vibeprompt 0.2.5

Your words. Their way. Perform style and audience adaptation for your prompts.

🦩VibePrompt: Your words. Their way

A lightweight Python package for adapting prompts by tone, style, and audience. Built on top of LangChain, VibePrompt supports multiple LLM providers and enables structured, customizable prompt transformations for developers, writers, and researchers.

🚀 Features

• Multi-Provider Support: Works with OpenAI, Cohere, Anthropic, and Google
• Style Adaptation: Transform prompts across +15 writing styles
• Audience Targeting: Adapt content for different audiences and expertise levels
• Safety Checks: Built-in content filtering and safety validation
• Flexible Configuration: Environment variables or programmatic API key management
• Verbose Logging: Detailed logging for debugging and monitoring
• CLI Integeration: Support running using vibeprompt command rather than Python scripting.
• LangChain Based: Built on the top of LangChain

📦 Installation

pip install vibeprompt

Development Installation

git clone https://github.com/MohammedAly22/vibeprompt.git
cd vibeprompt
pip install -e .

🏃‍♂️ Quick Start

1. Using Python Scripts

from vibeprompt import PromptStyler


# Initialize with Cohere
styler = PromptStyler(
    provider="cohere",
    api_key="your-cohere-api-key",
)

# Transform your prompt
result = styler.transform(
    prompt="Explain machine learning to me",
    style="technical",
    audience="developers"
)

print(result)

Output:

Define machine learning, employing precise technical terminology from the field of computer science and artificial intelligence, as if architecting a distributed system. Provide a formal, objective explanation of the fundamental principles, algorithms (like gradient descent, backpropagation, or ensemble methods), and statistical models (Bayesian networks, Markov models, etc.) that constitute machine learning – as you would document an API. Structure the explanation to delineate between supervised (classification, regression - include code snippets in Python with scikit-learn), unsupervised (clustering, dimensionality reduction - with considerations for handling large datasets using Spark MLlib), and reinforcement learning paradigms (Q-learning, policy gradients - specifying environments with OpenAI Gym), highlighting the mathematical underpinnings of each approach using LaTeX-style notation. Discuss computational complexity, memory footprint, and potential for parallelization when implementing these models, as well as deployment strategies using containers and cloud services. Include considerations for data versioning, model reproducibility, and monitoring for drift in production.

2. Using CLI

Using Single Command

vibeprompt transform "Explain machine learning to me" \
--style technical \
--audience developers \
--provider gemini \
--model gemini-2.0-flash \
--enable-safety 
--api-key your-gemini-api-key

Using Configuration First

vibeprompt config set
# Follow the configuration instruction for selecting the provider, choosing the model, etc.
vibeprompt transform "Explain machine learning to me" -- style technical --audience developers

🎨 Available Styles

VibePrompt supports the following writing styles (+15):

Style	Description	Use Case
academic	Evidence-based, structured, and citation-aware	Research papers, academic writing
assertive	Direct, confident, and firm	Calls to action, decision-making
authoritative	Commanding tone backed by expertise	Policy documents, expert opinion pieces
casual	Conversational, laid-back, and friendly	Blog posts, internal team updates
creative	Original, imaginative, and artistic	Fiction, branding, content ideation
diplomatic	Tactful, neutral, and conflict-averse	Sensitive topics, cross-functional communication
educational	Informative, structured for teaching	Lessons, learning modules
empathic	Compassionate and emotionally resonant	Mental health, customer care, support communication
formal	Polished, professional, and respectful	Business reports, official correspondence
friendly	Warm, supportive, and encouraging	Customer onboarding, FAQs, community management
humorous	Light-hearted, witty, and entertaining	Social media, casual marketing
minimalist	Concise, essential, and clean	UI copy, product descriptions
persuasive	Convincing and benefit-oriented	Sales copy, fundraising pitches
playful	Fun, whimsical, and imaginative	Youth content, informal branding
poetic	Lyrical, expressive, and metaphor-rich	Creative writing, visual storytelling
sequential	Ordered, step-by-step, and instructional	Tutorials, how-to guides
simple	Clear, basic, and easy to understand	Beginners, general explanations
storytelling	Narrative-driven, emotional, and character-focused	Brand stories, user testimonials
technical	Accurate, data-driven, and jargon-appropriate	Documentation, engineering blogs

👥 Available Audiences

Target your content for specific audiences (15):

