prompts.chat: 163k+ Prompts -- The Open-Source Prompt Library

Headroom: Compress LLM Inputs by 60-95% • Promptfoo: Test, Evaluate & Red-Team Your LLM Prompts If you are a developer, product manager, or AI researcher, you have likely hit the wall where the model is capable, but the prompt is not. We spend hours tweaking system instructions, debugging few-shot examples, and trying to get consistent outputs from LLMs.

Enter prompts.chat. It is not a new model. It is not a new API. It is the world’s largest open-source prompt library, boasting 163,640 GitHub stars as of our last check. Originally known as “Awesome ChatGPT Prompts,” it has evolved into a comprehensive ecosystem for discovering, sharing, and, crucially for enterprise users, self-hosting prompt collections.

In this guide, we will break down how to deploy your own instance, integrate it with your existing AI toolchain (Claude, Gemini, LangChain, etc.), and why having a private, curated prompt library might be the most cost-effective AI infrastructure decision you make this year.

Introduction #

The AI landscape in 2026 is saturated with tools that promise to “solve” prompt engineering. Most are overpriced SaaS platforms that lock you into their ecosystem. prompts.chat takes a different approach: it is a static-site-based, open-source repository that can be self-hosted.

This matters for two reasons:

1. Privacy: Your proprietary prompts never leave your infrastructure.
2. Cost: The core library is free. You only pay for the hosting (which can be near-zero) and the model API calls.

We will walk you through the technical setup, integration patterns, and honest limitations of using prompts.chat in a production environment.

What Is prompts.chat? #

At its core, prompts.chat is a curated collection of prompt examples. However, calling it a “list” undersells its utility. It is a structured dataset and a web application that allows for:

• Discovery: Browse 163k+ prompts categorized by use case (coding, writing, analysis, etc.).
• Contribution: Users can submit prompts via the web interface, which syncs to the main GitHub repository.
• Data Export: Prompts are available in CSV, Markdown, and as a Hugging Face dataset.
• Self-Hosting: Organizations can clone the repo, configure authentication, and run a branded, private instance.

It works with any modern AI assistant: ChatGPT, Claude, Gemini, Llama, Mistral, and more. The prompts are model-agnostic text blocks, not proprietary code.

How prompts.chat Works #

The architecture is surprisingly simple, which is a feature, not a bug.

1. The Core Repository: The main f/prompts.chat repo contains the web app code (HTML/JS/CSS) and the PROMPTS.md file, which is the source of truth for all prompts.
2. The Web App: The frontend is a static site generator. It renders the prompts into a searchable interface.
3. The Dataset: The prompts are also available on Hugging Face (fka/prompts.chat), making it easy to ingest into ML pipelines.

ASCII Architecture Diagram #

graph TD
    A[User/Dev] -->|Visits| B(prompts.chat Website)
    B -->|Fetches| C[PROMPTS.md / prompts.csv]
    C -->|Parsed| D[Frontend UI]
    D -->|Copy/Paste| E[AI Model API]
    
    F[Self-Hosted Instance] -->|Clones| G[f/prompts.chat Repo]
    G -->|Configures| H[Auth / Branding]
    H -->|Runs| I[Private UI]
    I -->|Secure Access| J[Internal Team]

The key takeaway: prompts.chat is a content management system for text prompts. It does not execute the prompts itself; it serves them to you to use elsewhere.

Installation & Setup #

You have two main paths: using the interactive wizard or a manual git clone. Both are straightforward.

Option 1: The Quick Start (npx) #

This is the fastest way to spin up a local instance.

# Create a new directory named my-prompt-library
npx prompts.chat new my-prompt-library

# Navigate into it
cd my-prompt-library

# Run the setup wizard
npm run setup

The npm run setup command will guide you through:

1. Branding: Logo, site title, description.
2. Theme: Dark/Light mode defaults.
3. Authentication: Configure GitHub, Google, or Azure AD login (critical for enterprise).
4. Database: Configure PostgreSQL (recommended: Neon).

Option 2: Manual Setup (Git Clone) #

For those who want full control over the codebase:

# Clone the repository
git clone https://github.com/f/prompts.chat.git

# Enter the directory
cd prompts.chat

# Install dependencies
npm install

# Run the setup wizard
npm run setup

Database Configuration #

The README recommends PostgreSQL for self-hosted instances. For a managed solution, Neon is the sponsored provider.

# Example .env configuration for local development
DATABASE_URL=postgresql://user:password@localhost:5432/prompts_chat
GITHUB_ID=your_github_client_id
GITHUB_SECRET=your_github_client_secret

Docker Deployment #

If you prefer containerization, use the provided DOCKER.md guide. Generally, it involves:

# Build the image
docker build -t prompts-chat .

# Run the container
docker run -p 3000:3000 -e DATABASE_URL=... prompts-chat

For production hosting, consider using DigitalOcean or HTStack for reliable, scalable infrastructure.

