[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EVJC0RFHJ34CNH2RW3CVDP":3,"skill-same:01M6EVJC0RFHJ34CNH2RW3CVDP":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":34,"publishedAt":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":67,"ownerHtmlUrl":68,"ownerBlogUrl":6,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M6EVJC0RFHJ34CNH2RW3CVDP","python-executor",null,"Execute Python code in a safe sandboxed environment via [inference.sh](https:\u002F\u002Finference.sh).\nPre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium,\nPlaywright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries.\nUse for: data processing, web scraping, image manipulation, video creation,\n3D model processing, PDF generation, API calls, automation scripts.\nTriggers: python, execute code, run script, web scraping, data analysis,\nimage processing, video editing, 3D models, automation, pandas, matplotlib","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Finference-sh\u002Fpython-executor\u002FSKILL.md",[18,23,26,29],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"python","executor","python executor","3D","pdf generation","processing","creation","scraping","model","video editing","Bash","PDF","Video","api","analysis","pandas","selenium","code","image","data analysis","2026-11-10 10:12:30",false,"---\nname: python-executor\ndescription: |\n  Execute Python code in a safe sandboxed environment via [inference.sh](https:\u002F\u002Finference.sh).\n  Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium,\n  Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries.\n  Use for: data processing, web scraping, image manipulation, video creation,\n  3D model processing, PDF generation, API calls, automation scripts.\n  Triggers: python, execute code, run script, web scraping, data analysis,\n  image processing, video editing, 3D models, automation, pandas, matplotlib\nallowed-tools: Bash(infsh *)\n---\n\n# Python Code Executor\n\nExecute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.\n\n## Quick Start\n\n```bash\ncurl -fsSL https:\u002F\u002Fcli.inference.sh | sh && infsh login\n\n# Run Python code\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import pandas as pd\\nprint(pd.__version__)\"\n}'\n```\n\n## App Details\n\n| Property | Value |\n|----------|-------|\n| App ID | `infsh\u002Fpython-executor` |\n| Environment | Python 3.10, CPU-only |\n| RAM | 8GB (default) \u002F 16GB (high_memory) |\n| Timeout | 1-300 seconds (default: 30) |\n\n## Input Schema\n\n```json\n{\n  \"code\": \"print('Hello World!')\",\n  \"timeout\": 30,\n  \"capture_output\": true,\n  \"working_dir\": null\n}\n```\n\n## Pre-installed Libraries\n\n### Web Scraping & HTTP\n- `requests`, `httpx`, `aiohttp` - HTTP clients\n- `beautifulsoup4`, `lxml` - HTML\u002FXML parsing\n- `selenium`, `playwright` - Browser automation\n- `scrapy` - Web scraping framework\n\n### Data Processing\n- `numpy`, `pandas`, `scipy` - Numerical computing\n- `matplotlib`, `seaborn`, `plotly` - Visualization\n\n### Image Processing\n- `pillow`, `opencv-python-headless` - Image manipulation\n- `scikit-image`, `imageio` - Image algorithms\n\n### Video & Audio\n- `moviepy` - Video editing\n- `av` (PyAV), `ffmpeg-python` - Video processing\n- `pydub` - Audio manipulation\n\n### 3D Processing\n- `trimesh`, `open3d` - 3D mesh processing\n- `numpy-stl`, `meshio`, `pyvista` - 3D file formats\n\n### Documents & Graphics\n- `svgwrite`, `cairosvg` - SVG creation\n- `reportlab`, `pypdf2` - PDF generation\n\n## Examples\n\n### Web Scraping\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import requests\\nfrom bs4 import BeautifulSoup\\n\\nresponse = requests.get(\\\"https:\u002F\u002Fexample.com\\\")\\nsoup = BeautifulSoup(response.content, \\\"html.parser\\\")\\nprint(soup.find(\\\"title\\\").text)\"\n}'\n```\n\n### Data Analysis with Visualization\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"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\u002Fchart.png\\\")\\nprint(\\\"Chart saved!\\\")\"\n}'\n```\n\n### Image Processing\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"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\u002Fgradient.png\\\")\\nprint(\\\"Image created!\\\")\"\n}'\n```\n\n### Video Creation\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"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\u002Fhello.mp4\\\", fps=24)\\nprint(\\\"Video created!