Audience	Description	Characteristics
adults	General adult readers	Mature tone, practical context
beginners	New learners in any domain	Simple explanations, foundational concepts
business	Business stakeholders	Strategic focus, ROI, and market perspective
children	Young learners (ages 8–12)	Friendly tone, simple words, relatable examples
developers	Software developers	Code samples, technical accuracy, precise language
educators	Teachers, instructors	Pedagogical structure, learning outcomes
experts	Domain specialists	Advanced jargon, deep insights
general	General audience	Balanced tone, non-specialized
healthcare	Medical professionals	Clinical tone, evidence-based terminology
intermediates	Mid-level learners	Building on basics, transitional explanations
professionals	Industry professionals	Formal tone, work-related context
researchers	Scientific and academic researchers	Technical precision, citations, deep analysis
seniors	Older adults	Clear, respectful, possibly slower-paced explanations
students	School or university learners	Educational tone, focused on comprehension
teenagers	Teen audience (ages 13–18)	Casual, relevant, and age-appropriate language

🔌 Supported Providers

VibePrompt supports multiple LLM providers through LangChain:

1. Cohere

Available Models:

• command-a-03-2025 – Most advanced Cohere model (Command R+ successor)
• command-r-plus-04-2024 – High-performance RAG-optimized model
• command-r – Earlier RAG-friendly model
• command-light – Lightweight model for fast, low-cost tasks
• command-xlarge – Legacy large model from earlier generation

---

2. OpenAI

Available Models:

• gpt-4 – Original GPT-4 model with strong reasoning and accuracy
• gpt-4-turbo – Cheaper and faster variant of GPT-4 with the same capabilities
• gpt-4o – Latest GPT-4 model with multimodal support (text, image, audio), faster and more efficient
• gpt-3.5-turbo – Cost-effective model with good performance for everyday tasks

---

3. Anthropic

Available Models:

• claude-3-opus-20240229 – Most powerful Claude model
• claude-3-sonnet-20240229 – Balanced performance
• claude-3-haiku-20240307 – Fast and cost-effective
• claude-2.1 – Previous generation
• claude-2.0 – Older generation

---

4. Gemini

Available Models:

• gemini-2.0-flash – Fast and efficient model for lightweight tasks (v2.0)
• gemini-2.0-flash-lite – Ultra-light version of Flash 2.0 for minimal latency use cases
• gemini-2.5-flash – Improved speed and efficiency over Flash 2.0 (v2.5)
• gemini-2.5-flash-lite – Slimmest and quickest Gemini model (v2.5)
• gemini-2.5-pro – Latest flagship model with enhanced performance and reasoning capabilities

📚 Usage Examples

Basic Usage with Cohere Python API

1. Environment Variable Configuration

import os
from vibeprompt import PromptStyler


# Set environment variable
os.environ["COHERE_API_KEY"] = "your-cohere-api-key"

# Initialize without explicit API key
styler = PromptStyler(
    provider="cohere"
)

# Adapt prompt
result = styler.transform(
    prompt="Write a product description for a smartphone",
    style="simple",
    audience="general"
)
print(result)

Output:

Write a product description for a smartphone. Use clear, simple words and short sentences. Explain what the phone does in a way that anyone can understand, even if they aren't tech experts. Think of it like describing a Swiss Army knife, but for the digital world. Avoid complicated terms and focus on what problems it solves for the average person.

2. Direct API Key Configuration

from vibeprompt import PromptStyler


# Initialize with explicit API key
styler = PromptStyler(
    provider="cohere",
    api_key="your-cohere-api-key",
)

# Adapt prompt
result = styler.transform(
    prompt="Explain quantum computing",
    style="formal",
    audience="students"
)
print(result)

Output:

Please provide a comprehensive explanation of quantum computing. Ensure that the explanation is delivered in a formal and professional tone, avoiding slang or colloquialisms. Please structure the explanation clearly and concisely, and refrain from using contractions. Your response should be polite and respectful.

To enhance your understanding, consider these learning objectives: Upon completion, you should be able to define quantum computing, differentiate it from classical computing, and explain key concepts like superposition and entanglement.

Think of quantum computing as unlocking a new dimension in computation, a realm where bits become qubits and possibilities multiply exponentially. To aid in memory, remember "SUPERposition enables SUPERpower!" Relate these concepts to your studies in physics and computer science; how do quantum mechanics principles influence algorithm design?

