semvec/pypi-packages huggingface dataset:
seashail-mcp 0.1.0
Run the Seashail MCP server in one line via uvx.
Requires Python: >=3.10
Keywords: crypto, mcp, trading, wallet

# `seashail-mcp` (uvx runner)

Run the Seashail MCP server in one line via `uvx` (Python). This is a thin wrapper that ensures `seashail` is installed and runs `seashail mcp` for you.

> See the [main README](../README.md) for full project documentation, or the [Install docs](https://seashail.com/docs/getting-started/install) for all installation methods.

## Usage

```bash
uvx seashail-mcp
```

Pass through args to `seashail mcp`:

```bash
uvx seashail-mcp -- --network testnet
```

## Use in Agent MCP Config

If you don't want to install the `seashail` binary globally, reference this package in your agent's MCP config instead:

```json
{
  "mcpServers": {
    "seashail": {
      "command": "uvx",
      "args": ["seashail-mcp"]
    }
  }
}
```

Testnet:

```json
{
  "mcpServers": {
    "seashail-testnet": {
      "command": "uvx",
      "args": ["seashail-mcp", "--", "--network", "testnet"]
    }
  }
}
```

## How It Works

- If `seashail` is already installed, the wrapper runs it directly
- If `seashail` is not installed, the wrapper executes the hosted installer:
  - macOS/Linux: `curl -fsSL https://seashail.com/install | sh`
  - Windows (PowerShell): `irm https://seashail.com/install.ps1 | iex`
- After installation, it runs `seashail mcp` with any provided args

## Alternatives

- **Direct install (recommended):** `brew install seashail/seashail/seashail` or `curl -fsSL https://seashail.com/install | sh` — see [Install docs](https://seashail.com/docs/getting-started/install)
- **Node.js wrapper:** `npx -y @seashail/mcp` — see [`packages/mcp/`](../packages/mcp/)
- **One-click agent templates:** `seashail agent install <target>` — see [Agent Integration](../README.md#agent-integration)

## Related

- [Main README](../README.md) — Full project documentation
- [`packages/mcp/`](../packages/mcp/) — `@seashail/mcp` npx wrapper
- [Agent Config Templates](../packages/agent-configs/) — Static config templates for editors/agents
- [Install Docs](https://seashail.com/docs/getting-started/install) — All installation methods
- [Quickstart](https://seashail.com/docs/getting-started/quickstart) — First run guide
ndfes 3.6.5
Companion library to the ndfes C++ program for analyzing umbrella sampling
Requires Python: >=3.7
Keywords: ndfes

INSTALLATION
------------

The configuration is performed with ``cmake``. You will need ``cmake``
(version 3.12 or later installed). The installation of the python
components is performed with ``pip``.

To install ``cmake`` and update your version of ``pip``, you can run::

   USERBASE=$(python3 -m site --user-base)
   USERSITE=$(python3 -m site --user-site)
   export PATH="${USERBASE}/bin:${PATH}"
   export PYTHONPATH="${USERSITE}:${PYTHONPATH}"
   python3 -m pip install pip --upgrade --user
   python3 -m pip install cmake --upgrade --user

You should consider adding the above export commands to your
``${HOME}/.bashrc`` and then ``source ~/.bashrc``.

If, for whatever reason, ``pip`` is unavailable on your system, you
can install it using the directions here:
https://pip.pypa.io/en/stable/installation/

To install ndfes::

   cd build
   bash ./run_cmake.sh
   make install VERBOSE=1 -j4
   cd ../local
   export PATH="${PWD}/bin:${PATH}"
   export PYTHONPATH="${PWD}/lib/python3.XX/site-packages:${PYTHONPATH}"

where ``python3.XX`` should be replaced by the appropriate python version.

ndfes has dependencies on ``blas/lapack`` and ``nlopt``.
The ``cmake`` configuration will check if these libraries are available.
If they are not available, it will automatically download them from
github and install them.

The ``blas/lapack`` libraries are installed from the openblas package.
   https://github.com/OpenMathLib/OpenBLAS
The ``nlopt`` software is used to perform nonlinear optimizations.
   https://github.com/stevengj/nlopt

These libraries could also be installed globally using your system's
package manager. For example, on Fedora, you could install them with::

   sudo dnf install NLopt-devel openblas

See the contents of ``build/run_cmake.sh`` to see how you can adjust
the compilers, compiler flags, python interpreter, and installation
directory.
edgembar 3.6.5
Companion library to the edgembar C++ program for analyzing free energy molecular dynamics simulations
Requires Python: >=3.7
Keywords: mbar

INSTALLATION
------------

The configuration is performed with cmake. You will need cmake
(version 3.12 or later installed). The installation of the python
components is performed with pip.

To install cmake and update your version of pip, you can run:
   USERBASE=$(python3 -m site --user-base)
   USERSITE=$(python3 -m site --user-site)
   export PATH="${USERBASE}/bin:${PATH}"
   export PYTHONPATH="${USERSITE}:${PYTHONPATH}"
   python3 -m pip install pip --upgrade --user
   python3 -m pip install cmake --upgrade --user

You should consider adding the above export commands to your
${HOME}/.bashrc and then "source ~/.bashrc".

