semvec/pypi-packages huggingface dataset:
iflow-mcp-tullymonster-exa-pool-mcp 0.1.0
Exa Pool MCP Server - Wraps the Exa Pool API as an MCP server for Claude Code
Requires Python: >=3.10

# Exa Pool MCP

轻量的 MCP Server，将 [Exa Pool API](https://github.com/chengtx809/exa-pool) 封装为可供 AI 助手调用的工具集。

## 安装与配置

### 1. 下载

下载 `exa_pool_mcp.py` 到本地：

```bash
# 下载到 ~/.claude/ 目录（推荐）
curl -o ~/.claude/exa_pool_mcp.py https://raw.githubusercontent.com/TullyMonster/exa-pool-mcp/main/exa_pool_mcp.py

# 或克隆整个仓库
git clone https://github.com/TullyMonster/exa-pool-mcp.git && cd exa-pool-mcp
```

### 2. 配置

> 以 Claude Code 为例：

```bash
claude mcp add --transport stdio exa-pool --env EXA_POOL_BASE_URL=... --env EXA_POOL_API_KEY=... -- uv run ~/.claude/exa_pool_mcp.py
```

重启 Claude Code 以应用 MCP 的配置变更。

## ❤️ 致谢与参考

- [Exa Pool GitHub](https://github.com/chengtx809/exa-pool)
- [Exa Docs](https://docs.exa.ai/)
- [Model Context Protocol](https://modelcontextprotocol.io/)
- [FastMCP](https://github.com/jlowin/fastmcp)

若需高使用限额或商业用途，请考虑使用 [Exa 官方 API](https://exa.ai/)。
skelform-pygame 0.2.3
SkelForm runtime for Pygame. Uses the generic runtime
Requires Python: >=3.9

Library for running [SkelForm](https://skelform.org) animations in
[Pygame](https://pygame.org).

```python
import skelform_pygame as skf_pg
```

## Basic Setup

- `skf_pg.load()` - loads `.skf` file and returns armature & textures, to be
  used later
- `skf_pg.animate()` - transforms the armature's bones based on the animation(s)
- `skf_pg.construct()` - provides the bones from this armature that are ready
  for use
- `skf_pg.draw()` - draws the bones on-screen, with the provided style(s)

### 1. Load:

```python
(armature, textures) = skf_pg.load("skellina.skf")
```

This should only be called once (eg; before main game loop), and `armature` and
`textures` should be kept for later use.

### 2\. Animate:

```python
# use `skf_pg.time_frame()` to get the animation frame based on time (1000 = 1 second)
time = 2000
frame = skf_pg.time_frame(time, armature.animations[0], False, True)

print(frame) # will be at the 2 second mark of the animation

armature.bones = skf_pg.animate(armature, [armature.animations[0]], [0], [0])
```

_Note: not needed if armature is statilc_

### 3\. Construct:

```python
center = pygame.Vector2(screen.get_width()/2, screen.get_height()/2)

final_bones = skf_pg.construct(
    armature,
    screen,
    skf_pg.AnimOptions(
      pos=center
    )
)
```

Modifications to the armature (eg; aiming at cursor) may be done before or after
construction.

### 4\. Draw:

```python
skf_pg.draw(final_bones, armature.styles, textures, screen)
```

## Limitations

- Mesh deformation not supported
injective-cli 1.17.2
Injective CLI - Injective blockchain node binary (injectived)
Requires Python: >=3.9
Keywords: blockchain, cosmos, defi, injective, injectived, node, validator

# Injective CLI

Python package wrapper for the Injective blockchain node binary (`injectived`).

## Installation

```bash
pip install injective-cli
```

Or use with uvx (no installation required):

```bash
uvx --from injective-cli injectived --help
```

## Supported Platforms

- macOS ARM64 (Apple Silicon)
- Linux ARM64
- Linux x64

## Using injectived

After installation, the `injectived` command is available:

```bash
injectived --help
injectived init
injectived start
```

### Python API

You can also use the package programmatically:

```python
from injective_core import get_binary_path, run_binary

# Get the path to the binary
binary_path = get_binary_path()
print(f"Binary located at: {binary_path}")

# Run the binary with arguments
run_binary(["--help"])
```

Note: the import path remains `injective_core` even though the package name is `injective-cli`.

## How It Works

This package bundles platform-specific binaries as wheel distributions. When you install from PyPI, pip will download the correct wheel for your platform. Each wheel contains the appropriate `injectived` binary for the target platform.

## Building from Source

To build platform-specific wheels:

```bash
# Set the target platform
export INJECTIVED_PLATFORM=manylinux_2_17_x86_64  # or manylinux_2_17_aarch64, macosx_11_0_arm64, macosx_11_0_x86_64

# Build the wheel
pip install build
python -m build --wheel
```

## License

BUSL-1.1
mlflow-cynex 0.1.3
Cynex trajectory viewer integration for MLflow
Requires Python: >=3.9
Keywords: cybersecurity, mlflow, reinforcement-learning, trajectories, visualization

# mlflow-cynex

Cynex trajectory viewer integration for MLflow. Adds a "View in Cynex" button to MLflow's artifact browser for trajectory JSON files.

## Installation

```bash
pip install mlflow-cynex
mlflow-cynex install
```

Then restart your MLflow server.

## Usage

### Logging Trajectories

The package provides a helper function to log trajectories to MLflow:

```python
from mlflow_cynex import log_trajectory
import mlflow

mlflow.set_tracking_uri("http://localhost:5000")

with mlflow.start_run():
    trajectory = {
        "blue_agent_name": "PPO",
        "red_agent_name": "Meander",
        "episode": 0,
        "blue_actions": [...],
        "red_actions": [...],
        "metric_scores": [...],
        "network_topology": {...}
    }
    log_trajectory(trajectory)
```

The trajectory is automatically:
- Validated against the expected schema
- Saved to `trajectories/<name>-trajectory.json` in MLflow artifacts

### Custom Naming

```python
log_trajectory(trajectory, name="my-custom-name")
# Saved as: trajectories/my-custom-name.json
```

### Validation Only

```python
from mlflow_cynex import validate_trajectory, TrajectoryValidationError

try:
    validate_trajectory(data)
except TrajectoryValidationError as e:
    print(f"Invalid trajectory: {e}")
```

## Trajectory Detection

The "View in Cynex" button appears for JSON files that:
- Are in a `trajectories/` folder, OR
- Match `*-trajectory.json` or `*_trajectory.json` pattern

## CLI Commands

```bash
mlflow-cynex install    # Install Cynex viewer into MLflow
mlflow-cynex uninstall  # Remove Cynex viewer from MLflow
mlflow-cynex status     # Check installation status
```

## Development

Build from source:

```bash
cd cynex/mlflow-integration
./scripts/build.sh
pip install dist/mlflow_cynex-*.whl
```

## License

MIT