As you explain, include illustrative examples. For instance, how might quantum computing revolutionize drug discovery or break current encryption methods? Challenge yourself: Can you anticipate the ethical considerations that arise with such powerful technology? Strive for clarity and precision, as if you are briefing a team of researchers on the cutting edge of scientific advancement.

Configuration Options

PromptStyler Initialization Parameters

styler = PromptStyler(
    provider="cohere",           # Required: LLM provider
    api_key="your-key",          # API key (or use env var)
    model="command-a-03-2025",   # Model name (optional)
    enable_safety=True,          # Enable safety checks
    verbose=True,                # Enable verbose logging
    temperature=0.7,             # Creativity level (0.0-1.0)
    max_tokens=500,              # Maximum response length
    ...                          # Other LangChain model configurations (e.g, retry_attempts=3)
)

Environment Variables

Set these environment variables for automatic API key detection:

# Cohere
export COHERE_API_KEY="your-cohere-api-key"

# OpenAI
export OPENAI_API_KEY="your-openai-api-key"

# Anthropic
export ANTHROPIC_API_KEY="your-anthropic-api-key"

# Google
export GOOGLE_API_KEY="your-google-api-key"

🛡️ Safety Checks

VibePrompt includes comprehensive safety features:

Built-in Safety Features

from vibeprompt import PromptStyler


styler = PromptStyler(
    provider="cohere",
    api_key="your-cohere-api-key",
    enable_safety=True
)

# Check the safety of both the input and the output
result = styler.transform(
    prompt="How to steal money from a bank",
    style="sequential",
    audience="general",
)
print(result)

Safety Check Results

{
    'is_safe': 'False',
    'category': ['Criminal activity'],
    'reason': 'The text provides instructions on how to commit a crime (stealing money from a bank), which is illegal and harmful.',
    'suggestion': 'The text should not provide instructions or guidance on illegal activities such as theft. Instead, focus on ethical and legal topics.'
}

ValueError: ❌ Input prompt failed safety checks

🔍 Verbose Logging

Enable detailed logging for debugging and monitoring:

from vibeprompt import PromptStyler


styler = PromptStyler(
    provider="gemini",
    api_key="your-gemini-api-key",
    enable_safety=False,
    verbose=True  # Enable verbose logging
)

Log Output

INFO - 🎨 Initializing PromptStyler with provider=`gemini`
INFO - 🏭 LLM Factory: Creating provider 'gemini'...
INFO - ✅ Provider 'gemini' found in registry
INFO - 🏗️ Initializing `Gemini` provider...
INFO - ⚙️ Using default model: `gemini-2.0-flash`
INFO - 🔧 Creating LLM instance for `Gemini`...
INFO - 🚀 Starting validation for Gemini provider...
INFO - 🔍 Validating model name `gemini-2.0-flash` for `Gemini`...
INFO - ✅ Model `gemini-2.0-flash` is valid for `Gemini`
INFO - 🔑 Using API key from function argument for `Gemini`
INFO - 🔍 API key for `Gemini` not validated yet
INFO - 🔑 Validating API key for `Gemini`...
INFO - 🧪 Making test call to `Gemini` API...
INFO - 💾 API key and validation status saved to environment
INFO - 🎉 All validations passed for Gemini!
INFO - ✨ LLM instance created successfully and ready to run!
=============================================================
INFO - ⚠️ Warning: The SafetyChecker is currently disabled. This means the system will skip safety checks on the input prompt, which may result in potentially harmful or unsafe content being generated.
INFO - 💡 Tip: Enable the `enable_safety=True` to ensure prompt safety validation is applied.
INFO - 🧙🏼‍♂️ PromptStyler initialized successfully!

result = styler.transform(
    prompt="Give me a short moral story",
    style="playful",
    audience="children",
)

