semvec/pypi-packages huggingface dataset:
pytest-filterfixtures 0.1.0
pytest plugin to execute or ignore tests based on fixtures
Requires Python: >=3.10

# pytest-filterfixtures

`pytest-filterfixtures` is a `pytest` plugin to filter tests based on their fixtures.

This is particularly useful in large test codebases when fixtures require changes/refactoring, or a specific fixture stops wokring due to external reasons. You can work on modifying the fixture(s) and have better control over which tests should run.

## Installation
Install with pip
```
pip install pytest-filterfixtures
```

## Usage
`pytest-filterfixtures` adds the following command line options:
- `--include-fixtures` to execute all tests that use at least one of the specified fixtures as part of their setup/teardown
- `--exclude-fixtures` to deselect all tests that use at least one of the specified fixtures as part of their setup/teardown

### Example
Given the following test file
```py
@pytest.fixture()
def fixt1():
    return 1

@pytest.fixture()
def fixt2():
    return 2

@pytest.fixture()
def fixt3(fixt1):
    return fixt1 + 1

def test1(fixt1):
    ...

def test2(fixt2):
    ...

def test3(fixt3):
    ...
```

- `pytest --include-fixtures fixt1 fixt2` executes `test1, test2, test3`
- `pytest --include-fixtures fixt3` executes `test3`
- `pytest --exclude-fixtures fixt1` executes `test2`
- `pytest --exclude-fixtures fixt3 --include-fixtures fixt1` executes `test1`
pyrems 0.1.1
Add your description here
Requires Python: >=3.12

# pyrems

A simple Python CLI tool for remembering and managing your bash commands.

## What it does

`pyrems` (Python Remember) helps you save, organize, and quickly access important commands you don't want to lose. Instead of digging through your bash history or losing commands when switching computers, `pyrems` stores your commands with optional notes and tracks how often you use them.

## Key Features

- **Save commands** with notes for future reference
- **Smart storage** - automatically increments hit counter for repeated commands
- **Interactive search** - browse and fuzzy-search your saved commands
- **Persistent across machines** - sync your commands via dotfiles
- **Usage tracking** - see which commands you use most often

## Quick Start

```bash
# Install pyrems
pip install pyrems

# Initialize the tool
pyrems install

# Save a command (add to your .bashrc)
tar -xzvf archive.tar.gz
rem "Extract gzipped tar archive"

# Browse and search saved commands
pyrems

# List all saved commands
pyrems list
```

## Commands

- `pyrems` - Interactive command browser with fuzzy search
- `pyrems store` - Store a command (usually called by the `rem` bash function)
- `pyrems list` - Display all saved commands in a table
- `pyrems install` - Set up configuration and files
whale-mcp-feedback 1.0.0
MCP server for interactive user feedback with GUI - Python wrapper for Rust binary
Requires Python: >=3.10
Keywords: mcp, ai, feedback, interactive, cursor, claude, kiro

# Whale Interactive Feedback

MCP server for interactive user feedback with GUI.

This is a Python wrapper that automatically downloads and runs the Rust binary.

## Installation

```bash
# Using uvx (recommended)
uvx whale-interactive-feedback

# Or install globally
pip install whale-interactive-feedback

# If you use SOCKS proxy, install with socks support
pip install "whale-interactive-feedback[socks]"
```

## MCP Configuration

Add to your MCP client configuration (Kiro, Cursor, Claude Desktop, Windsurf):

```json
{
  "mcpServers": {
    "whale-interactive-feedback": {
      "command": "uvx",
      "args": ["whale-interactive-feedback"]
    }
  }
}
```

## Commands

```bash
# Run the MCP server
whale-ask-server

# Check version
whale-ask-server --version

# Update to latest version
whale-ask-server --update
```

## Features

- 🐋 Interactive feedback collection via GUI popup
- 📎 File drag-and-drop support
- 🖼️ Image attachment support
- ⚡ Fast Rust binary with Python wrapper for easy installation
- 🔄 Auto-download and update

## License

MIT
raesl 0.16.2
Ratio ESL support in Python.
Requires Python: >=3.11
Keywords: engineering, language, management, requirements, specification, systems

# Ratio ESL

Ratio support for the Elephant Specification Language (ESL) in Python.

## Quickstart

### Installation

RaESL can be installed using `pip install raesl[all]` for any Python version >=3.11.
For managed projects you can use:

- uv: `uv add raesl[all]`
- Poetry: `poetry add raesl -E all`

For RaESL's different subcommands and functionality, the wheel provides extras which you could
provide instead of the `all` used above:

- `doc`: documentation generation using pandoc, Markdown and optionally LaTeX.
- `jupyter`: a Jupyter ESL kernel.
- `pygments`: an ESL syntax highlighter for pygments.
- `rich`: Rich doc output in the form of Plotly images.
- `server`: A language server to parse documents.

The default `raesl compile` command is always available.

Please refer to the [usage documentation](https://raesl.ratio-case.nl) for more info on how to use
RaESL.

## Development installation

This project is packaged using [uv](https://docs.astral.sh/uv/) as the environment manager and build
frontend. Packaging information as well as dependencies are stored in
[pyproject.toml](./pyproject.toml).

For ease of use, this project uses the [just](https://github.com/casey/just) command runner to
simplify common tasks. Installing the project and its development dependencies can be done by
running `just install` in the cloned repository directory or manually by running `uv sync --all-extras`.

