semvec/pypi-packages huggingface dataset:
slack-pat-mcp 0.1.0
Model Context Protocol (MCP) server for Slack using PAT (Personal Access Token)
Requires Python: >=3.10
Keywords: ai, claude, mcp, model-context-protocol, slack

# slack-pat-mcp

MCP server for personal Slack access using user tokens (xoxp-). 4 tools covering channels, DMs, chat, search, and user management.

## Install

```bash
uvx slack-pat-mcp
```

## Claude Code Config

```json
"slack": {
    "command": "uvx",
    "args": ["slack-pat-mcp"],
    "env": {
        "SLACK_USER_TOKEN": "xoxp-...",
        "SLACK_TEAM_ID": "T..."
    }
}
```

## Tools

- **slack_channel** — list, list_dms, history, thread, open_dm
- **slack_chat** — post, update, delete, react_add, react_remove
- **slack_search** — search messages with Slack syntax
- **slack_users** — list, info, profile, usergroups

## Required Slack OAuth Scopes (User Token)

channels:read, channels:history, groups:read, groups:history, im:read, im:history, im:write, mpim:read, mpim:history, mpim:write, chat:write, reactions:read, reactions:write, search:read, users:read, users:read.email, users.profile:read, usergroups:read
macaronipm 3.0.0
A PenguinMod API Wrapper
Requires Python: >=3.9

# MacaroniPM | A PenguinMod API wrapper

## Instalation

You can download the latest version of macaronipm via `pip install macaronipm`

## New functions

`macaronipm.user.UserExist()`

`macaronipm.user.IsBanned()`

`macaronipm.user.logout()`

`macaronipm.user.GetMessages()`

`macaronipm.user.getUnreadMessages()`

`macaronipm.IsOnline()`

`macaronipm.project.hasLovedVoted()`
dcscope 2.25.2
User interface for deformability cytometry (DC)
Requires Python: <4,>=3.9
Keywords: RT-DC, DC, deformability, cytometry

|DCscope|
===========

|PyPI Version| |Build Status| |Coverage Status| |Docs Status|


**DCscope** (formerly Shape-Out) is a graphical user interface for the
analysis and visualization of RT-DC datasets.


Documentation
-------------

The documentation, including the code reference and examples, is available at
`dcscope.readthedocs.io <https://dcscope.readthedocs.io>`__.


Installation
------------
Installers for Windows and macOS are available at the `release page <https://github.com/DC-analysis/DCscope/releases>`__.

If you have Python 3 installed, you can install DCscope with

::

    pip install dcscope


Citing DCscope
----------------
Please cite DCscope either in-line

::

  (...) using the analysis software DCscope (formerly Shape-Out) version 2.X.X
  (available at https://github.com/DC-analysis/DCscope).

or in a bibliography

::

  Paul Müller and others (2019), DCscope (formerly Shape-Out) version 2.X.X:
  Analysis software for real-time deformability cytometry [Software].
  Available at https://github.com/DC-analysis/DCscope.

and replace ``2.X.X`` with the version of DCscope that you used.


Testing
-------

::

    pip install -e .
    pip install -r tests/requirements.txt
    pytest tests


.. |DCscope| image:: https://raw.github.com/DC-analysis/DCscope/main/dcscope/img/splash.png
.. |PyPI Version| image:: https://img.shields.io/pypi/v/DCscope.svg
   :target: https://pypi.python.org/pypi/DCscope
.. |Build Status| image:: https://img.shields.io/github/actions/workflow/status/DC-analysis/DCscope/check.yml?branch=main
   :target: https://github.com/DC-analysis/DCscope/actions?query=workflow%3AChecks
.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/DC-analysis/DCscope/main.svg
   :target: https://codecov.io/gh/DC-analysis/DCscope
.. |Docs Status| image:: https://img.shields.io/readthedocs/dcscope
   :target: https://readthedocs.org/projects/dcscope/builds/
audio-workbench-player 0.0.3
Python wrapper for Audio Workbench Player (HTML/Streamlit embedding)
Requires Python: >=3.9
Keywords: audio, player, streamlit, jupyter, embed, waveform, spectrogram

# audio-workbench-player (Python wrapper)

Python helper package to embed `audio-workbench-player` in Streamlit, Jupyter, and other HTML-capable UIs.

## Install (PyPI)

```bash
pip install audio-workbench-player
```

Optional demo dependencies:

```bash
pip install "audio-workbench-player[streamlit]"
pip install "audio-workbench-player[gradio]"
```

## Install (local dev)

```bash
pip install -e .
```

## Usage

```python
from audio_workbench_player import render_daw_player
html = render_daw_player(
    audio_bytes,
    iframe_height=320,
    viewMode="spectrogram",
    transportStyle="hero",
    transportOverlay=True,
    showOverview=False,
    showFileOpen=False,
    showStatusbar=False,
)
```

## Demo Features

- Presets: `Full DAW`, `Compact`, `Preview Waveform Hero`, `Preview Spectrogram Hero`, `Ultra Compact Hero`
- Advanced toggles for all relevant player sections
- Live options preview as JSON

## Streamlit demo

```bash
streamlit run demo_streamlit.py
```

## Gradio demo

```bash
pip install gradio
python demo_gradio.py
```

## License

GNU AGPL-3.0
whoare 0.2.8
Another whois scraper
Requires Python: <4,>=3.6
Keywords: whois, domain, nic.ar