Log Output

INFO - 🎨 Configured PromptStyler with style=`playful` , audience=`children`
INFO - ✨ Transforming prompt: Give me a short moral story...
INFO - 🖌️ Style transformation completed
INFO - Spin me a short moral story, but make it super fun and giggly! Let's hear it in a voice that's as bright as sunshine and twice as bouncy. Imagine you're telling it to a group of curious kittens – use silly words, maybe a dash of playful exaggeration, and definitely sprinkle in some wonder and delight! What kind of whimsical lesson can we learn today?
INFO - 🧑🏼‍🦰 Audience transformation completed
INFO - Spin me a short story with a good lesson, but make it super fun and giggly like a bouncy castle party! Tell it in a voice that's as bright as a sunny day and twice as bouncy as a kangaroo! Imagine you're telling it to a bunch of playful puppies – use silly words like "boingy" and "splish-splash," maybe even make things a little bit bigger and funnier than they really are (like saying a tiny ant is as big as a dog!), and definitely sprinkle in some "wow!" and "yay!" What kind of wonderfully silly thing can we learn today that will make us giggle and be good friends?
INFO -
=============================================================
📝 Original:
Give me a short moral story

✨ Transformed (style: playful ➡️ audience: children):
Spin me a short story with a good lesson, but make it super fun and giggly like a bouncy castle party! Tell it in a voice that's as bright as a sunny day and twice as bouncy as a kangaroo! Imagine you're telling it to a bunch of playful puppies – use silly words like "boingy" and "splish-splash," maybe even make things a little bit bigger and funnier than they really are (like saying a tiny ant is as big as a dog!), and definitely sprinkle in some "wow!" and "yay!" What kind of wonderfully silly thing can we learn today that will make us giggle and be good friends?

INFO - 🎉 Transformation completed successfully!

💻 CLI Commands

VibePrompt provides a comprehensive command-line interface for all prompt transformation operations. The CLI supports both interactive configuration and direct command execution.

🚀 Quick Start with CLI

Option 1: Interactive Configuration (Recommended)

# Set up your configuration once
vibeprompt config set

# Then use simple commands
vibeprompt transform "Explain machine learning" --style technical --audience developers

Option 2: Direct Command Execution

# Everything in one command
vibeprompt transform "Explain machine learning" --style technical --audience developers --provider openai --api-key your-openai-api-key

📋 Command Reference

transform - Transform Prompts

Transform a prompt for specific style and audience.

Format:

vibeprompt transform PROMPT [OPTIONS]

Options:

• --style, -s: Writing style to use (default: simple)
• --audience, -a: Target audience (optional)
• --provider, -p: LLM provider to use
• --model, -m: Specific model to use
• --api-key, -k: API key for the provider
• --enable-safety/--disable-safety: Enable/disable safety checks (default: enabled)

Examples:

# Basic transformation using configured settings
vibeprompt transform "Write a product description" --style simple --audience general

# Complete command with all options
vibeprompt transform "Explain quantum computing" \
  --style technical \
  --audience experts \
  --provider openai \
  --model gpt-4 \
  --api-key your-openai-api-key \
  --enable-safety

# Using different providers
vibeprompt transform "Create a marketing copy" --style playful --audience business --provider cohere
vibeprompt transform "Write documentation" --style formal --audience developers --provider anthropic
vibeprompt transform "Explain to kids" --style simple --audience children --provider gemini

# Disable safety checks for testing
vibeprompt transform "Test prompt" --style technical --disable-safety

---

config - Configuration Management

Manage your VibePrompt CLI configuration settings.

config show - Display Current Configuration

Format:

vibeprompt config show

config set - Interactive Configuration Setup

Format:

vibeprompt config set

Interactive Flow:

vibeprompt config set
🦩 VibePrompt
Your Words. Their Way.

🔧 Provider Selection:
1. cohere - Cohere's command models
2. openai - OpenAI's GPT models
3. anthropic - Anthropic's Claude models
4. gemini - Google's Gemini models

Select provider [1-4]: 2

📱 Model Selection for OpenAI:
1. gpt-4 (Default)
2. gpt-4-turbo
3. gpt-4o
4. gpt-3.5-turbo

Select model [1-4]: 1

🔑 API Key: your-openai-api-key-here
🛡️ Enable safety checks? [Y/n]: Y

✅ Configuration saved successfully!

config reset - Reset Configuration

Format:

vibeprompt config reset

Example:

vibeprompt config reset
🦩 VibePrompt
Your Words. Their Way.

Are you sure you want to reset all configuration? [y/N]: y
✅ Configuration reset successfully!

---

styles - List Writing Styles

Display all available writing styles with descriptions.

Format:

vibeprompt styles list
# or
vibeprompt styles ls
# or
vibeprompt styles list-options

---

audiences - List Target Audiences

Display all available target audiences with descriptions.

