Skill: okaris/python-executor (version 0.1.5)

--- 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. ![Python Code Executor](https://cloud.inference.sh/u/33sqbmzt3mrg2xxphnhw5g5ear/01k8d8b4mckh6z89dhtxh72dsz.png) ## Quick Start ```bash curl -fsSL https://cli.inference.sh | sh && infsh login # Run Python code infsh app run infsh/python-executor --input '{ "code": "import pandas as pd\nprint(pd.__version__)" }' ``` > **Install note:** The [install script](https://cli.inference.sh) only detects your OS/architecture, downloads the matching binary from `dist.inference.sh`, and verifies its SHA-256 checksum. No elevated permissions or background processes. [Manual install & verification](https://dist.inference.sh/cli/checksums.txt) available. ## 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 ```json { "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 ```bash 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 ```bash 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 ```bash infsh 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 ```bash 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 ```bash 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 ```bash 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: ```python # 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 ```bash # 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 ## 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 ```bash # 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](https://inference.sh/docs/apps/running) - How to run apps via CLI - [App Code](https://inference.sh/docs/extend/app-code) - Understanding app execution - [Sandboxed Code Execution](https://inference.sh/blog/tools/sandboxed-execution) - Safe code execution for agents


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)