semvec/pypi-packages huggingface dataset:
xeni 0.1.1
xeni - A CLI program to manage all your MCPs
Requires Python: >=3.11
Keywords: AI, cli-mcp-manager, fastapi, mcp, xeni

# Xeni 🧠

Xeni is a modular Python framework designed for building intelligent agents and automation pipelines. It emphasizes extensibility, configuration-driven workflows, and ease of integration with various models or tools.

---

## 🚀 Features

- ⚙️ Modular architecture with customizable adapters  
- 📁 Configuration-driven behavior using YAML/JSON  
- 🧪 Includes testable CLI for development and debugging  
- 🧵 Easily extensible with your own modules and logic  

---

## 📦 Installation

```bash
git clone https://github.com/ZotacMaster/Xeni.git
cd Xeni
pip install .
```
Or, install via pipx or pip
```bash
pipx install xeni
pip install xeni
```

## 📂 Project Structure
```bash 
Xeni
└── src
    └── xeni
        ├── http_proxy
        │   ├── endpoints
        │   │   ├── __init__.py
        │   │   ├── search.py
        │   │   ├── insert.py
        │   │   └── healthcheck.py
        │   └── server.py
        ├── mcp_proxy
        │   ├── adapters
        │   │   ├── __init__.py
        │   │   ├── base.py
        │   │   ├── claude.py
        │   │   ├── cursor.py
        │   │   └── cline.py
        │   └── server.py
        ├── utils
        │   ├── __init__.py
        │   ├── config.py
        │   ├── file_manager.py
        │   └── models.py
        ├── __init__.py
        └── cli.py
.gitignore
.python-version
LICENCE
pyproject.toml
uv.lock
```

## 🧑‍💻 Usage
Commands avialable to xeni:
```bash
xeni start #starts the FastAPI server

xeni connect ${agent_name} #Connect to supported agents

xeni key ${API_key} --token ${Bearer token} #Sets the user credentials

xeni set-url ${url}
```
llm-md 0.1.0
CLI tool for generating LLM context from GitHub repositories
Requires Python: >=3.13

# llm-md

A CLI tool for generating LLM context from GitHub repositories. This tool scans through files in a repository and creates a single markdown file containing all relevant code, making it easy to provide context to Large Language Models.

## Features

- Automatically respects `.gitignore` patterns
- Automatically detects and uses `llm.md` configuration file in repository root
- Additional filtering via `llm.md` configuration file
- Generates table of contents for easy navigation
- Syntax highlighting for various programming languages
- Skips binary and non-text files automatically

## Installation

```bash
# Install using uv
uv pip install -e .

# Or install directly
pip install -e .
```

## Usage

Basic usage:
```bash
llm-md /path/to/repo
```

Specify output file:
```bash
llm-md /path/to/repo -o context.md
```

Override the llm.md configuration file:
```bash
llm-md /path/to/repo -c custom-llm.md
```

Verbose output:
```bash
llm-md /path/to/repo -v
```

## Configuration

### .gitignore

The tool automatically reads and respects `.gitignore` patterns in the repository root.

### llm.md

The tool automatically detects and uses an `llm.md` file if present in the repository root. You can override this by specifying a different configuration file with the `-c` option.

Create an `llm.md` file to control which files are included:

```
# Example llm.md

INCLUDE:
# If this section has patterns, ONLY these files will be included
src/**/*.py
docs/*.md

EXCLUDE:
# These files will be excluded (in addition to .gitignore)
tests/**
*.test.js
```

Pattern syntax follows gitignore conventions:
- `*` matches any characters except `/`
- `**` matches any number of directories
- `?` matches any single character
- `[abc]` matches any character in the brackets
- `!` at the start negates the pattern

## Output Format

The generated markdown file includes:
1. Header with metadata (timestamp, repository path, file count)
2. Table of contents with links to each file section
3. Each file's content in a code block with appropriate syntax highlighting

## Example

```bash
# Generate context for the current directory (auto-detects llm.md if present)
llm-md .

# Generate context for a specific project
llm-md ~/projects/my-app -o my-app-context.md

# Use a different llm.md file than the one in the repository
llm-md ~/projects/my-app -c ~/configs/python-only.md

# Include only Python files (create llm.md in the repo)
echo "INCLUDE:\n**/*.py" > llm.md
llm-md .
```

## License

MIT
speaky 0.1.1
Text to speech with voice cloning
Requires Python: >=3.12.8

# Speaky

**Text‑to‑Speech with Voice Cloning made easy with Chatterbox TTS**

Generate natural‑sounding speech from plain text—locally on your GPU/CPU using a single, ergonomic command‑line tool.

