An Open AI Prompt Library: 2,600 Image and Video Prompts You Can Download as a Dataset
---
title: "An Open AI Prompt Library: 2,600 Image and Video Prompts You Can Download as a Dataset"
published: true
description: "AgentCraft is an AI prompt library for Seedance, Nano Banana, GPT Image and more. All 2,600 prompts are open on GitHub and Hugging Face, each traced to its source."
tags: ai, promptengineering, opensource, machinelearning
---
Here is my advice for anyone writing prompts for image or video models: start from a prompt that already worked.
The reason is simple. A good prompt carries a structure, and the structure is the part that is hard to invent. Once you have it, you only swap in your own subject.
So where do you find prompts that worked? AgentCraft is a free library built for exactly this job.
A quick guess
Let me start with a question. A 15 second film clip, 1940s war documentary look, made with MiniMax H3. How long do you think the prompt was?
One sentence? A short paragraph?
The answer: the creator split the 15 seconds into timed beats. An establishing shot, rising tension, the firefight, a human moment, then a pull back. Each beat has its own camera move. Below that sit separate blocks for the environment, the characters, the visual style and the physics, plus a long negative prompt.
That structure is why the clip holds its 35mm look from start to end. You can copy it for any period piece you want to make.
This prompt came from @RuzainaMeer on X. AgentCraft keeps it word for word, next to the clip it produced, with a link back to the post.
What AgentCraft has
The whole library is open, and the numbers are large.
- Video prompts, 940. Seedance 2.5, MiniMax H3, Wan 3.0, Kling 4.0, Veo and Gemini Omni, grouped by type: cinematic, action, sci-fi, anime, ads and more.
- Image prompts, 1,307. GPT Image 2 and 2.5 and Nano Banana, grouped by use: posters, portraits, infographics, product shots, UI mockups, logos.
- Agent prompts, 353. System and role prompts for Claude Code, Codex and other coding agents, grouped by field.
- Claude Code skills, 4,636. Each one read and screened before it was listed, with its install command.
Every video and image prompt shows the output it made and the place it came from, either the model maker's guide or a creator's post. You see the result first, then decide whether the prompt is worth your time.
Links: video prompts, image prompts, agent prompts, skills.
The open data
This is the part I like most. Everything behind the site is public, so you can take the data and build on it.
1. awesome-image-video-prompts on GitHub, 3.4k stars. The video and image prompts, with outputs and sources.
2. awesome-prompts on GitHub, 9.2k stars. Prompts, frameworks and papers for LLMs and agents.
3. awesome-prompts on Hugging Face, 3,149 prompts in two subsets. The engineering subset has 372 system prompts; 73 of them cite arXiv papers by id. The library subset has 2,777 everyday prompts for writing, marketing, study and design, each in English and Chinese.
Loading the dataset takes two lines:
from datasets import load_dataset
eng = load_dataset("Adrian2016/awesome-prompts", "engineering", split="train")
lib = load_dataset("Adrian2016/awesome-prompts", "library", split="train")
A prompt dataset with sources attached is handy for evals, for fine-tuning a prompt writer, or for studying which patterns models follow.
Three patterns worth stealing
Browse the library for a while and a few patterns keep showing up. Here are three that are easy to reuse!
1. Give video a timeline. Write the clip as timed beats with a camera move for each, as in the war clip above. The model then plans the shots in order.
2. Give images a count. One GPT Image 2 prompt asks for a town scene with exactly 100 elements, then asks the model to list every item. The model plans the picture as an inventory, and you get a dense illustrated town under a numbered list.
3. Name the layout. Another prompt asks for a catalog of 1990s anime character styles: 16 labeled archetypes in a grid, one per panel, at 16:9. One sentence, and the result is a clean reference sheet.
The common thread: tell the model how to organize the work, and it fills in the rest well.
Lesson
Good prompts are templates. Borrow the structure, keep your own subject.
Which model are you prompting most these days, and what structure works for you?
