python-executor

name	python-executor
description	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
allowed-tools	Bash(infsh *)

Python Code Executor

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

Quick Start

Requires inference.sh CLI (infsh). Install instructions

infsh login

# Run Python code
infsh 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

infsh 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

infsh 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

infsh app run infsh/python-executor --input '{
  "code": "from PIL import Image\nimport numpy as np\n\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

infsh 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

infsh 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

infsh 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)
infsh app run infsh/python-executor --input input.json

# High memory (16GB RAM) for large datasets
infsh 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

Shamrock Trading Bot Integration

This skill is useful for the Shamrock Trading Bot when you need to:

• Analyze trade data — Run pandas/numpy analysis on historical trades
• Generate charts — Create performance visualizations (PnL, win rate, drawdown)
• Scrape market data — Fetch supplementary data from web sources
• Process images — Generate report graphics for Slack/Telegram
• Backtest strategies — Run quick backtesting scripts in isolation
• Debug data pipelines — Test data transformations without modifying production code

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

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