Integration with [3-5 Tools] #

prompts.chat is not just a website. It provides integrations for CLI, Claude Code, and MCP (Model Context Protocol) servers. This makes it a first-class citizen in modern AI dev workflows.

1. CLI Integration #

You can access prompts directly from your terminal without opening a browser.

# Run the interactive CLI
npx prompts.chat

# Search for a specific prompt
npx prompts.chat search "python debugging"

# Copy a prompt to clipboard (if supported by your OS)
npx prompts.chat copy "react component generator"

2. Claude Code Plugin #

If you use Claude Code, you can install prompts.chat as a plugin.

# Add the plugin from the marketplace
/plugin marketplace add f/prompts.chat

# Install the plugin
/plugin install prompts.chat@prompts.chat

This allows you to trigger prompts directly within your coding session.

3. MCP Server Integration #

The Model Context Protocol (MCP) is becoming the standard for connecting AI tools to external data. prompts.chat offers both remote and local MCP servers.

Remote MCP (Recommended for most users):

{
  "mcpServers": {
    "prompts.chat": {
      "url": "https://prompts.chat/api/mcp"
    }
  }
}

Local MCP (For self-hosted instances):

{
  "mcpServers": {
    "prompts.chat": {
      "command": "npx",
      "args": ["-y", "prompts.chat", "mcp"]
    }
  }
}

This enables tools like Cursor, Windsurf, or custom LLM agents to query your prompt library programmatically.

Real-World Use Cases #

Since prompts.chat does not provide proprietary performance benchmarks (it is a static library, not a model), we assess its value qualitatively based on community adoption and enterprise use cases.

Qualitative Impact Assessment #

• Onboarding New Developers: A company can self-host prompts.chat and curate a list of “internal best practices” for prompt engineering. New hires can browse these prompts to understand how the team structures system instructions for code generation, documentation, and testing.
• Consistency in Marketing: Marketing teams can maintain a library of approved prompt templates for blog post outlines, social media captions, and email drafts. This ensures brand voice consistency across multiple AI tools.
• Research & Experimentation: Data scientists can download the prompts.csv or Hugging Face dataset to analyze prompt structures, common patterns, and effective few-shot examples across thousands of use cases.

Use Case: Internal AI Governance #

Large organizations struggle with “prompt sprawl.” Every employee has their own copy-paste prompts. By self-hosting prompts.chat with authentication (GitHub/SSO), you create a single source of truth.

1. Auditability: You know which prompts are being used.
2. Security: No sensitive internal data is shared with public prompt sites.
3. Version Control: Prompts are stored in Git, allowing for rollback and review.

Advanced Usage / Production Hardening #

For production deployments, there are several hardening steps you should consider.

1. Custom Domain & SSL #

Ensure your self-hosted instance uses a custom domain with valid SSL certificates. If using a reverse proxy (Nginx/Apache):

server {
    listen 443 ssl;
    server_name prompts.internal.yourcompany.com;

    ssl_certificate /etc/ssl/certs/prompts.chat.crt;
    ssl_certificate_key /etc/ssl/private/prompts.chat.key;

    location / {
        proxy_pass http://localhost:3000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

2. Authentication & RBAC #

The setup wizard allows you to configure GitHub, Google, or Azure AD. For enterprise, Azure AD (Entra ID) is often preferred for SSO integration.

# Example Azure AD config in .env
AZURE_AD_CLIENT_ID=your_azure_client_id
AZURE_AD_TENANT_ID=your_azure_tenant_id
AZURE_AD_CLIENT_SECRET=your_azure_client_secret

You can then restrict access to specific domains or groups.

3. Database Scaling #

If you expect high concurrency (e.g., 1000+ employees browsing simultaneously), ensure your PostgreSQL instance has appropriate connection pooling. PgBouncer is recommended.

# pgbouncer.ini
[databases]
prompts_chat = host=127.0.0.1 port=5432 dbname=prompts_chat

[pgbouncer]
pool_mode = transaction
max_client_conn = 1000
default_pool_size = 20

4. Backup Strategy #

Since prompts are stored in Git, version control is your backup. However, user contributions (if enabled) and database configurations should be backed up regularly.

# Backup PostgreSQL
pg_dump -U postgres prompts_chat > prompts_backup_$(date +%F).sql

# Backup Git repo
git push origin main --mirror

Comparison with Alternatives #

How does prompts.chat compare to other solutions?

| Feature | prompts.chat | PromptBase | ShareGPT | PromptPerfect | | :