semvec/pypi-packages huggingface dataset:
charmos 0.4.21
The Unified System Layer for Agentic Intelligence
Requires Python: >=3.10

<p align="center">
  <img src="assets/charm-banner.png" alt="Charm Banner" width="100%" />
</p>

<p align="center">
  <a href="https://charmos.io/">Homepage</a> .
  <a href="https://store.charmos.io/">Store</a> .
  <a href="https://discord.gg/gdakynHUEb">Discord</a>
</p>

# Charm: The Unified Platform for Agentic Intelligence

**Charm** is a **Unified Application Layer** that provides a standardized architecture that enables agents to distribute, run, and scale across heterogeneous ecosystems, turning them into real, commercial-ready applications.

## Getting started

### Installation

Charm is available on PyPI. We recommend using [uv](https://github.com/astral-sh/uv) for the best experience.

#### Standard Setup (For Agent Development)

If you only want to build and define agents

```bash
uv add charmos
# or
pip install charmos
```

#### Advanced Setup (For Local Cloud Runner)

If you want to simulate the isolated cloud environment locally

```bash
uv add charmos[runner]
# or
pip install charmos[runner]
```

> **Note:** To use the docker simulation mode, you must have Docker Desktop or Docker Engine running on your machine.
>
### Register your agent

Read our simple [tutorial](https://github.com/CharmAIOS/Charm/blob/main/docs/getting-started/charm-store.md) to learn how to register your agent on the Charm Store.

## Contributing

To know how to contribute to Charm, please read our [contribution guide](https://github.com/CharmAIOS/Charm/blob/main/CONTRIBUTING.md).

## Contact

- Support and Questions: [Community](https://discord.gg/gdakynHUEb) / [Email](mailto:team@charmos.io)
- Join us: [Chat with us](mailto:uc@charmos.io)

## Changelog

Check our [updates](https://charmos.io/changelog)

## License

Charm is licensed under the [GNU Affero General Public License v3.0](https://github.com/CharmAIOS/CharmOS/blob/main/LICENSE)
iflow-mcp_simovilab-transit 0.1.0
Model Context Protocol server enabling AI assistants to access transit information
Requires Python: >=3.12

# Infobús MCP (servidor)

Interfaz de inteligencia artificial en sistemas de información para personas usuarias del transporte público

Fabián Abarca Calderón

Nota: manejador de paquetes de Python uv.

```bash
uv init transit
cd transit
```

```bash
uv venv
source .venv/bin/activate
```

```bash
uv add "mcp[cli]" httpx
```

```bash
touch transit.py
```

```Python

@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    points_url = f"{INFOBUS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_infobus_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for this location."

    # Get the forecast URL from the points response
    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_infobus_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]:  # Only show next 5 periods
        forecast = f"""
{period['name']}:
Temperature: {period['temperature']}°{period['temperatureUnit']}
Wind: {period['windSpeed']} {period['windDirection']}
Forecast: {period['detailedForecast']}
"""
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)
```
icotest 0.8.0
Hardware tests for ICOtronic system
Requires Python: <4,>=3.12
Keywords: smart-tool, smh, stu, sth, tool-holder

[documentation]: https://icotest.readthedocs.io

[![API Documentation](https://img.shields.io/readthedocs/icotest?label=Documentation)][documentation]

# ICOtest

Please take a look at the [online documentation][documentation].
ashlar-saas 0.1.9
A quick way to set up a Django SaaS application with a custom User model
Requires Python: <4,>=3.12

# Ashlar-SaaS

A SaaS structure for Django

# Usage
Add the user model to your settings:
```python
AUTH_USER_MODEL = 'account.User'
```

Add this to your MIDDLEWARE, after `django.contrib.auth.middleware.AuthenticationMiddleware`:
```python
[
    'account.middleware.TenantMiddleware',
]
```
smart-code-assistant 0.1.2
A CLI tool to explain, debug, test and optimize code using LLM
Requires Python: >=3.8

# Smart Code Assistant CLI

## Short Description
Smart Code Assistant CLI helps beginners debug, optimize, explain, and generate tests for Python code instantly, powered by an offline **Ollama 3.21B** model. It keeps developers focused by providing AI-powered solutions directly in the project directory.