\\\")\",\n  \"timeout\": 120\n}'\n```\n\n### 3D Model Processing\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import trimesh\\n\\nsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)\\nsphere.export(\\\"outputs\u002Fsphere.stl\\\")\\nprint(f\\\"Created sphere with {len(sphere.vertices)} vertices\\\")\"\n}'\n```\n\n### API Calls\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import requests\\nimport json\\n\\nresponse = requests.get(\\\"https:\u002F\u002Fapi.github.com\u002Fusers\u002Foctocat\\\")\\ndata = response.json()\\nprint(json.dumps(data, indent=2))\"\n}'\n```\n\n## File Output\n\nFiles saved to `outputs\u002F` are automatically returned:\n\n```python\n# These files will be in the response\nplt.savefig('outputs\u002Fchart.png')\ndf.to_csv('outputs\u002Fdata.csv')\nvideo.write_videofile('outputs\u002Fvideo.mp4')\nmesh.export('outputs\u002Fmodel.stl')\n```\n\n## Variants\n\n```bash\n# Default (8GB RAM)\ninfsh app run infsh\u002Fpython-executor --input input.json\n\n# High memory (16GB RAM) for large datasets\ninfsh app run infsh\u002Fpython-executor@high_memory --input input.json\n```\n\n## Use Cases\n\n- **Web scraping** - Extract data from websites\n- **Data analysis** - Process and visualize datasets\n- **Image manipulation** - Resize, crop, composite images\n- **Video creation** - Generate videos with text overlays\n- **3D processing** - Load, transform, export 3D models\n- **API integration** - Call external APIs\n- **PDF generation** - Create reports and documents\n- **Automation** - Run any Python script\n\n## Important Notes\n\n- **CPU-only** - No GPU\u002FML libraries (use dedicated AI apps for that)\n- **Safe execution** - Runs in isolated subprocess\n- **Non-interactive** - Use `plt.savefig()` not `plt.show()`\n- **File detection** - Output files are auto-detected and returned\n\n## Related Skills\n\n```bash\n# AI image generation (for ML-based images)\nnpx skills add inference-sh\u002Fskills@ai-image-generation\n\n# AI video generation (for ML-based videos)\nnpx skills add inference-sh\u002Fskills@ai-video-generation\n\n# LLM models (for text generation)\nnpx skills add inference-sh\u002Fskills@llm-models\n```\n\n## Documentation\n\n- [Running Apps](https:\u002F\u002Finference.sh\u002Fdocs\u002Fapps\u002Frunning) - How to run apps via CLI\n- [App Code](https:\u002F\u002Finference.sh\u002Fdocs\u002Fextend\u002Fapp-code) - Understanding app execution\n- [Sandboxed Code Execution](https:\u002F\u002Finference.sh\u002Fblog\u002Ftools\u002Fsandboxed-execution) - Safe code execution for agents\n","# Python Code Executor\n\nExecute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.\n\n## Quick Start\n\n```bash\ncurl -fsSL https:\u002F\u002Fcli.inference.sh | sh && infsh login\n\n# Run Python code\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import pandas as pd\\nprint(pd.__version__)\"\n}'\n```\n\n## App Details\n\n| Property | Value |\n|----------|-------|\n| App ID | `infsh\u002Fpython-executor` |\n| Environment | Python 3.10, CPU-only |\n| RAM | 8GB (default) \u002F 16GB (high_memory) |\n| Timeout | 1-300 seconds (default: 30) |\n\n## Input Schema\n\n```json\n{\n  \"code\": \"print('Hello World!')\",\n  \"timeout\": 30,\n  \"capture_output\": true,\n  \"working_dir\": null\n}\n```\n\n## Pre-installed Libraries\n\n### Web Scraping & HTTP\n- `requests`, `httpx`, `aiohttp` - HTTP clients\n- `beautifulsoup4`, `lxml` - HTML\u002FXML parsing\n- `selenium`, `playwright` - Browser automation\n- `scrapy` - Web scraping framework\n\n### Data Processing\n- `numpy`, `pandas`, `scipy` - Numerical computing\n- `matplotlib`, `seaborn`, `plotly` - Visualization\n\n### Image Processing\n- `pillow`, `opencv-python-headless` - Image manipulation\n- `scikit-image`, `imageio` - Image algorithms\n\n### Video & Audio\n- `moviepy` - Video editing\n- `av` (PyAV), `ffmpeg-python` - Video processing\n- `pydub` - Audio manipulation\n\n### 3D Processing\n- `trimesh`, `open3d` - 3D mesh processing\n- `numpy-stl`, `meshio`, `pyvista` - 3D file formats\n\n### Documents & Graphics\n- `svgwrite`, `cairosvg` - SVG creation\n- `reportlab`, `pypdf2` - PDF generation\n\n## Examples\n\n### Web Scraping\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import requests\\nfrom bs4 import BeautifulSoup\\n\\nresponse = requests.get(\\\"https:\u002F\u002Fexample.com\\\")\\nsoup = BeautifulSoup(response.content, \\\"html.parser\\\")\\nprint(soup.find(\\\"title\\\").text)\"\n}'\n```\n\n### Data Analysis with Visualization\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"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\u002Fchart.png\\\")\\nprint(\\\"Chart saved!