사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/bankai-skills/superpowers --skill python-executor시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Install the belt CLI skill: npx skills add belt-sh/cliPython Code Executor
Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.
Quick Start
Requires inference.sh CLI (belt). Install instructionsbash
belt login
# Run Python code
belt 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
json
{
"code": "print('Hello World!')",
"timeout": 30,
"capture_output": true,
"working_dir": null
}Pre-installed Libraries
Web Scraping & HTTP
requests,httpx,aiohttp- HTTP clientsbeautifulsoup4,lxml- HTML/XML parsingselenium,playwright- Browser automationscrapy- Web scraping framework
Data Processing
numpy,pandas,scipy- Numerical computingmatplotlib,seaborn,plotly- Visualization
Image Processing
pillow,opencv-python-headless- Image manipulationscikit-image,imageio- Image algorithms
Video & Audio
moviepy- Video editingav(PyAV),ffmpeg-python- Video processingpydub- Audio manipulation
3D Processing
trimesh,open3d- 3D mesh processingnumpy-stl,meshio,pyvista- 3D file formats
Documents & Graphics
svgwrite,cairosvg- SVG creationreportlab,pypdf2- PDF generation
Examples
Web Scraping
bash
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
bash
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
bash
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
bash
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
bash
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
bash
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:
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)
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.jsonUse 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()notplt.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-modelsDocumentation
- Running Apps - How to run apps via CLI
- App Code - Understanding app execution
- Sandboxed Code Execution - Safe code execution for agents
