semvec/pypi-packages huggingface dataset:
pyats-genie-command-parse 1.4.6
Run genie parsers directly.
Requires Python: >=3.10
Keywords: pyATS, genie, cisco, ios, ios-xr, nxos, parse, wrapper

| BRANCH | STATUS |
|---|---|
| master | [![Unit-Testing, Coverage, Linting](https://github.com/btr1975/pyats-genie-command-parse/actions/workflows/test-coverage-lint.yml/badge.svg?branch=master)](https://github.com/btr1975/pyats-genie-command-parse/actions/workflows/test-coverage-lint.yml) |
| develop | [![Unit-Testing, Coverage, Linting](https://github.com/btr1975/pyats-genie-command-parse/actions/workflows/test-coverage-lint.yml/badge.svg?branch=develop)](https://github.com/btr1975/pyats-genie-command-parse/actions/workflows/test-coverage-lint.yml) |

[![Downloads](https://pepy.tech/badge/pyats-genie-command-parse)](https://pepy.tech/project/pyats-genie-command-parse)
[![Supported Versions](https://img.shields.io/pypi/pyversions/pyats-genie-command-parse.svg)](https://pypi.org/project/pyats-genie-command-parse)


# pyats-genie-command-parse
This is a library to be able to parse NOS command output using the available
[Genie parsers](https://developer.cisco.com/docs/genie-docs/), it seperates out 
needing to create a testbed.yml, and allows you to just get the output, and parse the 
output with [Genie parsers](https://developer.cisco.com/docs/genie-docs/). You can find the 
[Genie Parse Source here](https://github.com/CiscoTestAutomation/genieparser/tree/master/src/genie/libs/parser)

# Line 55 in pyats_genie_command_parse.py
```python
 supported_nos = {'aireos',
                  'apic',
                  'asa',
                  'bigip',
                  'cheetah',
                  'comware',
                  'dnac',
                  'gaia',
                  'ios',
                  'iosxe',
                  'iosxr',
                  'ironware',
                  'junos',
                  'linux',
                  'nxos',
                  'sros',
                  'viptela'}
```

# Some options to get the data
1. You could use [netmiko](https://ktbyers.github.io/netmiko/) to get the command output
   from devices and then parse using the string parser.

2. You could use [Cisco Network Services Orchestrator "NSO"](https://developer.cisco.com/docs/nso/)
   to get the command output from devices and then parse using the string parser.

3. Use your imagination, as long as you can get the output data in a string format the string parser
   will work.

4. If you prefer you could store the data in a text file, and then using the file parser, you can also
   parse the output data.
labdata 0.0.22
Package to manage data in experimental neuroscience labs
Requires Python: >=3.0

## labdata

Tools to copy data and manage data analysis in an experimental neuroscience lab.

This is built after [labdata-tools](github.com/jcouto/labdata-tools) and integrates with S3 and a database through datajoint.

### Concepts
Experiments often involve many computers in the lab and data has to be copied to a server(and sometimes backed up). In this process files can be corrupted, data loss and so on, specially if manually copying files. Further, in some cases, tasks have to be performed on the files (those might involve compressing, formatting, etc).

What ``labdata`` does to copy files:
     1. After data are acquired, labdata performs a md5 checksum of the file to be copied and copies the file to a server.
     2. After the copy, the checksum and datapath are placed on the ``Upload`` database table.
     3. The computer/server that manages the copy to the permanent storage server will read the database table. The server will perform a checksum on the file to ensure the copy from the acquisition computer was ok.
     4. If needed it will compress data or do whatever is specified in the preference file rules (see ``upload_rules``)
     5. Data will be put in S3 and added to the ``Files``
     

### Instalation

Clone the repository to a folder in your computer and do:

``pip install .`` or ``pip install -e .`` to if you want that the source code follows git.
er_helm 0.0.1
A stub for the EntityRisk PROVEN Platform
Requires Python: <4.0,>=3.9

This is a public stub of a proprietary EntityRisk package uploaded to protect
users of the EntityRisk PROVEN Platform from
[dependency confusion](https://medium.com/@alex.birsan/dependency-confusion-4a5d60fec610).

This package is non-functional and users accidentally installing it will
receive an error message when trying to import it.
iflow-mcp_farzad-r-mcp-lab 0.1.0
MCP Crash Course - A collection of MCP server examples including news reader, stock news agent, and Wikipedia summary agent
Requires Python: >=3.10

# 🧪 MCP Lab – Experiments with Anthropic's Model Context Protocol

Welcome to **MCP Lab**, a collection of projects, demos, and experiments using Model Context Protocol (MCP). This repository serves as a playground to explore the power and versatility of MCP in connecting AI models to external tools, data sources, and services.

> Think of MCP as **USB-C for AI** – a standardized way to plug large language models (LLMs) into your tools with minimal custom code.

---

## 📂 Structure

Each folder in this repo represents an independent, self-contained project or tutorial built using MCP. Projects vary from beginner-friendly crash courses to deeper integrations with real-world APIs and local infrastructure.

```bash
mcp-lab/
│
├── mcp-crash-course/         # Introductory project – walk-through of MCP basics
├── Coming soon...
```

Each project includes:
- Clear setup instructions
- Source code for MCP servers and/or clients
- Examples or video walkthroughs
- Optional Claude or local LLM integration

---

## 🧠 Why MCP?

Model Context Protocol (MCP) enables:
- 🔌 **Plug-and-play integrations** between LLMs and external tools
- 🔒 **Privacy-aware, local-first design** (no need to expose your data to the cloud)
- 🧱 **Composable, reusable servers** for Slack, GitHub, databases, and more
- 🌐 **LLM-agnostic architecture** (works with Claude, Ollama, ChatGPT, etc.)

With MCP, you write a connector **once**, and any compatible model can use it. It’s the future of LLM-to-tool communication.

---

## 🛠 Getting Started

To run any project:

1. Clone the repo:
   ```bash
   git clone https://github.com/Farzad-R/mcp-lab.git
   cd mcp-lab
   ```

2. Navigate to the project folder you're interested in (e.g. `mcp-crash-course`)
3. Follow the `README.md` inside that folder for setup and instructions

---

## 🎥 Related Video Series

All of these projects are featured in my educational videos on [YouTube](https://www.youtube.com/@airoundtable).

➡️ Watch the full crash course: **[MCP Crash Course – YouTube Link]**

---

## 🤝 Contributing

Want to add your own MCP experiment?  
PRs are welcome! Follow the format of existing folders and include:
- A clear `README.md`
- A self-contained demo (scripts, server, or configs)
- Optional: video or blog post link

---

MCP-Crash-Course YouTube Video: [URL](https://youtu.be/StgbwIQH-C4)

---