\\\")\"\n}'\n```\n\n### Image Processing\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"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\u002Fgradient.png\\\")\\nprint(\\\"Image created!\\\")\"\n}'\n```\n\n### Video Creation\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"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\u002Fhello.mp4\\\", fps=24)\\nprint(\\\"Video created!\\\")\",\n  \"timeout\": 120\n}'\n```\n\n### 3D Model Processing\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import trimesh\\n\\nsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)\\nsphere.export(\\\"outputs\u002Fsphere.stl\\\")\\nprint(f\\\"Created sphere with {len(sphere.vertices)} vertices\\\")\"\n}'\n```\n\n### API Calls\n\n```bash\ninfsh app run infsh\u002Fpython-executor --input '{\n  \"code\": \"import requests\\nimport json\\n\\nresponse = requests.get(\\\"https:\u002F\u002Fapi.github.com\u002Fusers\u002Foctocat\\\")\\ndata = response.json()\\nprint(json.dumps(data, indent=2))\"\n}'\n```\n\n## File Output\n\nFiles saved to `outputs\u002F` are automatically returned:\n\n```python\n# These files will be in the response\nplt.savefig('outputs\u002Fchart.png')\ndf.to_csv('outputs\u002Fdata.csv')\nvideo.write_videofile('outputs\u002Fvideo.mp4')\nmesh.export('outputs\u002Fmodel.stl')\n```\n\n## Variants\n\n```bash\n# Default (8GB RAM)\ninfsh app run infsh\u002Fpython-executor --input input.json\n\n# High memory (16GB RAM) for large datasets\ninfsh app run infsh\u002Fpython-executor@high_memory --input input.json\n```\n\n## Use Cases\n\n- **Web scraping** - Extract data from websites\n- **Data analysis** - Process and visualize datasets\n- **Image manipulation** - Resize, crop, composite images\n- **Video creation** - Generate videos with text overlays\n- **3D processing** - Load, transform, export 3D models\n- **API integration** - Call external APIs\n- **PDF generation** - Create reports and documents\n- **Automation** - Run any Python script\n\n## Important Notes\n\n- **CPU-only** - No GPU\u002FML libraries (use dedicated AI apps for that)\n- **Safe execution** - Runs in isolated subprocess\n- **Non-interactive** - Use `plt.savefig()` not `plt.show()`\n- **File detection** - Output files are auto-detected and returned\n\n## Related Skills\n\n```bash\n# AI image generation (for ML-based images)\nnpx skills add inference-sh\u002Fskills@ai-image-generation\n\n# AI video generation (for ML-based videos)\nnpx skills add inference-sh\u002Fskills@ai-video-generation\n\n# LLM models (for text generation)\nnpx skills add inference-sh\u002Fskills@llm-models\n```\n\n## Documentation\n\n- [Running Apps](https:\u002F\u002Finference.sh\u002Fdocs\u002Fapps\u002Frunning) - How to run apps via CLI\n- [App Code](https:\u002F\u002Finference.sh\u002Fdocs\u002Fextend\u002Fapp-code) - Understanding app execution\n- [Sandboxed Code Execution](https:\u002F\u002Finference.sh\u002Fblog\u002Ftools\u002Fsandboxed-execution) - Safe code execution for agents\n",{"name":5,"description":60,"allowed-tools":61},"Execute Python code in a safe sandboxed environment via [inference.sh](https:\u002F\u002Finference.sh).\nPre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium,\nPlaywright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries.\nUse for: data processing, web scraping, image manipulation, video creation,\n3D model processing, PDF generation, API calls, automation scripts.\nTriggers: python, execute code, run script, web scraping, data analysis,\nimage processing, video editing, 3D models, automation, pandas, matplotlib\n","Bash(infsh *)","main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Faiskillstore\u002Fmarketplace\u002Finference-sh\u002Fpython-executor",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill python-executor",{"tool":77,"command":78},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill python-executor",{"tool":80,"command":81},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill 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