If, for whatever reason, pip is unavailable on your system, you
can install it using the directions here:
   https://pip.pypa.io/en/stable/installation/

To install edgembar,
   cd build
   bash ./run_cmake.sh
   make install VERBOSE=1 -j4
   cd ../local
   export PATH="${PWD}/bin:${PATH}"
   export PYTHONPATH="${PWD}/lib/python3.XX/site-packages:${PYTHONPATH}"
where python3.XX should be replaced by the appropriate python version.

Edgembar has dependencies on blas/lapack and nlopt.
The cmake configuration will check if these libraries are available.
If they are not available, it will automatically download them from
github and install them.

The blas/lapack libraries are installed from the openblas package.
   https://github.com/OpenMathLib/OpenBLAS
The nlopt software is used to perform nonlinear optimizations.
   https://github.com/stevengj/nlopt

These libraries could also be installed globally using your system's
package manager. For example, on Fedora, you could install them with:
   sudo dnf install NLopt-devel openblas

See the contents of build/run_cmake.sh to see how you can adjust
the compilers, compiler flags, python interpreter, and installation
directory.
stablehlo-coreml-experimental 0.0.15
Convert StableHLO models into Apple Core ML format
Requires Python: >=3.9
Keywords: converter, coreml, coremltools, hlo, machinelearning, ml, neural, stablehlo, xla

# Convert StableHLO models into Apple Core ML format

**This repo is currently experimental!**

Only a subset of the StableHLO operations have been implemented, and some of them may have restrictions.

Due to the current _dot_general_ op implementation, it is only possible to target iOS >= 18.

Look in the `tests` directory, to see what has currently been tested.

The package is published to PyPi as `stablehlo-coreml-experimental`.

## Converting a model

To convert a StableHLO module, do the following:

```python
import coremltools as ct
from stablehlo_coreml.converter import convert
from stablehlo_coreml import DEFAULT_HLO_PIPELINE

mil_program = convert(hlo_module, minimum_deployment_target=ct.target.iOS18)
cml_model = ct.convert(
    mil_program,
    source="milinternal",
    minimum_deployment_target=ct.target.iOS18,
    pass_pipeline=DEFAULT_HLO_PIPELINE,
)
```

For a Jax project, the `hlo_module` can be obtained the following way:

```python
import jax
from jax._src.lib.mlir import ir
from jax._src.interpreters import mlir as jax_mlir
from jax.export import export

import jax.numpy as jnp

def jax_function(a, b):
    return jnp.einsum("ij,jk -> ik", a, b)

context = jax_mlir.make_ir_context()
input_shapes = (jnp.zeros((2, 4)), jnp.zeros((4, 3)))
jax_exported = export(jax.jit(jax_function))(*input_shapes)
hlo_module = ir.Module.parse(jax_exported.mlir_module(), context=context)
```

For the Jax example to work, you will additionally need to install `absl-py` and `flatbuffers` as dependencies.

For additional examples see the `tests` directory.

## Notes
* `coremltools` supports up to python 3.13. Do not run hatch with a newer version.
  Can be controlled using fx `export HATCH_PYTHON=python3.13`
* Run tests using `hatch run test:pytest tests`
iflow-mcp_shawhint-yt-mcp-agent 0.1.0
Add your description here
Requires Python: >=3.11

# yt-mcp-agent
A YouTube video agent built using a custom MCP server and OpenAI's Agent's SDK. It can extract video transcripts given link fetch specialized instructions for things like: writing blog posts, video chapters, and social posts.

Resources:
- [Talk recording](https://youtu.be/w-Ml3NivoFo)
- [Slides](https://drive.google.com/file/d/1id7V9nrNetW72k6vERS6oTy0bW0wEloo/view?usp=sharing)

>This example is a prelude to [Cohort 7](https://github.com/ShawhinT/AI-Builders-Bootcamp-7/tree/main) of the [AI Builders Bootcamp](https://maven.com/shaw-talebi/ai-builders-bootcamp).

## Requirements

- Python 3.13+
- OpenAI API key
- `uv` package manager (recommended)

## How to run this example

### uv (recommended)

1. **Clone the repository**
   ```bash
   git clone <repository-url>
   cd yt-mcp-agent
   ```

2. **Install dependencies with uv**
   ```bash
   uv sync
   ```

3. **Set up your OpenAI API key**
   
   Create a `.env` file in the root directory:
   ```bash
   echo "OPENAI_API_KEY=your_api_key_here" > .env
   ```

4. **Run the agent**
   ```bash
   uv run main.py
   ```

5. **Interact with agent**
   
   Once running, you can ask the agent to analyze YouTube videos. Try prompts like:
   - "Summarize this: https://youtu.be/N3vHJcHBS-w?si=aw8PV0acYHJGPy7R"
   - "Generate chapter timestamps with links"
   - "Write me a LinkedIn post about the video"

### Base Python/pip

1. **Clone the repository**
   ```bash
   git clone <repository-url>
   cd yt-mcp-agent
   ```

2. **Create a virtual environment**
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. **Install dependencies**
   ```bash
   pip install -e .
   ```

4. **Set up your OpenAI API key**
   
   Create a `.env` file in the root directory:
   ```bash
   echo "OPENAI_API_KEY=your_api_key_here" > .env
   ```

5. **Run the agent**
   ```bash
   python main.py
   ```

6. **Interact with agent**