Please consult the [justfile](./justfile) for the underlying commands or run `just` to display a
list of all available commands.

### Tests

Tests can be run using `just test` and subsequent arguments will be passed to pytest.

### Linting

Linting the project can be done using `just lint`, automatic fixes can be applied using `just fix`.
Linting config is included in [pyproject.toml](./pyproject.toml) for Ruff.

### Documentation

Documentation can be built using `just docs` or served continuously using `just serve-docs` with
the help of [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/).

## Contributions and license

To get contributing, feel free to fork, pick up an issue or file your own and get going for your
first merge! We'll be more than happy to help.

For contribution instructions, head over to [CONTRIBUTING.md](./CONTRIBUTING.md).

RaPlan is licensed following a dual licensing model. In short, we want to provide anyone that
wishes to use our published software under the GNU GPLv3 to do so freely and without any further
limitation. The GNU GPLv3 is a strong copyleft license that promotes the distribution of free,
open-source software. In that spirit, it requires dependent pieces of software to follow the same
route. This might be too restrictive for some. To accommodate users with specific requirements
regarding licenses, we offer a proprietary license. The terms can be discussed by reaching out to
Ratio.
whale-interactive-feedback 1.0.3
MCP server for interactive user feedback with GUI - Python wrapper for Rust binary
Requires Python: >=3.10
Keywords: mcp, ai, feedback, interactive, cursor, claude, kiro

# Whale Interactive Feedback

MCP server for interactive user feedback with GUI.

This is a Python wrapper that automatically downloads and runs the Rust binary.

## Installation

```bash
# Using uvx (recommended)
uvx whale-interactive-feedback

# Or install globally
pip install whale-interactive-feedback

# If you use SOCKS proxy, install with socks support
pip install "whale-interactive-feedback[socks]"
```

## MCP Configuration

Add to your MCP client configuration (Kiro, Cursor, Claude Desktop, Windsurf):

```json
{
  "mcpServers": {
    "whale-interactive-feedback": {
      "command": "uvx",
      "args": ["whale-interactive-feedback"]
    }
  }
}
```

## Commands

```bash
# Run the MCP server
whale-ask-server

# Check version
whale-ask-server --version

# Update to latest version
whale-ask-server --update
```

## Features

- 🐋 Interactive feedback collection via GUI popup
- 📎 File drag-and-drop support
- 🖼️ Image attachment support
- ⚡ Fast Rust binary with Python wrapper for easy installation
- 🔄 Auto-download and update

## License

MIT
prompt-spec 0.1.4
Lightweight pydantic wrapper prompting library to support sharing of oncology-related LLM prompts
Requires Python: >=3.11

# `prompt-spec`
*A lightweight, LinkML-aligned prompt and schema management toolkit for clinical NLP & LLM workflows.*

`prompt-spec` provides:

- **Structured prompt templates**  
- **LinkML → Pydantic** auto-generation for LLM output schemas  
- **Validation of few-shot examples** within prompts
- A **shared format** for prompt libraries across collaborating groups

This toolkit is intentionally minimal and designed for stability, reuse, and strong typing when integrating with LLM libraries such as **[pydantic-instructor](https://python.useinstructor.com/)**

---

# Project Structure

```text
prompt-spec/
├── cli.py                        ← Main command line interface
├── core/
│   └── prompt_template.py        ← Prompt class utilities
├── engines/
│   ├── __init__.py
│   └── instructor_engine.py      ← Wrapper function for integrating prompts with ontoGPT via Instructor interface
├── output_models/
│   └── condition_model.yaml      ← LinkML schema for defining model outputs - create new endpoint definitions here
├── generated_models/
│   └── (auto-generated pydantic models)
└── prompts/
    └── condition_prompt.yaml     ← This is where the actual prompt definitions exist
```

---

# Installation

`uv pip install -e .`

---

# Usage

## 1. Defining LinkML output models

Place all LinkML schemas inside `prompt-spec/output_models`

Example: *condition_model.yaml*

```yaml
name: ConditionList
id: https://example.org/condition_model
prefixes:
  linkml: https://w3id.org/linkml/
default_range: string

classes:
  Condition:
    attributes:
      label: string
      verbatim_name: string
      codable_name: string
      who_diagnosed: string
      is_negated: boolean

  ConditionList:
    attributes:
      conditions:
        multivalued: true
        range: Condition
```

## 2. Generate Pydantic models from LinkML

`prompt-spec build-models`

This reads all `*.yaml` schemas in `prompt-spec/output_models/` and generates Pydantic classes into `prompt-spec/generated_models/`.

After running this, you will have files such as:

* `prompt-spec/generated_models/ConditionList.py`

Alternatively, run for just one class update at a time: `prompt-spec generate-pydantic-from-linkml output_models/condition_model.yaml`

## 3. Populate prompt template

`prompt-spec create-empty-prompt condition_model prompts/condition_prompt.yaml`

This produces:

```yaml
output_model: condition_model
instruction: "<<< fill in your system prompt here >>>"
examples:
  example_1:
    input: "<<< sample input text here >>>"
    output:
      conditions: []
```

## 4. Validate a prompt against its template

`prompt-spec validate-prompt condition_prompt.yaml`