semvec/pypi-packages huggingface dataset:
rpa-runner 0.1.5
A library and runner for executing Robot Framework tasks in parallel using JSON configuration.
Requires Python: >=3.6

# RPA Runner

This package provides a mechanism to execute Robot Framework tasks defined in JSON format with support for `PARALLEL` blocks, and a `Run Parallel` keyword for parallel execution.

## Installation

```bash
pip install .
```

## Usage

### Command Line Runner

The package provides a command-line tool `rpa-runner` (or `python -m rpa_runner.runner`) to execute JSON test definitions.

```bash
rpa-runner test_parallel.json --outputdir results
```

### Built-in Listeners

The library includes a `ProbeListener` for real-time reporting via Socket.IO.

```bash
rpa-runner test_parallel.json --listener rpa_runner.listeners.ProbeListener
```

### JSON Format & Parallel Execution

The runner automatically transforms `PARALLEL` blocks in your JSON test definition into calls to the `Run Parallel` keyword, which uses `pabot` under the hood.

```json
{
  "name": "My Suite",
  "tests": [
    {
      "name": "Test 1",
      "body": [
        {
          "type": "PARALLEL",
          "branches": [
            {
              "name": "Concurrent A",
              "body": [{ "name": "Log", "args": ["Running A"] }]
            }
          ]
        }
      ]
    }
  ]
}
```

### Library Usage

You can also use the library directly in Robot Framework:

```robot
*** Settings ***
Library    rpa_runner
```
fluff-cutter 0.4.1
A CLI tool to analyze academic papers and extract their core value
Requires Python: >=3.10

# Paper Fluff Cutter

A CLI tool that cuts through academic paper fluff to extract what actually matters.

Most research has close to zero value. This tool uses multimodal LLMs to analyze papers and answer three questions:

1. **Why should I care?** - What problem does this address and why does it matter?
2. **What's the actual innovation?** - What's the core idea in plain terms?
3. **Is the evidence convincing?** - Do the experiments actually support the claims?

## Quick Start

```bash
pip install fluff-cutter
fluff-cutter init          # set up API keys and defaults
fluff-cutter analyze paper.pdf
```

Requires Python 3.10+.

## Usage

```bash
# Local file
fluff-cutter analyze paper.pdf

# URL (arxiv /abs/ links are auto-converted to PDF)
fluff-cutter analyze https://arxiv.org/pdf/2411.19870

# Options
fluff-cutter analyze paper.pdf --output analysis.md   # custom output path
fluff-cutter analyze paper.pdf --print                 # stdout only, no file
fluff-cutter analyze paper.pdf --provider openai       # openai, anthropic, openrouter
fluff-cutter analyze paper.pdf --model gpt-5.2         # override default model
fluff-cutter analyze paper.pdf --max-pages 30          # limit pages for long papers
```

By default, results are printed to the terminal and saved as a `.md` file next to the input PDF.

## Supported Providers

| Provider | Default Model | Env Variable |
|----------|---------------|--------------|
| OpenAI | gpt-5.2 | `OPENAI_API_KEY` |
| Anthropic | claude-sonnet-4-5 | `ANTHROPIC_API_KEY` |
| OpenRouter | anthropic/claude-sonnet-4-5 | `OPENROUTER_API_KEY` |

All providers support native PDF input -- no external dependencies like poppler needed.

## Configuration

Run `fluff-cutter init` for interactive setup, or set environment variables directly:

```bash
export OPENAI_API_KEY=sk-your-key-here
export FLUFF_CUTTER_PROVIDER=anthropic          # default provider
export FLUFF_CUTTER_ANTHROPIC_MODEL=claude-sonnet-4-5  # override model
```

Config is read in this order (highest priority first): CLI flags, env variables, `~/.fluff-cutter/config.yaml`, provider defaults.

## License

MIT