separate repository from the prompt list, and it is the piece that turns a static JSON file into something an assistant can query. If you only want the data, you do not need the server at all, and the JSON download is enough.

The system prompt is the more reusable artifact

Alongside the dataset, the project ships a system prompt designed to turn simple inputs into detailed image descriptions following NanoBanana practices, usable in n8n, Gemini, GPTs or anything else that accepts a system prompt. Its stated principles are specific enough to borrow: replace adjectives with quantified parameters such as 90mm and f/1.8 instead of professional looking, replace feeling words with pro terminology such as Kodak Vision3 500T instead of cinematic vibe, keep negative constraints because telling the model what not to do matters, describe multiple senses including texture and temperature, and group the description by content type.

That file is the part of this repository with the longest useful life. A ranked list of prompts from social posts ages quickly, since the formats that circulated last month are not what people publish next month. A set of rewriting rules for turning a vague brief into a parameterised one does not expire in the same way, and it applies to models beyond the ones this dataset targets.

Where the licence labelling does not line up

The README carries a badge that labels the project CC BY 4.0 and links to the LICENSE file, while GitHub itself classifies the repository licence as a custom licence that it cannot categorise. That disagreement is worth resolving before you ship anything derived from it, and the way to resolve it is to read the LICENSE file in the repository rather than to rely on a badge.

The attribution layer is thinner than the licence question suggests. Entries credit the X account that posted the original, and the project notes that all prompts are hand-curated and asks for issues to be opened if something is wrong. What is not documented anywhere in the README is where the underlying prompts come from, how a post was chosen for inclusion, or how a prompt's author would object. Prompts that circulate on social media are typically short enough to be copied casually, and a repository that aggregates them inherits that ambiguity. That makes this a reference collection to sample from, not a corpus to embed in a product.

The wrong tool when you need images, not prompt text

This repository produces no images. It stores strings and metadata about prompts, and the generation happens elsewhere, in whichever image model you point the prompt at. If your need is a pipeline that renders a thousand product shots on a schedule, the missing pieces are exactly the parts that are not here: no scheduler, no model API wrapper, no retry logic, no output directory management. The MCP server covers search, browsing and generation, but that is a convenience for an assistant, not a job runner.

There is a second mismatch. The prompts are grouped and ranked for a specific set of models, GPT Image 2, NanoBanana Pro and Midjourney V7 among them, and they contain model specific instructions such as output resolution and aspect ratio. Moving one to a different generator is possible, but the parameter block was tuned for the model the author used, and the README does not discuss how to translate between them.

The realistic use is a human in the loop, skimming ranked examples to borrow a structure for a prompt you write yourself, or asking an assistant to search the library through the MCP server when a vague brief needs a starting point.

Editorial conclusion

Use nanobanana-trending-prompts when you want a ready-made prompt library for GPT Image 2, NanoBanana Pro or Midjourney V7 and you are happy to treat it as inspiration rather than a specification, and when the MeiGen MCP setup saves you writing your own index. Do not adopt it if your pipeline needs reproducible, licensed prompt text with per-entry attribution, because the repository is a snapshot of posts made by individual X users and the licence labelling is inconsistent between the badge and GitHub. Before you build on it, open the LICENSE file to settle the terms yourself, and read the system prompt in prompts/system-prompt-en.md, since that file is the part most worth copying into your own tooling.

Frequently asked questions

Official sources

1. Issues
2. jau123/nanobanana-trending-prompts on GitHub
3. Project website
4. README