## Features
- **Code Debugger** – Automatically detects issues in your code and generates a text file with suggestions.
- **Code Optimizer** – Analyzes your code and suggests more efficient alternatives in a separate file.
- **Code Explainer** – Provides clear explanations of your code logic up to a specific point.
- **Test Generator** – Generates sample test cases to validate correctness and handle edge cases.
- **Offline Support** – Powered by Ollama 3.21B, works entirely offline without internet dependency.
- **Fast Execution** – Commands execute in 2–3 seconds (network optional) for instant feedback.

## How It Works
1. Execute a custom command (e.g., `debug`) in your terminal.
2. Your code is sent to the **Ollama LLM** for processing.
3. The generated output (debugged code, optimized version, explanation, or tests) is saved as a text file in your project folder.
4. No need to switch tools—stay focused and reduce attention residue.

## Supported Commands
| Command        | Description |
|----------------|-------------|
| `debug`        | Debugs your code and writes suggestions to a file. |
| `optimize`     | Suggests improvements for efficiency. |
| `explain`      | Explains your code logic clearly. |
| `generate-test`| Creates sample test cases for validation. |

## Tech Stack
- Python
- Ollama 3.21B LLM (offline)
- Windows PowerShell (custom CLI commands)
django-seeker 8.0.1
A python package for mapping and querying Django models in OpenSearch.

**Please Note: ElasticSearch will no longer be supported in Seeker 8+.  Seeker will only support OpenSearch.**

Requirements
============

  * Django 4.x or later


Documentation
=============

To build the documentation, go into the docs directory and type:

    make html

The documentation will be built with an index of docs/_build/index.html
piscal-processor 0.1.0
PISCAL CSV/Parquet processing: convert, export, and schema utilities for curves and measurements.
Requires Python: >=3.10

# piscal-processor

PISCAL CSV/Parquet processing: convert curves to Parquet, export to CSV/TSV, and use standard schemas. PISCAL is used at [LeafWeb.org](https://leafweb.org).

Install from PyPI, a local clone, or a Git URL (see below).

## Install

**From PyPI** (when published):

```bash
pip install piscal-processor
```

**Optional S3 support** (e.g. for `s3a://` paths):

```bash
pip install piscal-processor[s3]
```

**From a local clone** (development):

```bash
pip install -e /path/to/piscal-processor
```

**From a Git URL** (CI or private install): use a personal access token or SSH:

```bash
pip install "piscal-processor @ git+https://github.com/kolpacksoftware/piscal-processor.git@main"
# or
pip install "piscal-processor @ git+ssh://git@github.com/kolpacksoftware/piscal-processor.git@main"
```

You can pin a branch (`@main`), tag (`@v0.1.0`), or commit (`@abc1234`).

## CLI

**Convert** PISCAL CSV files to Parquet (metadata + measurements):

```bash
piscal-processor /path/to/csv_dir --output-dir parquet_output
```

Options: `--no-discover-pathway-subdirs`, `--source-pathway`, `--metadata-name`, `--measurements-name`. Input can be a local path or `s3a://` URI.

**Export** measurement Parquet to CSV or TSV:

```bash
piscal-processor-export curve_measurements.parquet -o out.tsv --format tsv --columns curve_id,AnetCO2,PARi
```

## Library

```python
from piscal_processor import convert_curves, export_curves, get_backend

# Convert CSVs to DataFrames (or write Parquet via converter.normalize_and_write_parquet)
backend = get_backend("/path/to/csv_dir")
meta_df, meas_df = convert_curves("/path/to/csv_dir", backend, source_pathway="C3")

# Export measurements to CSV/TSV
export_curves(meas_df, "out.tsv", columns=["curve_id", "AnetCO2", "PARi"], format="tsv")
```

Schema constants and parser helpers are also available:

```python
from piscal_processor import STANDARD_MEASUREMENT_COLUMNS, STANDARD_METADATA_COLUMNS
from piscal_processor.parser import parse_csv_line, parse_key_value_section
```

## Tests

```bash
pip install -e ".[dev]"
pytest
```

## Documentation

- `docs/csv_structure.md`: CSV file structure (header, site/parameter blocks, measurement table).
- `docs/inputformat.txt`: PISCAL input file specification (official format description).