python-executor

OverviewHistoryStatsSecurity

npx skills add skills-101/superpowers --skill python-executor

Documentation

SKILL.md

npx skills add ...

Documentation

SKILL.md

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

npx skills add skills-101/superpowers --skill python-executor

Documentation

SKILL.mdView source

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.

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

# 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

Running Apps - How to run apps via CLI
App Code - Understanding app execution
Sandboxed Code Execution - Safe code execution for agents

npx skills add skills-101/superpowers --skill python-executor

Documentation

SKILL.md

npx skills add skills-101/superpowers --skill python-executor

Documentation

SKILL.mdView source

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.

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

# 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

Running Apps - How to run apps via CLI
App Code - Understanding app execution
Sandboxed Code Execution - Safe code execution for agents

python-executor

Documentation

Documentation

python-executor

Documentation

Python Code Executor

Quick Start

App Details

Input Schema

Pre-installed Libraries

Web Scraping & HTTP

Data Processing

Image Processing

Video & Audio

3D Processing

Documents & Graphics

Examples

Web Scraping

Data Analysis with Visualization

Image Processing

Video Creation

3D Model Processing

API Calls

File Output

Variants

Use Cases

Important Notes

Related Skills

Documentation

Documentation

Documentation

Python Code Executor

Quick Start

App Details

Input Schema

Pre-installed Libraries

Web Scraping & HTTP

Data Processing

Image Processing

Video & Audio

3D Processing

Documents & Graphics

Examples

Web Scraping

Data Analysis with Visualization

Image Processing

Video Creation

3D Model Processing

API Calls

File Output

Variants

Use Cases

Important Notes

Related Skills

Documentation