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Python Executor Agent Skill

by aiskillstore

Execute Python code in a safe sandboxed environment via [inference.sh](https://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

16k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
404
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/aiskillstore/marketplace --skill python-executor

What comes with it

57 218 bytes besides the instruction
skill-report.json

What it tells the agent to use

found in the instruction text
Bash runs shell commands — read the instruction before connecting

The instruction itself

24 sections, as written by the author

> 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

| Property | Value |

|----------|-------|

| App ID | infsh/python-executor |

| Environment | Python 3.10, CPU-only |

| RAM | 8GB (default) / 16GB (high_memory) |

| Timeout | 1-300 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

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How to use it

Copy the folder

Take aiskillstore/python-executor from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npx. Without those the skill loads but fails at the first command.