![Lint and test](https://github.com/avdata99/whoare/workflows/Lint%20and%20test/badge.svg?branch=main)
[![GitHub All Releases](https://img.shields.io/github/downloads/avdata99/whoare/total?style=for-the-badge)](https://github.com/avdata99/whoare/releases)
[![GitHub Issues](https://img.shields.io/github/issues/avdata99/whoare?style=for-the-badge)](https://github.com/avdata99/whoare/issues)
[![GitHub PR](https://img.shields.io/github/issues-pr/avdata99/whoare?style=for-the-badge)](https://github.com/avdata99/whoare/pulls)
[![Licence](https://img.shields.io/github/license/avdata99/whoare?style=for-the-badge)](https://github.com/avdata99/whoare/blob/main/LICENSE)
[![Pypi py version](https://img.shields.io/pypi/pyversions/whoare?style=for-the-badge)](https://pypi.org/project/whoare/)
[![Last Commit](https://img.shields.io/github/last-commit/avdata99/whoare?style=for-the-badge)](https://github.com/avdata99/whoare/commits/main)

# A WhoIs parser

Just a `whois` parser

Available countries:
 - `.ar`: Argentina

## Sample

```python
from whoare.whoare import WhoAre
wa = WhoAre()
wa.load('fernet.com.ar')  # optional torify=True to run "torify whois ..."

wa.domain.base_name
'fernet'
wa.domain.zone
'com.ar'
wa.domain.full_name()
'fernet.com.ar'
wa.domain.registered
datetime.datetime(2020, 5, 7, 10, 44, 4, 210977)
wa.domain.expire
datetime.datetime(2021, 5, 7, 0, 0)

wa.registrant.name
'XXXX jose XXXXX'
wa.registrant.legal_uid
'20XXXXXXXX9'
wa.dnss[0].name
'ns2.sedoparking.com'
wa.dnss[1].name
'ns1.sedoparking.com'
```

## Get new domains

### Argentina

```python
from datetime import date
from whoare.zone_parsers.ar.news_from_blockchain import NewDomains
nd = NewDomains()
nd.data_path = ''  # here
results = nd.get_from_date(date(2020, 3, 28))

{
    'zonas': {
        'com.ar': [
            '3cconstrucciones.com.ar',
            '4kids.com.ar'
            ],
        'ar': [
            'andamios.ar',
            'apuesta.ar',
            'camaras.ar'
            ],
        'tur.ar': [
            'villacarlospaz.tur.ar'
            ]
        },
    'errors': {}
}

```
aviary.notebook 0.33.0
Jupyter notebook environment implemented with aviary
Requires Python: >=3.11

# aviary.notebook

A Jupyter notebook environment.

## Installation

To install the notebook environment, run the following command:

```bash
pip install 'fhaviary[notebook]'
```

To allow the environment to run notebooks in containerized sandboxes (recommended), first build the default image:

```bash
cd docker/
docker build -t aviary-notebook-env -f Dockerfile.pinned .
```

And second, set the environment variable `NB_ENVIRONMENT_USE_DOCKER=true`.
You may use your own Docker image with a different name,
in which case you must override the environment variable `NB_ENVIRONMENT_DOCKER_IMAGE`.
aviary.lfrqa 0.33.0
LFRQA environment implemented with aviary
Requires Python: >=3.11

# aviary.lfrqa

An environment designed to utilize PaperQA
for answering questions from the LFRQATaskDataset
Long-form RobustQA (LFRQA) is a human-annotated dataset introduced in the RAG-QA-Arena,
featuring over 1400 questions from various categories, including science.

## Installation

To install the LFRQA environment, run:

```bash
pip install 'fhaviary[lfrqa]'
```

## Usage

Refer to [this tutorial][2] for instructions on how to run the environment.

[2]: https://github.com/Future-House/paper-qa/blob/main/docs/tutorials/running_on_lfrqa.md

## References

[1] RAG-QA Arena (<https://arxiv.org/pdf/2407.13998>)
aviary.hotpotqa 0.33.0
HotPotQA environment implemented with aviary
Requires Python: >=3.11

# aviary.hotpotqa

The HotPotQA environment asks agents to perform multi-hop question answering on the HotPotQA dataset [1].

## References

[1] Yang et al. [HotpotQA: A Dataset for Diverse,
Explainable Multi-Hop Question Answering](https://aclanthology.org/D18-1259/). EMNLP, 2018.

## Installation

To install the HotPotQA environment, run the following command:

```bash
pip install 'fhaviary[hotpotqa]'
```
aviary.gsm8k 0.33.0
GSM8k environment implemented with aviary
Requires Python: >=3.11

# aviary.gsm8k

GSM8k environment where agents solve math word problems
from the GSM8k dataset using a calculator tool.

## Citation

The citation for GSM8k is given below:

```bibtex
@article{gsm8k-paper,
  title   = {Training verifiers to solve math word problems},
  author  = {
    Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and
    Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and
    Hilton, Jacob and Nakano, Reiichiro and others
  },
  year    = 2021,
  journal = {arXiv preprint arXiv:2110.14168}
}
```

## References

[1] Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano,
R. and Hesse, C., 2021.
[Training verifiers to solve math word problems](https://arxiv.org/abs/2110.14168). arXiv preprint arXiv:2110.14168.

## Installation

To install the GSM8k environment, run the following command:

```bash
pip install 'fhaviary[gsm8k]'
```
pyinfrincus 0.2.0
A pyinfra connector for incus.
Requires Python: <4,>=3.10

# pyinfrincus

A small package linking [pyinfra](https://pyinfra.com) and [incus](https://linuxcontainers.org/incus/)

### The `incus` connector

The most useful part of this package is the connector. It is both an `inventory` and `executing` connectorl. Once you have added this package to your pyproject or requirements.txt, you may do things like `pyinfra @incus/my-instance-name-1 fact server.LinuxName` (you may also omit the container name to run against ALL containers).

For now, the available options are just as follows.

| connector string | meaning            |
| ---------------- | ------------------ |
| `@incus`         | All instances      |
| `@incus/NAME`    | Run against `NAME` |