semvec/pypi-packages huggingface dataset:
formidable 0.14
Small but powerful library for rendering and processing web forms
Requires Python: <4,>=3.12

<div align="center">
  <h1><img alt="Formidable" src="https://raw.githubusercontent.com/jpsca/formidable/main/docs/formidable.png"></h1>
</div>
<p align="center">
  <img alt="coverage: 100%" src="https://raw.githubusercontent.com/jpsca/formidable/main/docs/coverage.svg">
  <img alt="license: MIT" src="https://raw.githubusercontent.com/jpsca/formidable/main/docs/license.svg">
</p>

### A small but powerful library for rendering and processing web forms


## Features

- Dynamic nested forms
- Subforms
- Translatable/customizable messages
- Custom filters and validators
- Integration with any ORM and template system


## Vision

By Internet standards, HTML forms are an ancient technology. At some point, we even forgot they existed, and for about 10 years, everyone was super excited about sending JSON over everything, all of the time.

But now we are realizing *"Oh, do you know what? HTML is actually really good!" It can be sent directly to a browser, and the browser can send form data back without any translation step. This native functionality is powerful and efficient.

However, in the world of Python web frameworks there is a problem: web forms had always been somewhat awkward. In fact, they were a big reason to use JavaScript in the first place. The existing libraries lack support for features like nested forms and/or are embedded in a particular framework.

That's why I built **Formidable**, a library that, although small, can handle all the advanced scenarios like subforms, dynamic nested forms, custom validators, translatable messages, integration with any ORM and template system, and more. I hope you enjoy it.


## Contributing Back

Find a bug? Head over to our issue tracker and we'll do our best to help. We love pull requests, too!
crustdata-screener-mcp 1.0.3
MCP server wrapper for CrustData Screener APIs
Requires Python: >=3.10

# CrustData Screener MCP Server

Connect Claude Desktop to CrustData's B2B data platform. Search companies, enrich data, and find decision makers directly from Claude.

## Installation

```bash
pip install crustdata-screener-mcp
```

## Setup

### 1. Get your CrustData API token
- Sign up at [crustdata.com](https://crustdata.com)
- Get your API token from your dashboard

### 2. Configure Claude Desktop

Add to your Claude Desktop config file:

**macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`  
**Windows**: `%APPDATA%\Claude\claude_desktop_config.json`

```json
{
  "mcpServers": {
    "crustdata": {
      "command": "crustdata-mcp",
      "env": {
        "CRUSTDATA_API_TOKEN": "your_api_token_here"
      }
    }
  }
}
```

### 3. Restart Claude Desktop

That's it! The CrustData tools are now available in Claude.

## For Claude CLI Users

If you're using Claude CLI instead of Claude Desktop:

```bash
claude mcp add crustdata "crustdata-mcp" \
  -e "CRUSTDATA_API_TOKEN=your_api_token_here"

# Verify connection
claude mcp list
```

## Usage Examples

Ask Claude:
- "Search for AI companies with 50-200 employees"
- "Get information about Stripe including funding and headcount"
- "Find the LinkedIn profile of John Doe at Apple"
- "Search for recent LinkedIn posts about product launches"

## Available Tools

- **Company Search & Enrichment**
  - `search_companies` - Search with filters
  - `enrich_company_data` - Get detailed company info
  - `identify_company` - Find companies by name/domain

- **People Search & Enrichment**  
  - `search_people` - Find people by title, company, location
  - `enrich_person_profile` - Get detailed person info

- **LinkedIn Data**
  - `get_linkedin_posts` - Retrieve LinkedIn posts
  - `search_linkedin_posts_by_keyword` - Search posts by keywords

## Environment Variables

- `CRUSTDATA_API_TOKEN` (required) - Your CrustData API token
- `CRUSTDATA_API_BASE_URL` (optional) - API base URL (defaults to production)

## Support

- Documentation: [docs.crustdata.com](https://docs.crustdata.com)
- Issues: [github.com/crustdata/mcp-server/issues](https://github.com/crustdata/mcp-server/issues)
- Email: support@crustdata.com

## License

MIT
airavata-python-sdk 2.2.7
Apache Airavata Python SDK
Requires Python: >=3.10

# Apache Airavata Python SDK

The Apache Airavata Python SDK for third party clients to integrate with Airavata middleware

### Folder Structure
- `airavata`
  Includes thrift stubs generated by a thrift compiler.
  Mainly contains data model and functions.
  You may need to import data model packages to integrate with clients.
- `airavata_sdk`
  - `clients`
    Includes integration clients, which you want to import and integrate with your code to access airavata middleware.
  - `samples`
    Includes set of sample implementation of integration clients to demonastrate the integration with airavata middleware
  - `transport`
    Includes connection handling classes and connection pool implementations
- `airavata_experiments`
  Python APIs to run experiments from anywhere.
  Handles uploading data, running experiments, tracking progress, and fetching data from past runs.
- `airavata_jupyter_magic`
  Jupyter Plugin providing magic annotations (%) to shift notebook runtimes between resources (local/remote).

### Before Integration

- Create a virtual environment
  ```bash
  python3 -m venv venv
  ```
- Activate the virtual environment
  ```bash
  source venv/bin/activate
  ```
- Install dependencies
  ```bash
  pip install -e .
  ```
- Create a INI file containing server configuration details. For more information refer to default settings file
  [settings.ini](airavata_sdk/transport/settings.ini)

### Generating Distribution Archives (Optional)

You can generate a `*.tar.gz` distribution and install to any external project.
- Make sure you have the latest versions of `build` and `setuptools` installed
  ```bash
  python3 -m pip install --upgrade build setuptools
  ```
- Now run this command from the same directory where `pyproject.toml` is located
  ```bash
  python3 -m build .
  ```
- This command should output a lot of text and once completed should generate two files in the dist directory
  ```
  dist/
      airavata_python_sdk-2.0.0-py2.py3-none-any.whl
      airavata-python-sdk-2.0.0.tar.gz
  ```

Now, you should be able to install those packages into your project.