The Model Is the Brain. The Harness Is Everything Else.
When an AI agent fails, the first reaction is usually: "We need a better model."
But most of the time, the model isn't the problem. The problem is everything around it.
That "everything around it" has a name: the harness.
What is a harness?
A language model on its own does one thing: it takes in text and gives back text. That's it.
It can't open a file. It can't call an API. It doesn't remember what happened ten minutes ago. It has no idea whether its own answer is right.
So how do tools like coding agents actually do things, like reading your codebase, running tests, and fixing bugs?
Someone built a system around the model that lets it act. That system is the harness.
A simple way to remember it:
> Agent = Model + Harness
The model is the brain. The harness is the body, the rules, and the safety checks.
What's inside a harness?
There's no single standard, but most harnesses are made of the same building blocks:
| Part | What it does |
|---|---|
| 📋 Instructions | The rules, goals, and context the agent starts with |
| 🔧 Tools | What it's allowed to use: APIs, files, search, a code runner |
| 🧠 Memory | What it keeps between steps or sessions |
| 🔁 Control loop | Plan → act → look at the result → decide the next step → stop |
| 🛡️ Guardrails | What it's not allowed to do without permission |
| ✅ Checks | Tests and validation that verify its output |
| 📊 Logs | A record of every step it took |
| 🙋 Human review | A person approves risky actions before they happen |
None of these are the model. All of them decide how well the model performs.
A simple example
Imagine you ask an agent: "Fix the failing test in this project."
Without a harness, the model can only guess. It writes some code that looks right and hopes for the best.
With a harness, the flow looks like this:
1. The instructions tell it how the project is structured and which commands to use.
2. A tool lets it read the test file and the code being tested.
3. Another tool lets it run the test and see the actual error.
4. It edits the code, then the check runs the test again.
5. If it still fails, the loop sends it back to try again, up to a limit.
6. Before it changes anything risky, a guardrail asks you for approval.
7. Every step is saved in the logs, so you can see exactly what happened.
Same model. Completely different result.
Why the harness matters so much
Here's what surprised me most when I started reading about this: two teams can use the exact same model and get very different results.
That's because the harness controls:
- What the model sees. Good context in, good decisions out.
- What the model can do. The right tools, nothing more.
- How mistakes get caught. Checks catch errors before they reach you.
- When it stops. A clear end point keeps time and cost under control.
Models are getting better and more similar every few months. The harness is where teams can really make their agent stand out.
What happens if you ignore it
If you plug a powerful model into a weak harness, you get a powerful but unreliable agent. Common problems:
- ❌ Endless loops: the agent repeats the same step because nothing tells it to stop
- ❌ Lost context: it forgets earlier decisions halfway through a long task
- ❌ Unchecked errors: wrong answers go straight through with nothing verifying them
- ❌ Risky actions: it deletes, sends, or changes things with no approval step
- ❌ Silent failures: something breaks, and with no logs you can't tell why
- ❌ Rising costs: every extra loop is another paid model call
A better model can make an agent smarter. Only a better harness makes it reliable.
If you're building an agent, start here
You don't need a complex setup to begin. Ask yourself these five questions:
1. Does my agent know the rules? Write clear instructions and project context.
2. Does it have only the tools it needs? Fewer, well-defined tools beat many vague ones.
3. How does it check its own work? Add at least one test or validation step.
4. When does it stop? Set a step limit or a clear finish condition.
5. Can I see what it did? Log every action.
Get these right, and even a smaller model can do surprisingly well.
Final thought
We spend a lot of time comparing models: which one is smarter, faster, cheaper. That matters.
But the next time an agent lets you down, look at the harness before you blame the brain.
What's the biggest problem you've hit while building or using an AI agent? Tell me in the comments 👇
---
I write about how tech actually works at Compiled Thoughts on Substack. If this was useful, follow me here or subscribe there for more breakdowns like this.
