MCPInstall

python-executor

Execute Python code in a safe sandboxed environment via inference.sh. Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib

Installs
96.3K
Last 24h
+1.2K
Trending rank
#287
Security
Warn

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/magentosh/superpowers --skill python-executor
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads python-executor automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·This skill provides a legitimate interface for executing Python code in a sandboxed environment on the inference.sh platform. It supports a wide range of pre-installed libraries for data science, web scraping, and media processing. The observed behaviors are consistent with its stated purpose.
Socket Warn1 alert: gptAnomaly
Snyk WarnMEDIUM ·Risk: MEDIUM · 1 issue

SKILL.md

The instructions your agent reads when the skill is triggered.

Install the belt CLI skill: npx skills add belt-sh/cli

Python Code Executor

Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.

Python Code Executor

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Run Python code
belt app run infsh/python-executor --input '{
  "code": "import pandas as pd\nprint(pd.__version__)"
}'

App Details

PropertyValue
App IDinfsh/python-executor
EnvironmentPython 3.10, CPU-only
RAM8GB (default) / 16GB (high_memory)
Timeout1-300 seconds (default: 30)

Input Schema

{
  "code": "print('Hello World!')",
  "timeout": 30,
  "capture_output": true,
  "working_dir": null
}

Pre-installed Libraries

Web Scraping & HTTP

  • requests, httpx, aiohttp - HTTP clients
  • beautifulsoup4, lxml - HTML/XML parsing
  • selenium, playwright - Browser automation
  • scrapy - Web scraping framework

Data Processing

  • numpy, pandas, scipy - Numerical computing
  • matplotlib, seaborn, plotly - Visualization

Image Processing

  • pillow, opencv-python-headless - Image manipulation
  • scikit-image, imageio - Image algorithms

Video & Audio

  • moviepy - Video editing
  • av (PyAV), ffmpeg-python - Video processing
  • pydub - Audio manipulation

3D Processing

  • trimesh, open3d - 3D mesh processing
  • numpy-stl, meshio, pyvista - 3D file formats

Documents & Graphics

  • svgwrite, cairosvg - SVG creation
  • reportlab, pypdf2 - PDF generation

Examples

Web Scraping

belt app run infsh/python-executor --input '{
  "code": "import requests\nfrom bs4 import BeautifulSoup\n\nresponse = requests.get(\"https://example.com\")\nsoup = BeautifulSoup(response.content, \"html.parser\")\nprint(soup.find(\"title\").text)"
}'

Data Analysis with Visualization

belt app run infsh/python-executor --input '{
  "code": "import pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = {\"name\": [\"Alice\", \"Bob\"], \"sales\": [100, 150]}\ndf = pd.DataFrame(data)\n\nplt.bar(df[\"name\"], df[\"sales\"])\nplt.savefig(\"outputs/chart.png\")\nprint(\"Chart saved!\")"
}'

Image Processing

belt app run infsh/python-executor --input '{
  "code": "from PIL import Image\nimport numpy as np\n\n# Create gradient image\narr = np.linspace(0, 255, 256*256, dtype=np.uint8).reshape(256, 256)\nimg = Image.fromarray(arr, mode=\"L\")\nimg.save(\"outputs/gradient.png\")\nprint(\"Image created!\")"
}'

Video Creation

belt app run infsh/python-executor --input '{
  "code": "from moviepy.editor import ColorClip, TextClip, CompositeVideoClip\n\nclip = ColorClip(size=(640, 480), color=(0, 100, 200), duration=3)\ntxt = TextClip(\"Hello!\", fontsize=70, color=\"white\").set_position(\"center\").set_duration(3)\nvideo = CompositeVideoClip([clip, txt])\nvideo.write_videofile(\"outputs/hello.mp4\", fps=24)\nprint(\"Video created!\")",
  "timeout": 120
}'

3D Model Processing

belt app run infsh/python-executor --input '{
  "code": "import trimesh\n\nsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)\nsphere.export(\"outputs/sphere.stl\")\nprint(f\"Created sphere with {len(sphere.vertices)} vertices\")"
}'

API Calls

belt app run infsh/python-executor --input '{
  "code": "import requests\nimport json\n\nresponse = requests.get(\"https://api.github.com/users/octocat\")\ndata = response.json()\nprint(json.dumps(data, indent=2))"
}'

File Output

Files saved to outputs/ are automatically returned:

# These files will be in the response
plt.savefig('outputs/chart.png')
df.to_csv('outputs/data.csv')
video.write_videofile('outputs/video.mp4')
mesh.export('outputs/model.stl')

Variants

# Default (8GB RAM)
belt app run infsh/python-executor --input input.json

# High memory (16GB RAM) for large datasets
belt app run infsh/python-executor@high_memory --input input.json

Use Cases

  • Web scraping - Extract data from websites
  • Data analysis - Process and visualize datasets
  • Image manipulation - Resize, crop, composite images
  • Video creation - Generate videos with text overlays
  • 3D processing - Load, transform, export 3D models
  • API integration - Call external APIs
  • PDF generation - Create reports and documents
  • Automation - Run any Python script

Important Notes

  • CPU-only - No GPU/ML libraries (use dedicated AI apps for that)
  • Safe execution - Runs in isolated subprocess
  • Non-interactive - Use plt.savefig() not plt.show()
  • File detection - Output files are auto-detected and returned

Related Skills

# AI image generation (for ML-based images)
npx skills add inference-sh/skills@ai-image-generation

# AI video generation (for ML-based videos)
npx skills add inference-sh/skills@ai-video-generation

# LLM models (for text generation)
npx skills add inference-sh/skills@llm-models

Documentation

FAQ

What does the python-executor skill do?

Execute Python code in a safe sandboxed environment via inference.sh. Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib

How do I install python-executor?

Run npx skills add https://github.com/magentosh/superpowers --skill python-executor in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-executor/ (personal) or .claude/skills/python-executor/ (project).

Which AI agents can use python-executor?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is python-executor safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket flagged items to review; Snyk flagged items to review. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains python-executor?

It is published by magentosh in magentosh/superpowers. It has 96,275 recorded installs.

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