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CLI to retrieve prompt blueprints from the Promptary API

Project description

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Promptary

AI Prompt Knowledge Library for Data Science & Machine Learning

Promptary is a collaborative platform and premium prompt blueprint library built specifically for Data Scientists, Machine Learning Engineers, and AI practitioners.

It works as a prompt engineering workspace where professionals can discover, optimize, organize, reuse, and share high-quality AI workflows for real-world data projects.

From exploratory analysis to model development and debugging, Promptary helps teams transform scattered AI interactions into reusable engineering assets.


🛠️ Installation & Usage

Promptary provides multiple ways to access and integrate prompt blueprints into your AI workflows.


📦 Install Promptary CLI

Install the official Python CLI tool:

pip install promptary-cli

Or install directly from the repository:

git clone https://github.com/promptary/promptary.git
cd promptary
pip install .

Verify the installation:

promptary --version

The CLI uses https://promptary-cli-cr.xyz as the default API endpoint. Override it at runtime with the PROMPTARY_API_URL environment variable:

PROMPTARY_API_URL=https://custom-domain.com promptary pull p_1

🚀 Retrieve a Prompt Blueprint

Each Promptary blueprint has a unique identifier.

Example:

p_1

You can retrieve any blueprint dynamically using the Promptary CLI or API.


💻 Using Promptary CLI

Pull a blueprint directly from Promptary:

promptary pull p_1

This retrieves the latest version of the prompt template.


🐍 Using Python

Blueprints can be loaded directly into Python workflows:

import requests

prompt_id = "p_1"

url = f"https://promptary-cli-cr.xyz/api/prompts/{prompt_id}/raw"

response = requests.get(url)
response.raise_for_status()

prompt_template = response.text

print("Loaded blueprint prompt successfully!")
print(prompt_template[:120] + "...")

Example output:

Loaded blueprint prompt successfully!

You are an expert Data Scientist specialized in...

Useful for:

  • Jupyter Notebooks
  • AI agents
  • automation scripts
  • ML workflows

🌐 Using cURL

Retrieve the raw prompt directly from the API:

curl -s "https://promptary-cli-cr.xyz/api/prompts/p_1/raw"

The API returns the blueprint as plain text.


🔌 Integration Workflow

Promptary blueprints can be integrated into:

  • Python applications
  • Data Science pipelines
  • AI agents
  • LLM automation workflows
  • Internal developer tools

The goal of Promptary is to make AI workflows:

  • reusable
  • versioned
  • shareable
  • easy to integrate

📚 Example Use Case

A Data Scientist can:

  1. Discover a blueprint in Promptary.
  2. Pull it using the CLI or API.
  3. Customize the workflow.
  4. Integrate it into a notebook or production pipeline.
  5. Share improvements back with the community.

Promptary turns AI prompts into reusable engineering assets.


🎨 Design Philosophy

Promptary is designed for long technical sessions, combining a professional developer experience with a modern AI-native interface.

Premium Dark Interface

A focused dark environment built with deep grayscale and slate tones, enhanced with subtle purple and emerald accents.

Developer-Focused Typography

A refined typography system combining:

  • Sans-serif fonts for readability and navigation.
  • Mono fonts (JetBrains Mono) for:
    • code snippets
    • commands
    • technical metadata
    • development indicators

Smooth Interaction

Polished animations, transitions, and responsive components create a fluid experience optimized for productivity.


🚀 Core Features

1. Prompt Blueprint Library

A structured catalog of high-quality AI workflows organized around key Data Science domains.

Exploratory Data Analysis (EDA)

Prompts designed to accelerate:

  • dataset understanding
  • pattern discovery
  • statistical exploration
  • visualization workflows

Data Cleaning

Reusable workflows for:

  • missing value handling
  • anomaly detection
  • outlier analysis
  • data normalization

Machine Learning & Modeling

Prompt architectures for:

  • model experimentation
  • hyperparameter optimization
  • pipeline design
  • evaluation strategies

Debugging & Development

AI-assisted troubleshooting for:

  • Python
  • SQL
  • PyTorch
  • ML pipelines
  • complex errors

2. Collaborative Prompt Ecosystem

Inspired by modern software development workflows.

Fork System

Users can clone existing prompt blueprints and customize them for their own projects.

Each fork maintains:

  • original structure
  • modifications
  • contributor ownership
  • evolution history

Turning prompts into reusable community-driven assets.

Community Publishing

Professionals can publish their own workflows with:

  • author information
  • role
  • tags
  • professional links
  • technical context

3. Cloud Architecture & Data Persistence

Promptary combines cloud synchronization with local performance.

Authentication

Secure developer authentication powered by Firebase Google Auth.

Ensures:

  • personalized libraries
  • user profiles
  • contribution ownership

Cloud Database

Real-time synchronization using Cloud Firestore.

Features:

  • persistent prompt storage
  • community contributions
  • scalable data structure
  • secure access rules

Personal Library

Users can save favorite prompts locally for fast access through a personalized collection.


4. AI Development Utilities

Each prompt blueprint includes a detailed inspection environment.

Code Integration Snippets

Generate ready-to-use examples for:

  • Python API calls
  • modern AI SDKs
  • pip installation commands
  • curl requests

Designed for direct integration into:

  • Jupyter Notebooks
  • development environments
  • terminal workflows

5. Community Ranking System

Promptary uses interaction signals to surface valuable workflows.

Users can provide feedback through:

  • 👍 Likes
  • 👎 Dislikes

Helping identify:

  • trending prompts
  • high-performing workflows
  • community favorites

Vision

Promptary aims to become the GitHub for AI prompts in Data Science: a place where professionals build, share, improve, and reuse AI knowledge instead of starting from zero every time.

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