semvec/pypi-packages huggingface dataset:
pointofpresence 0.5.2
A Python client library for interacting with the specific API.
Requires Python: >=3.6

[![Contributor Covenant](https://img.shields.io/badge/code%20of%20conduct-Contributor%20Covenant-brightgreen.svg)](CODE_OF_CONDUCT.md)

# Point Of Presence Python Client Library

A Python client library for interacting with a specific POP API. This library provides easy-to-use methods for performing GET, POST, PUT, PATCH and DELETE requests, with built-in authentication support.

## Table of Contents

- [Installation](https://github.com/sci-ndp/pop-py/blob/main/README.md#installation)
- [Tutorial](https://github.com/sci-ndp/pop-py/blob/main/README.md#tutorial)
- [Running Tests](https://github.com/sci-ndp/pop-py/blob/main/README.md#running-tests)
- [Contributing](https://github.com/sci-ndp/pop-py/blob/main/README.md#contributing)
- [License](https://github.com/sci-ndp/pop-py/blob/main/README.md#license)

## Installation

Ensure you have Python 3.6 or higher installed. It's recommended to use a virtual environment.

### Option 1: Install from GitHub

1. **Clone the repository:**

   ```bash
   git clone https://github.com/sci-ndp/pop-py.git
   cd pop-py
   ```
2. **Create and activate a virtual environment:**

   ```bash
   python3 -m venv .venv
   source .venv/bin/activate
   ```
3. **Install the package in editable mode:**

   ```bash
   pip install -e .
   ```
4. **Install development dependencies (optional, for testing):**

   ```bash
   pip install -r requirements.txt
   ```

### Option 2: Install via pip

Once the package is published on PyPI, you can install it directly using pip:

```bash
pip install pointofpresence
```

## Tutorial

For a step-by-step guide on how to use the `pointofpresence` library, check out our comprehensive tutorial: [10 Minutes for a Point of Presence](https://github.com/sci-ndp/pop-py/blob/main/docs/point_of_presence_tutorial_0.5.2.ipynb).

## Running Tests

To run the tests, navigate to the project root and execute:

```bash
pytest
```

## Contributing

Contributions are welcome! Please follow these steps:

1. **Fork the repository**
2. **Create a new branch** (`git checkout -b feature/new-feature`)
3. **Make your changes** and **commit** (`git commit -m 'Add new feature'`)
4. **Push** to the branch (`git push origin feature/new-feature`)
5. **Open a Pull Reques**

## License

This project is licensed under the MIT License. See the [LICENSE](https://github.com/sci-ndp/pop-py/blob/main/LICENSE) file for details.
mcp-llms-txt 0.2.0
Add your description here
Requires Python: >=3.11

# mcp-llms-txt

[![smithery badge](https://smithery.ai/badge/@SecretiveShell/MCP-llms-txt)](https://smithery.ai/server/@SecretiveShell/MCP-llms-txt)

MCP server for [Awesome-llms-txt](https://github.com/SecretiveShell/Awesome-llms-txt). Add documentation directly into your conversation via mcp resources.

<a href="https://glama.ai/mcp/servers/kqwhhpe8l7"><img width="380" height="200" src="https://glama.ai/mcp/servers/kqwhhpe8l7/badge" alt="MCP-llms-txt MCP server" /></a>

## Installation

View a setup guide + example usage on [pulsemcp.com](https://www.pulsemcp.com/use-cases/utilize-llm-txt-files/secretiveshell-claude-llmstxt)

### Installing via Smithery

To install MCP Server for Awesome-llms-txt for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@SecretiveShell/MCP-llms-txt):

```bash
npx -y @smithery/cli install @SecretiveShell/MCP-llms-txt --client claude
```

### Manual Installation
Setup your claude config like this:

```json
{
    "mcpServers": {
        "mcp-llms-txt": {
            "command": "uvx",
            "args": ["mcp-llms-txt"],
            "env": {
                "PYTHONUTF8": "1"
            }
        }
    }
}
```

## testing

Use [mcp-cli](https://github.com/wong2/mcp-cli) to test the server:

```bash
npx -y "@wong2/mcp-cli" -c config.json
```

The config file is already setup and does not need to be changed since there are no api keys or secrets.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License.
otaro 0.0.12
Auto-optimization of LLM tasks
Requires Python: >=3.9

# otaro

```
uv add otaro
```

Documentation: https://silanthro.github.io/otaro

## Example

Create a config YAML file to describe the task.

```yaml
# A sample config for a haiku writing task

model: gemini/gemini-2.0-flash-001

inputs:
- topic

outputs:
- haiku
```

```py
from otaro import Task

task = Task.from_config("poet.yml")
response = task.run(topic="frog")
print(response.haiku)

"""
Green skin on blue pond,
A croaking song fills the air,
Summer's gentle kiss.
"""
```

See `./examples` for more examples and configs.

## Deploy API

Run `otaro` to deploy a config file as an API.

```
$ otaro examples/poet.yml
```

Navigate to "https://localhost:8000" to view and test the API documentation.