semvec/pypi-packages huggingface dataset:
neuromotifs 0.1.0a2
Geometry-aware network motif analysis for neocortical microcircuits
License: MIT
Requires Python: >=3.10
Keywords: neuroscience, motifs, networks, geometry, microcircuit

# neuromotifs
Python tools to load neuronal microcircuit geometry, generate geometry-aware null models, and quantify over/under-expression of 3-node motifs.

> Paper: *Neuron Morphological Asymmetry Explains Fundamental Network Stereotypy Across Neocortex* (Gal et al.)

## Install
```bash
pip install neuromotifs
# or, for dev
pip install -e .[dev]
```

## Highlights
- Motif counting for directed triplets (#1-#13)
- Geometry-driven random graph generators (1st-5th order) mirroring the paper’s models
- Reproducibility notebooks for Figures 1-4
- Simple CLI: `neuromotifs motifs`, `neuromotifs generate`, `neuromotifs fit`

## Quickstart
```python
# TBD
```

## Data
- `data/nmc/` contains tiny demonstrators only.
- For full datasets, see `data/README.md` for scripted download instructions.

## Citing
Please cite the paper and this package (see `CITATION.cff`).
able-recipe 1.0.16
Bluetooth Low Energy for Android
License: MIT
Keywords: android ble bluetooth kivy

Android Bluetooth Low Energy
============================

.. start-badges
.. image:: https://img.shields.io/pypi/v/able_recipe.svg
    :target: https://pypi.python.org/pypi/able_recipe
    :alt: Latest version on PyPi
.. end-badges

`Python <https://github.com/kivy/python-for-android>`_ interface to Android Bluetooth Low Energy API.

:Code repository: https://github.com/b3b/able
:Documentation: https://herethere.me/able
:Changelog: https://github.com/b3b/able/blob/master/CHANGELOG.rst

Quick start development environment
-----------------------------------

*able* is included in `PythonHere <https://herethere.me/pythonhere>`_ app, together with the `Jupyter Notebook <https://jupyter.org/>`_ it could be used as a development environment.

Usage example: https://herethere.me/pythonhere/examples/ble.html


Build
-----

The following instructions are for building app with `buildozer <https://github.com/kivy/buildozer/>`_ tool.

*able_recipe* recipe should be added to buildozer.spec requirements::

   requirements = python3,kivy,android,able_recipe


Bluetooth permissions should be requested in buildozer.spec::

    android.permissions = BLUETOOTH, BLUETOOTH_ADMIN, BLUETOOTH_SCAN, BLUETOOTH_CONNECT, BLUETOOTH_ADVERTISE, ACCESS_FINE_LOCATION


App configuration example: `buildozer.spec <https://github.com/b3b/able/tree/master/examples/alert/buildozer.spec>`_


Build with a local version
--------------------------

To build app with a local (modified) version of *able*,

path to *able* recipes directory should be set in buildozer.spec::

    p4a.local_recipes = /path/to/cloned/repo/recipes


Contributors
------------

Thanks,

.. csv-table::

    `andfmart <https://github.com/andfmart>`_
    `andreamerello <https://github.com/andreamerello>`_
    `datmaniac95  <https://github.com/datmaniac95>`_
    `dgatf <https://github.com/dgatf>`_
    `dwmoffatt <https://github.com/dwmoffatt>`_
    `Enkumicahel <https://github.com/Enkumicahel>`_
    `hailesir <https://github.com/hailesir>`_
    `HelaFaye <https://github.com/HelaFaye>`_
    `jacklinquan <https://github.com/jacklinquan>`_
    `juasiepo <https://github.com/juasiepo>`_
    `MininDMhvh <https://github.com/MininDMhvh>`_
    `PapoKarlo <https://github.com/PapoKarlo>`_
    `quasarsuport-del <https://github.com/quasarsuport-del>`_
    `RoberWare <https://github.com/RoberWare>`_
    `Rowataro <https://github.com/Rowataro>`_
    `robgar2001 <https://github.com/robgar2001>`_
    `sodef <https://github.com/sodef>`_
    `sooko_io <https://github.com/sooko>`_
    `woutersj <https://github.com/woutersj>`_
vibeteam 0.1.1
MCP server for orchestrating sub-agents based on query analysis
License: MIT
Requires Python: >=3.10
Keywords: agents, ai, claude, mcp, orchestration

# Vibeteam

An MCP (Model Context Protocol) server that intelligently orchestrates sub-agents based on query analysis and project context.

## Features

- **Query Analysis**: Understands user intent (build, debug, feature, refactor, research, etc.)
- **Project Detection**: Automatically identifies project type (backend, frontend, fullstack, library)
- **Smart Agent Orchestration**: Deploys specialized agents based on project context and query intent
- **Parallel Execution**: Runs multiple agents concurrently for faster results
- **Tool Detection**: Automatically adapts to Cursor, Windsurf, VS Code, Claude Desktop, etc.
- **Multi-Agent Support**:
  - Backend Specialist
  - Frontend Specialist
  - Web Researcher
  - Code Analyzer
  - Test Engineer
  - DevOps Specialist

## Installation

### Via uvx (Recommended)

Once published to PyPI, you can install and run directly with uvx:

```bash
uvx vibeteam
```

### Via pip

```bash
pip install vibeteam
```

### From Source

```bash
git clone https://github.com/XiaoConstantine/vibeteam.git
cd vibeteam
uv sync
uv run vibeteam
```

## MCP Configuration

Add to your Claude Desktop or MCP client configuration:

```json
{
  "servers": {
    "vibeteam": {
      "command": "uvx",
      "args": ["vibeteam"]
    }
  }
}
```

Or if installed via pip:

```json
{
  "servers": {
    "vibeteam": {
      "command": "vibeteam"
    }
  }
}
```

## Usage

### As MCP Server

The server runs as an MCP server and exposes tools for Claude or other MCP clients:

```bash
# Run with uvx
uvx vibeteam

# Or if installed
vibeteam
```

### Testing

```bash
uv run python test_server.py
```

## Tools Available

1. **analyze_and_orchestrate**: Main orchestration tool that analyzes query and deploys agents
2. **analyze_project**: Analyzes project structure and technology stack
3. **analyze_query**: Determines query intent and complexity

## How It Works

1. **Project Analysis**: Scans the current working directory to understand project structure
2. **Query Understanding**: Analyzes user query to determine intent and complexity
3. **Agent Planning**: Based on project type and query intent, plans appropriate agents
4. **Orchestration**: Executes agents in priority order and collects results
5. **Summary**: Provides consolidated output from all agents

## Architecture

The orchestrator uses a sophisticated analysis system to:
- Detect backend (Python, Go, Java, etc.) and frontend (React, Vue, Angular) technologies
- Understand query complexity and required expertise
- Deploy agents with appropriate priority levels
- Coordinate multi-agent workflows for complex tasks