semvec/pypi-packages huggingface dataset:
gitagent-ai 1.0.5
AI-powered Git assistant with persistent context management
License: MIT
Requires Python: >=3.8
Keywords: git, ai, assistant, automation, version-control

# GitAgent - Unified AI-Powered Git Assistant

**GitAgent** is a unified AI-powered Git assistant that provides intelligent, context-aware Git operations with persistent session management and automatic command verification.


## 🚀 Key Features

### 📝 **Persistent Context Management**
- **Session persistence** across multiple invocations
- **Workflow resumption** for interrupted operations  
- **Command history** and verification tracking

### ✅ **Advanced Command Verification**
- **Automatic success verification** after each command execution
- **Semantic verification** beyond just error codes
- **Intelligent error recovery** with specific issue identification
- **Pre-execution prerequisite checking**

### 🔄 **Multi-Step Workflow Management**
- **Intelligent workflow decomposition** for complex operations
- **Step-by-step execution** with user confirmation
- **Context preservation** across workflow steps
- **Automatic workflow completion** tracking


## 🛠️ Installation

1. **Install the package:**
   ```bash
   pip install gitagent-ai
   ```

3. **Setup User:**
   ```bash
   gitagent-setup
   ```

4. **Set up your Groq API key** (currently need to contact admin : vivekskale03@gmail.com):


## 📖 Usage

```bash
# Interactive mode - ask any Git question
gitagent "What's the current status of my repository?"

# Multi-step operations
gitagent "Stage all changes, commit with a meaningful message, and push to origin"

# Branch operations  
gitagent "Delete current branch and create a new feature branch called user-auth"
```


## 🔧 Architecture

### Unified Agent Design
- **Single LangGraph workflow** handles all scenarios
- **State management** with persistent sessions
- **Command verification** at each step
- **Intelligent routing** based on context and intent

### Session Persistence
- Full command history with verification results
- Workflow context and step tracking
- Resumable across GitAgent invocations

### Verification System
- **Pre-execution** prerequisite checking
- **Post-execution** state verification
- **Semantic validation** of command outcomes
- **Detailed error reporting** with recovery suggestions


## 🆘 Support

If you encounter any issues or have questions:
Contact support : vivekskale03@gmail.com

---

**GitAgent** - Intelligent Git assistance with persistent context and reliable execution tracking.
tpfeat 0.0.1
A flexible and modular time-point feature extraction toolkit for sensor time series.
License: MIT
Requires Python: <4.0,>=3.10

# tpfeat

**tpfeat** is a lightweight, modular framework for extracting custom features from sliding time windows, with full support for multi-axis sensor data, temporal interactions, and integration with TSFEL/tsfresh.

## Features

- Flexible windowing in all directions
- Modular axis, time, and feature operations
- Time-point interaction features
- Designed for multi-axis sensor data

## Installation

Install from source using pip:

```bash
pip install tpfeat
```
passive-agent 0.1.0
A passive agent for prompt engineering experiments with instruction-based context building
License: MIT
Requires Python: >=3.8
Keywords: ai, context-building, llm, openai, openrouter, prompt-engineering

# passive-agent

A passive agent for prompt engineering experiments with instruction-based context building.

## Overview

Passive Agent is a flexible tool for building complex contexts by processing instructions that combine file content with AI completions. It's designed for prompt engineering experiments where you need to iteratively build context from multiple sources.

## Features

- 📄 Load content from files and directories
- 🤖 Integrate AI completions into context (supports OpenAI and OpenRouter)
- 🔄 Sequential processing with context accumulation
- 📊 Token usage tracking and reporting
- 🎯 Simple instruction-based workflow

## Installation

```bash
pip install passive-agent
```

Or install from source:
```bash
git clone https://github.com/yourusername/passive-agent
cd passive-agent
pip install -e .
```

## Quick Start

1. Create an `INSTRUCT.md` file with your instructions:
   ```
   @header.md
   @data/
   /completion
   @footer.md
   ```

2. Run passive-agent:
   ```bash
   passive-agent
   ```

3. Check the generated files:
   - `CONTEXT.md` - Complete context with all content
   - `COMPLETION.json` - Raw API response
   - `COMPLETION.md` - Completion text

## Example

See the `example/` directory for a complete working example:

```bash
cd example
passive-agent
```

## Configuration

### OpenAI
```bash
export OPENAI_API_KEY="your-key"
passive-agent
```

### OpenRouter
```bash
export OPENROUTER_API_KEY="your-key"
export OPENROUTER_MODEL="anthropic/claude-3-opus"
passive-agent
```

## Documentation

For detailed usage instructions, see [USAGE.md](USAGE.md).

## License

MIT License - see [LICENSE](LICENSE) file for details.
prosen-pythonlib 0.1.3
A simple greeting package
License: MIT
Requires Python: >=3.6

# prosen-pythonlib

[![PyPI Version](https://img.shields.io/pypi/v/prosen-pythonlib)](https://pypi.org/project/prosen-pythonlib/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

A simple Python library for greeting messages.
## ðŸ“¦ Available on PyPI: [prosen-pythonlib](https://pypi.org/project/prosen-pythonlib/)

## Features

- A straightforward `hello()` function that returns a personalized greeting.
- Easy to install and use.

## Installation

You can install the package directly from PyPI using pip:

```bash
pip install prosen-pythonlib
```
## Usage
Here's a quick example on how to use prosen-pythonlib:
```
from mylib import hello

print(hello("Prosen Sir"))
# Output: Hello, Prosen Sir!
```
## Development
To contribute or develop locally:

1. Clone this repository:
```
git clone https://github.com/pronad1/Python-Library.git

```
2. Navigate into the project directory:
```
   cd Python-Library

```
3. Create and activate a virtual environment (optional but recommended):
```
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
```
4. Install dependencies:
```
pip install -r requirements.txt
```
5. Make your changes and commit.

   
## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contact

For questions or suggestions, reach out to [Prosenjit Mondol](https://github.com/pronad1).