Further reading:
- What Is an Agent Harness? Harness Engineering Explained
- What Is Harness Engineering? (Atlan)
- Harness Engineering: A Guide to AI Coding Agents (Faros AI)
- Awesome Harness Engineering (GitHub)
1 reaction
Mentions
BUILD COMPATIBLE AGENTS WITH AGENT2AGENT PROTOCOL! 🤝 Ai Boost Awesome A2aCurates resources to help developers build agents compatible with the open Agent2Agent protocol. ai-boost/awesome-a2a t.co
An Open AI Prompt Library: 2,600 Image and Video Prompts You Can Download as a Dataset --- title: "An Open AI Prompt Library: 2,600 Image and Video Prompts You Can Download as a Dataset" published: true description: "AgentCraft is an AI prompt library for Seedance, Nano Banana, GPT Image and more. All 2,600 prompts are open on GitHub and Hugging Face, each traced to its source." tags: ai, promptengineering, opensource, machinelearning --- Here is my advice for anyone writing prompts for image or video models: start from a prompt that already worked. The reason is simple. A good prompt carries a structure, and the structure is the part that is hard to invent. Once you have it, you only swap in your own subject. So where do you find prompts that worked? AgentCraft is a free library built for exactly this job. A quick guess Let me start with a question. A 15 second film clip, 1940s war documentary look, made with MiniMax H3. How long do you think the prompt was? One sentence? A short paragraph? The answer: the creator split the 15 seconds into timed beats. An establishing shot, rising tension, the firefight, a human moment, then a pull back. Each beat has its own camera move. Below that sit separate blocks for the environment, the characters, the visual style and the physics, plus a long negative prompt. That structure is why the clip holds its 35mm look from start to end. You can copy it for any period piece you want to make. This prompt came from @RuzainaMeer on X. AgentCraft keeps it word for word, next to the clip it produced, with a link back to the post. What AgentCraft has The whole library is open, and the numbers are large. - Video prompts, 940. Seedance 2.5, MiniMax H3, Wan 3.0, Kling 4.0, Veo and Gemini Omni, grouped by type: cinematic, action, sci-fi, anime, ads and more. - Image prompts, 1,307. GPT Image 2 and 2.5 and Nano Banana, grouped by use: posters, portraits, infographics, product shots, UI mockups, logos. - Agent prompts, 353. System and role prompts for Claude Code, Codex and other coding agents, grouped by field. - Claude Code skills, 4,636. Each one read and screened before it was listed, with its install command. Every video and image prompt shows the output it made and the place it came from, either the model maker's guide or a creator's post. You see the result first, then decide whether the prompt is worth your time. Links: video prompts, image prompts, agent prompts, skills. The open data This is the part I like most. Everything behind the site is public, so you can take the data and build on it. 1. awesome-image-video-prompts on GitHub, 3.4k stars. The video and image prompts, with outputs and sources. 2. awesome-prompts on GitHub, 9.2k stars. Prompts, frameworks and papers for LLMs and agents. 3. awesome-prompts on Hugging Face, 3,149 prompts in two subsets. The engineering subset has 372 system prompts; 73 of them cite arXiv papers by id. The library subset has 2,777 everyday prompts for writing, marketing, study and design, each in English and Chinese. Loading the dataset takes two lines: from datasets import load_dataset eng = load_dataset("Adrian2016/awesome-prompts", "engineering", split="train") lib = load_dataset("Adrian2016/awesome-prompts", "library", split="train") A prompt dataset with sources attached is handy for evals, for fine-tuning a prompt writer, or for studying which patterns models follow. Three patterns worth stealing Browse the library for a while and a few patterns keep showing up. Here are three that are easy to reuse! 1. Give video a timeline. Write the clip as timed beats with a camera move for each, as in the war clip above. The model then plans the shots in order. 2. Give images a count. One GPT Image 2 prompt asks for a town scene with exactly 100 elements, then asks the model to list every item. The model plans the picture as an inventory, and you get a dense illustrated town under a numbered list. 3. Name the layout. Another prompt asks for a catalog of 1990s anime character styles: 16 labeled archetypes in a grid, one per panel, at 16:9. One sentence, and the result is a clean reference sheet. The common thread: tell the model how to organize the work, and it fills in the rest well. Lesson Good prompts are templates. Borrow the structure, keep your own subject. Which model are you prompting most these days, and what structure works for you?
