[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FZB9YA1HG316END358YYWD":3,"skill-same:01M6FZB9YA1HG316END358YYWD":88},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":33,"publishedAt":54,"linkOnly":55,"markdownContent":56,"markdownBody":57,"frontMatter":58,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":67,"install":68,"resources":81},"01M6FZB9YA1HG316END358YYWD","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","kbarbel640-del\u002Fskills","kbarbel640-del","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F255315490?v=4","01M6CJPPQGCCESXMG3K7BBGQ5J","01M6CJPPX2PB2BCZERFVM2C2B3",1,"MIT","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fokaris\u002Fpython-executor\u002FSKILL.md",[17,22,25,28],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":26,"categoryName":27,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":29,"categoryName":30,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53],"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\u002Fagent-skills@ai-image-generation\n\n# AI video generation (for ML-based videos)\nnpx skills add inference-sh\u002Fagent-skills@ai-video-generation\n\n# LLM models (for text generation)\nnpx skills add inference-sh\u002Fagent-skills@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\u002Fagent-skills@ai-image-generation\n\n# AI video generation (for ML-based videos)\nnpx skills add inference-sh\u002Fagent-skills@ai-video-generation\n\n# LLM models (for text generation)\nnpx skills add inference-sh\u002Fagent-skills@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":59,"allowed-tools":60},"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","e7e336ac628c3e3bd771aff1aaa469c93778f94a","skills\u002Fokaris\u002Fpython-executor",0,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del","2026-08-23 22:39:37",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add kbarbel640-del\u002Fskills --skill python-executor",{"tool":74,"command":75},"bunx","bunx skills add kbarbel640-del\u002Fskills --skill python-executor",{"tool":77,"command":78},"pnpm","pnpm dlx skills add kbarbel640-del\u002Fskills --skill python-executor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fokaris\u002Fpython-executor\u002FSKILL.md",true,[82],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"_meta.json","REFERENCE","参考文档",282,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fokaris\u002Fpython-executor\u002F_meta.json",[89,117,144,165,171,191],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":98,"publishedAt":54},"01M6G8J55967D6281ACMKAKTEQ","crowd-prompting","A marketplace where AI agents improve prompts, system instructions, tool descriptions, and other text-based content with domain expertise from real-world operations — and earn tokens for valuable contributions.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzzadrian\u002Fcrowd-prompting\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000036","内容创作者",[99,100,47,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116],"crowd prompting","prompting","with","tokens","domain","world","prompts","operations","instructions","text","tool","system","improve","productivity","marketplace","https","content","ai agents",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":126,"publishedAt":54},"01M6G8J0PKPAYVET6BWE8W4C26","dilemm-ai","Compete in DilemmAI, the prisoner's dilemma AI arena at dilemm.ai. Use when an OpenClaw agent wants to create an account, design and submit strategy prompts for their bot, enter matchmaking, analyze historical matches and leaderboards, and iterate on bot strategy. Requires headless browser automation (no API available yet).","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzyori\u002Fdilemm-ai\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[127,47,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,105,143],"dilemm ai","arena","analyze","browser","openclaw agent","create","automation","browser automation","bot","openclaw","design","strategy","headless","compete","submit","matchmaking","use",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":153,"publishedAt":54},"01M6G8HWAF3K9SW7NP3Z83CPJ6","arxiv-paper-reviews","Interact with arXiv Crawler API to fetch papers, read reviews, and submit comments. Use when working with arXiv papers, fetching paper lists by date\u002Fcategory\u002Finterest, viewing paper details with comments, or submitting paper reviews via API at http:\u002F\u002F150.158.152.82:8000.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Fweak-accept\u002FSKILL.md",[150],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",[154,155,156,157,158,159,160,101,161,141,143,162,47,163,164],"arxiv paper reviews","reviews","arxiv","paper","crawler","papers","comments","fetch","read","http","via",{"skillNo":166,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":170,"publishedAt":54},"01M6G8HQJ7N7WMY6YV7D7SSJ16","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Farxiv-paper-reviews\u002FSKILL.md",[169],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},[157,156,155,154,164,159,160,101,158,141,143,161,162,47,163],{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":54},"01M6G8HM95Y64JNVFC2HN4E2BG","git-summary","Get a quick summary of the current Git repository including status, recent commits, branches, and contributors.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzweack\u002Fgit-summary\u002FSKILL.md",[],[178,179,180,181,182,183,184,185,186,187,188,189,190],"git","summary","git summary","commits","repository","win32","status","linux","contributors","true","quick","get","branches",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":201,"publishedAt":54},"01M6G8HGEGF05CNEJCGVYFSC4R","agent-constitution","Interact with AgentConstitution governance contracts on Base Sepolia. Check compliance, read rules, log actions, query governance state.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fztsalexey\u002Fagent-constitution\u002FSKILL.md",[197,198],{"categoryNo":124,"categoryName":125,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":199,"categoryName":200,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000037","律师与法务",[202,203,204,205,101,206,207,61,208,162,209,210,211,212,213,114,214,215,216],"agent constitution","constitution","contracts","tree","query","base","log","check","Rules","projects","compliance","actions","state","governance","github"]