semvec/pypi-packages huggingface dataset:
calimero-client-py-bindings 0.2.2
Python bindings for Calimero client library
Requires Python: >=3.9

# Calimero Client Python Bindings

This directory contains the Rust-based Python bindings for the Calimero client library.

## 🏗️ Structure

```
bindings/
├── Cargo.toml              # Rust package configuration
├── src/
│   ├── lib.rs             # Entry point
│   ├── python.rs          # Python bindings implementation
│   ├── mod.rs             # Module definitions
│   └── README.md          # This file
├── calimero_client_py_bindings/
│   └── __init__.py        # Python package initialization
├── build.sh                # Build script for bindings only
└── tests/                  # Rust tests
```

## 🔧 Building

### From the Repository Root (Recommended)

```bash
# From calimero-client-py root directory
./build.sh

# Or with options
./build.sh --install  # Build and install in development mode
./build.sh --test     # Build and run tests
```

### From the Bindings Directory Only

```bash
cd bindings
./build.sh
```

## 📦 Dependencies

The bindings depend on these crates (to be published to crates.io):
- `calimero-client` - Core client functionality
- `calimero-primitives` - Core data types

## 🚀 Integration

These bindings are automatically built and integrated with the main Python client when you run the root build script. The `pyproject.toml` in the repository root is configured to use maturin to build these bindings.

## 🧪 Testing

```bash
# Test Rust code
cd bindings
cargo test

# Test Python integration
cd ..
./build.sh --test
```

## 📚 Documentation

- [Main README](../README.md) - Overall project documentation
- [PUBLISHING.md](PUBLISHING.md) - How to publish the bindings
- [STANDALONE_BUILD.md](STANDALONE_BUILD.md) - Standalone build instructions
trusted-publishing-examples 0.1.3
Add your description here
Requires Python: >=3.13

# uv trusted publishing examples

Trusted publishing allows uploading package from GitHub Action to PyPI without
manually setting a secret token. Instead, you specify on PyPI a GitHub Actions
workflow that is allowed to publish the package.

This repository contains a full, self-contained example for trusted publishing
with uv. The release workflow can be found in
[.github/workflows/release.yml](.github/workflows/release.yml). On PyPI, the
matching configuration is set under
`https://pypi.org/manage/project/<package-name>/settings/publishing/`:

You can find the published package at
https://pypi.org/project/trusted-publishing-examples/.

[.github/workflows/ci.yml](.github/workflows/ci.yml) is a minimal test and lint
workflow for a Python package, while
[.github/workflows/errors.yml](.github/workflows/errors.yml) is for testing uv
itself only.

## Documentation

- uv's side: https://docs.astral.sh/uv/guides/publish/
- PyPI's side: https://docs.pypi.org/trusted-publishers/
ki-aws-strands-agentcore-mcp-server 0.1.4
MCP server for Strands Agent to Bedrock AgentCore workflow automation
Requires Python: >=3.10

# Ki AWS Strands AgentCore MCP Server

MCP server for automating Strands Agent development and Bedrock AgentCore deployment workflow.

## Installation

```bash
uvx ki-aws-strands-agentcore-mcp-server@latest
```

## Usage

Add to your Q CLI Agent configuration:

```json
{
  "mcpServers": {
    "ki.aws.strands-agentcore-mcp-server": {
      "command": "uvx",
      "args": ["ki-aws-strands-agentcore-mcp-server@latest"]
    }
  }
}
```

## Tools

- `develop_strands_agent`: Phase 1 - Strands Agent development and local testing
- `deploy_to_agentcore`: Phase 2 - Bedrock AgentCore deployment

## Features

- Automatic virtual environment setup
- Package installation on startup
- Phase 1-2 workflow automation
- Compatible with Q CLI Agent
cooper-epic-jungle 2.0.1
An extension of the mipi package. Contains a repository of sql scripts and functions to build modular reports using them
Requires Python: <4.0,>=3.12

# CooperEpicJungle

This is the Cooper internal Epic Jungle. It  is an extension of the MiPi package.
It contains pre-configured SQL templates

## Access the scripts using mipi

```python
# Import the data manager
from mipi_datamanager import DataManager

# Import the Epic Jungle
from cooper_epic_jungle import COOPER_EPIC_JUNGLE

# use the default jungle with all of its presets (COOPER_EPIC_JUNGLE)
mipi = DataManager.from_jinja_repo_preset("epic/EPT/encounters/ambulatory/population/generic.sql")

# override the jungle for the entire datamanager object
from cooper_epic_jungle import EPT
mipi = DataManager.from_jinja_repo_preset(override_jinja_repo_source=EPT)


```