7 Viral AI Agent Repos: 4 Checks Before You Install One Seven popular AI agent repos, four checks before you install any of them (checked 20-21 Oct 2026; star counts change daily). 1. Who made it? Anthropic Cybersecurity Skills is an independent community project by @mukul975. Its README says "Not affiliated with Anthropic PBC". 2. Is it the right repo? Two separate curated lists are both called Awesome Harness Engineering (ai-boost and walkinglabs), and several repos use "Agent Memory"-style names. Match the GitHub owner, not just the name. 3. The licence. OpenViking is AGPL-3.0, which has conditions if you offer a modified version as a service. Browser Use, Scientific Agent Skills and Diagram Design are MIT; agentmemory and Anthropic Cybersecurity Skills are Apache-2.0. Read the licence before business use. 4. The real cost. Browser Use's code is MIT and free, but you pay for the AI model you connect to it. Its optional Browser Use Cloud starts from $0.02 per browser-hour (the project's own figure). Also: agentmemory's "95.2% retrieval" and Scientific Agent Skills' "250,000+ scientists" are the projects' own claims. Diagram Design has a Claude Code plugin and also works with other agents. Repos (all 7, public on GitHub): Browser Use: browser-use/browser-use agentmemory: rohitg00/agentmemory Scientific Agent Skills: K-Dense-AI/scientific-agent-skills Diagram Design: cathrynlavery/diagram-design Anthropic Cybersecurity Skills (community project, not made by Anthropic): mukul975/Anthropic-Cybersecurity-Skills Awesome Harness Engineering (two lists share the name): ai-boost/awesome-harness-engineering and walkinglabs/awesome-harness-engineering OpenViking (AGPL-3.0): volcengine/OpenViking Browser Use Cloud pricing: browser-use.com Which check would have caught you out? #AI #AIagents #GitHub #shorts Made with AI-generated visuals and voice.
Who doesn't like a well-curated awesome list?! Here's a cool one if you've not seen it yet on harness engineering. I feel smarter just skimming the TOC. 😁 ai-boost/awesome-harness-engineering
6 открытых репозиториев для ИИ-агентов 6 открытых репозиториев для ИИ-агентов, которые экономят месяцы работы. Один превращает Claude в учёного. 1. Браузер для агента: browser-use/browser-use 2. Память агента: rohitg00/agentmemory 3. Наука, 177 навыков: K-Dense-AI/scientific-agent-skills 4. Схемы: cathrynlavery/diagram-design 5. База памяти и знаний: volcengine/OpenViking 6. Обвязка агентов: ai-boost/awesome-harness-engineering Открой Claude Code, вставь нужную ссылку и напиши: «Поставь это и покажи на примере». Ставь по одному, а не все сразу. Нужна подписка Claude Pro или Max. В терминал ничего вводить не нужно. Бесплатная игра и материалы из всех роликов: ig.onai.academy #shorts #claude #ииагенты #github
GitHub Repo Curates Top-Rated GPT Prompts #shorts #GitHub #GPT #Prompts #AI #MachineLearning Most developers struggle to find reliable prompts that work across different GPT models. This GitHub repo curates the best prompts and prompt engineering research, making it easy for developers to access. Link: ai-boost/awesome-prompts This video's script and narration were generated with AI tools. #GitHub #GPT #Prompts #AI #MachineLearning Watch next: youtube.com
The Model Is the Brain. The Harness Is Everything Else. When an AI agent fails, the first reaction is usually: "We need a better model." But most of the time, the model isn't the problem. The problem is everything around it. That "everything around it" has a name: the harness. What is a harness? A language model on its own does one thing: it takes in text and gives back text. That's it. It can't open a file. It can't call an API. It doesn't remember what happened ten minutes ago. It has no idea whether its own answer is right. So how do tools like coding agents actually do things, like reading your codebase, running tests, and fixing bugs? Someone built a system around the model that lets it act. That system is the harness. A simple way to remember it: > Agent = Model + Harness The model is the brain. The harness is the body, the rules, and the safety checks. What's inside a harness? There's no single standard, but most