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python-executor

★ 584

by halt-catch-fire · part of halt-catch-fire/skills

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 model

🔌 This skill ships inside the inference-sh plugin — installing the plugin keeps everything updated together.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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

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
}

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