aiskillstore/python-executor
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 https://github.com/aiskillstore/marketplace --skill python-executor> Install the belt CLI skill: npx skills add belt-sh/cli
Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.
> 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__)"
}'
| 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) |
{
"code": "print('Hello World!')",
"timeout": 30,
"capture_output": true,
"working_dir": null
}
requests, httpx, aiohttp - HTTP clientsbeautifulsoup4, lxml - HTML/XML parsingselenium, playwright - Browser automationscrapy - Web scraping frameworknumpy, pandas, scipy - Numerical computingmatplotlib, seaborn, plotly - Visualizationpillow, opencv-python-headless - Image manipulationscikit-image, imageio - Image algorithmsmoviepy - Video editingav (PyAV), ffmpeg-python - Video processingpydub - Audio manipulationtrimesh, open3d - 3D mesh processingnumpy-stl, meshio, pyvista - 3D file formatssvgwrite, cairosvg - SVG creationreportlab, pypdf2 - PDF generationbelt 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)"
}'
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!\")"
}'
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!\")"
}'
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
}'
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\")"
}'
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))"
}'
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')
# 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
plt.savefig() not plt.show()# 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
Take aiskillstore/python-executor from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.
The instructions reference npx.
Without those the skill loads but fails at the first command.