harnesses are made of the same building blocks: | Part | What it does | |---|---| | 📋 Instructions | The rules, goals, and context the agent starts with | | 🔧 Tools | What it's allowed to use: APIs, files, search, a code runner | | 🧠 Memory | What it keeps between steps or sessions | | 🔁 Control loop | Plan → act → look at the result → decide the next step → stop | | 🛡️ Guardrails | What it's not allowed to do without permission | | ✅ Checks | Tests and validation that verify its output | | 📊 Logs | A record of every step it took | | 🙋 Human review | A person approves risky actions before they happen | None of these are the model. All of them decide how well the model performs. A simple example Imagine you ask an agent: "Fix the failing test in this project." Without a harness, the model can only guess. It writes some code that looks right and hopes for the best. With a harness, the flow looks like this: 1. The instructions tell it how the project is structured and which commands to use. 2. A tool lets it read the test file and the code being tested. 3. Another tool lets it run the test and see the actual error. 4. It edits the code, then the check runs the test again. 5. If it still fails, the loop sends it back to try again, up to a limit. 6. Before it changes anything risky, a guardrail asks you for approval. 7. Every step is saved in the logs, so you can see exactly what happened. Same model. Completely different result. Why the harness matters so much Here's what surprised me most when I started reading about this: two teams can use the exact same model and get very different results. That's because the harness controls: - What the model sees. Good context in, good decisions out. - What the model can do. The right tools, nothing more. - How mistakes get caught. Checks catch errors before they reach you. - When it stops. A clear end point keeps time and cost under control. Models are getting better and more similar every few months. The harness is where teams can really make their agent stand out. What happens if you ignore it If you plug a powerful model into a weak harness, you get a powerful but unreliable agent. Common problems: - ❌ Endless loops: the agent repeats the same step because nothing tells it to stop - ❌ Lost context: it forgets earlier decisions halfway through a long task - ❌ Unchecked errors: wrong answers go straight through with nothing verifying them - ❌ Risky actions: it deletes, sends, or changes things with no approval step - ❌ Silent failures: something breaks, and with no logs you can't tell why - ❌ Rising costs: every extra loop is another paid model call A better model can make an agent smarter. Only a better harness makes it reliable. If you're building an agent, start here You don't need a complex setup to begin. Ask yourself these five questions: 1. Does my agent know the rules? Write clear instructions and project context. 2. Does it have only the tools it needs? Fewer, well-defined tools beat many vague ones. 3. How does it check its own work? Add at least one test or validation step. 4. When does it stop? Set a step limit or a clear finish condition. 5. Can I see what it did? Log every action. Get these right, and even a smaller model can do surprisingly well. Final thought We spend a lot of time comparing models: which one is smarter, faster, cheaper. That matters. But the next time an agent lets you down, look at the harness before you blame the brain. What's the biggest problem you've hit while building or using an AI agent? Tell me in the comments 👇 --- I write about how tech actually works at Compiled Thoughts on Substack. If this was useful, follow me here or subscribe there for more breakdowns like this. Further reading: - What Is an Agent Harness? Harness Engineering Explained - What Is Harness Engineering? (Atlan) - Harness Engineering: A Guide to AI Coding Agents (Faros AI) - Awesome Harness Engineering (GitHub)
If you learn one AI skill before 2027, make it harness engineering. I recently came across a repo on GitHub called Harness Engineering, and it has everything you need to learn for free. Here it is: ai-boost/awesome-harness-engineering t.co