## Features

* **Voice cloning** from a short audio prompt (optional)
* **Built‑in voices** accessible via `-v NAME` (or `-v ALL` for every voice)
* **Emotion control** via exaggeration & classifier‑free guidance
* **Device auto‑detection** — Apple Silicon (*mps*), CUDA GPUs, or CPU
* **Smart sentence chunking** (NLTK) to handle long passages gracefully
* **Trailing‑silence trimming** so outputs end crisply
* **Glitch / clipping detection** heuristic for cleaner audio
* **Verification via transcription** (Distil‑Whisper) to catch missing words

## 🚀 Quickstart (CLI)
Run without installation:
```bash
uvx speak "Do or do not. There is no try."  --voice vader
```

or install first:
```bash
uv tool install speaky

speak --file speech.txt  --voice attenborough
```

Run `speak --help` for the full list of options.

## 🐍 Python API

```python
from pathlib import Path
from speaky.core import batch_synthesize

batch_synthesize(
    inputs=[("Hello there!", "greeting")],  # (text, stem)
    output_dir=Path("out"),
)
```

## Examples
https://github.com/user-attachments/assets/f1956d1a-c407-4944-b9f8-a5402f71cbd8

https://github.com/user-attachments/assets/8e6a81df-773c-4d48-876b-4cced0b4f643

https://github.com/user-attachments/assets/2c2884e2-759a-4b1d-884c-c1d5eed9ddf0

https://github.com/user-attachments/assets/2d70a497-b156-40d8-907f-0beb33ea68d6

https://github.com/user-attachments/assets/0ab6195d-ae9e-420e-bde2-719dad772563

https://github.com/user-attachments/assets/aeabd1d9-3839-4190-8460-8ad2e364dd02

## Credit

This project is built on top of [chatterbox open-source TTS](https://github.com/resemble-ai/chatterbox).

## Development

### Quick Commands
 - `make init` create the environment and install dependencies
 - `make help` see available commands
 - `make af` format code
 - `make lint` run linter
 - `make typecheck` run type checker
 - `make test` run tests
 - `make check` run all checks (format, lint, typecheck, test)
 - `uv add pkg` add a python dependency
 - `uv run -- python foo/bar.py` run arbitrary command in python env

### Code Conventions

- Always run `make checku` after making changes.

#### Testing
- Use **pytest** (no test classes).
- Always set `match=` in `pytest.raises`.
- Prefer `monkeypatch` over other mocks.
- Mirror the source-tree layout in `tests/`.

#### Exceptions
- Catch only specific exceptions—never blanket `except:` blocks.
- Don't raise bare `Exception`.

#### Python
- Manage env/deps with **uv** (`uv add|remove`, `uv run -- ...`).
- No logging config or side-effects at import time.
- Keep interfaces (CLI, web, etc.) thin; put logic elsewhere.
- Use `typer` for CLI interfaces, `fastapi` for web interfaces, and `pydantic` for data models.
accurate-hijri 0.1.3
Accurate Hijri-Gregorian converter using Umm al-Qura calendar data
Requires Python: >=3.7

# Accurate Hijri

📅 **Accurate Hijri–Gregorian Converter**  
A Python package for accurate two-way conversion between Gregorian and Hijri dates using the Umm al-Qura calendar dataset.

---

## ✨ Features

- ✅ Convert **Gregorian to Hijri**
- ✅ Convert **Hijri to Gregorian**
- 📚 Based on **official Umm al-Qura** calendar data
- 🔁 Supports **round-trip conversion**
- ⚠️ Handles **invalid or out-of-range** dates with clear errors

---

## 📦 Installation

First, ensure you have Python 3.7+ and `pip` installed:

```bash
pip install accurate-hijri
from accurate_hijri import AccurateHijriConverter, HijriDate
from datetime import datetime

# Load the converter
converter = AccurateHijriConverter()

# Convert Gregorian → Hijri
g_date = datetime(2024, 4, 10)
hijri = converter.to_hijri(g_date)
print(hijri)  # Output: 1 Shawwal 1445 AH

# Convert Hijri → Gregorian
h_date = HijriDate(1445, 10, 1)
gregorian = converter.to_gregorian(h_date)
print(gregorian.date())  # Output: 2024-04-10
```

🙏 Acknowledgments
Umm al-Qura Calendar Data: Based on the official Saudi calendar dataset.

Thanks to the Python open-source community for the incredible tools.

Heartfelt gratitude to my mother and sister for their constant encouragement, love, and support throughout this project.

📄 License
Licensed under the MIT License.