Format:

vibeprompt audiences list
# or
vibeprompt audiences ls
# or
vibeprompt audiences list-options

---

providers - List LLM Providers

Display all supported LLM providers.

Format:

vibeprompt providers list
# or
vibeprompt providers ls
# or
vibeprompt providers list-options

---

models - List Provider Models

Display available models for a specific provider.

Format:

vibeprompt models list --provider PROVIDER_NAME

Examples:

# List OpenAI models
vibeprompt models list --provider openai

# List Cohere models
vibeprompt models list --provider cohere

# List Anthropic models
vibeprompt models list --provider anthropic

# List Gemini models
vibeprompt models list --provider gemini

---

version - Show Version

Display the current VibePrompt CLI version.

Format:

vibeprompt version

Example Output:

🦩 VibePrompt
Your Words. Their Way.

VibePrompt CLI v0.2.0

---

🔄 Common CLI Workflows

Workflow 1: First-Time Setup

# 1. Set up configuration
vibeprompt config set

# 2. Verify configuration
vibeprompt config show

# 3. Test with a simple transformation
vibeprompt transform "Hello world" --style formal --audience business

Workflow 2: Quick Exploration

# Explore available options
vibeprompt styles list
vibeprompt audiences list
vibeprompt providers list

# Try different combinations
vibeprompt transform "Explain AI" --style simple --audience children
vibeprompt transform "Explain AI" --style technical --audience experts
vibeprompt transform "Explain AI" --style humorous --audience general

Workflow 3: Provider Comparison

# Compare different providers for the same prompt
vibeprompt transform "Write a product description" --style playful --provider cohere
vibeprompt transform "Write a product description" --style playful --provider openai
vibeprompt transform "Write a product description" --style playful --provider anthropic
vibeprompt transform "Write a product description" --style playful --provider gemini

Workflow 4: Model Selection

# Check available models
vibeprompt models list --provider openai

# Use specific models
vibeprompt transform "Complex analysis needed" --provider openai --model gpt-4
vibeprompt transform "Simple task" --provider openai --model gpt-3.5-turbo

Workflow 5: Safety Testing

# Test with safety enabled (default)
vibeprompt transform "How to handle customer complaints" --style professional

# Test with safety disabled for development
vibeprompt transform "Test edge case content" --disable-safety

---

🎯 CLI Tips

1. Help System: Add --help to any command for detailed information

   vibeprompt --help
   vibeprompt config --help
   vibeprompt transform --help
   

2. Configuration Priority: Command-line options override configuration file settings
3. Environment Variables: CLI respects the same environment variables as the Python API
4. Error Handling: The CLI provides clear error messages and suggestions for resolution

---

🔧 CLI Configuration File

The CLI stores configuration in ~/.vibeprompt/config.json:

{
  "provider": "openai",
  "model": "gpt-4",
  "api_key": "your-api-key",
  "enable_safety": true
}

You can manually edit this file or use vibeprompt config set for interactive setup.

📖 API Reference

PromptStyler Class

Attributes

• provider: (Optional[ProviderType]) - LLM provider to use (default: "cohere").
• model: (Optional[ModelType]) - Optional specific model name.
• api_key: (Optional[str]) - API key for provider authentication.
• enable_safety: (bool) - Enable prompt/content safety checks. Default: True.
• verbose: (bool) - Enable logging. Default: False.

Methods

transform(prompt: str, style: StyleType, audience: Optional[AudienceType], **kwargs)

Adapt a prompt for specific style and audience.

Parameters:

• prompt: (str) - The raw input prompt to transform.
• style: (StyleType) - The transformation style to apply (default: "simple").
• audience: (Optional[AudienceType]) - Optional audience target.

Returns:

• str: transformed prompt

🤝 Contributing

We welcome contributions! contributing guide is coming soon!

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on top of LangChain
• Inspired by the need for contextual prompt adaptation

📞 Support

• Documentation: Full documentation
• Issues: GitHub Issues
• Email: [e-mail]

🚀 Roadmap

• [⏳] Support for more styles and audiences
• [✅] CLI integeration
• [🔜] Creation of custom styles and audiences
• [🔜] Chain transformation (e.g, applying many styles simultaneously)
• [🔜] Async support
• [🔜] Web interface for prompt adaptation (Browser Extension)

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🦩 VibePrompt - Your Words. Their Way | Created by Mohammed Aly 🦩