[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EBK636VPK3NFHTCJ1YCCC3":3,"skill-same:01M6EBK636VPK3NFHTCJ1YCCC3":78},{"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":23,"publishedAt":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":46,"updatedAt":47,"install":48,"resources":61},"01M6EBK636VPK3NFHTCJ1YCCC3","oc-arxiv-skill-learning",null,"Orchestrates the continuous learning of new skills from arXiv papers.","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Foc-arxiv-skill-learning\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29],"learning","arxiv","oc arxiv skill learning","python","Tools","continuous learning","2026-11-10 10:12:30",false,"---\nauthor: luo-kai\nname: oc-arxiv-skill-learning\nversion: 1.0.0\ndescription: Orchestrates the continuous learning of new skills from arXiv papers.\nlicense: MIT\nmetadata:\n  author: luokai0\n  version: \"1.0\"\n  category: python-tools\n---\n\n# Arxiv Skill Learning\n\nYou are an expert python engineer. Orchestrates the continuous learning of new skills from arXiv papers.\n\n## Before Starting\n\n1. **Goal** — what specific outcome do you need?\n2. **Environment** — versions, platform, existing setup?\n3. **Constraints** — performance, security, compatibility requirements?\n4. **Integration** — what systems does this connect to?\n5. **Output format** — code, config, script, or documentation?\n\n---\n\n## Core Expertise Areas\n\n- **Core implementation** — full working code for Arxiv Skill Learning\n- **Error handling** — robust error recovery and logging\n- **Performance** — optimized patterns for production use\n- **Testing** — unit and integration test strategies\n- **Configuration** — environment-specific setup and tuning\n- **Security** — secure coding patterns and best practices\n- **Documentation** — clear API and usage documentation\n\n---\n\n## Key Patterns & Code\n\n### Core Implementation\n\n```python\nimport anthropic\nimport json\n\nclient = anthropic.Anthropic()\n\nTOOLS = [\n    {\n        \"name\": \"search\",\n        \"description\": \"Search for information on a topic\",\n        \"input_schema\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"query\": {\"type\": \"string\", \"description\": \"Search query\"}\n            },\n            \"required\": [\"query\"]\n        }\n    }\n]\n\ndef handle_tool(name: str, inputs: dict) -> str:\n    if name == \"search\":\n        return f\"Search results for: {inputs['query']}\"\n    return f\"Unknown tool: {name}\"\n\ndef run_arxiv_skill_learning(task: str, max_turns: int = 10) -> str:\n    messages = [{\"role\": \"user\", \"content\": task}]\n    for _ in range(max_turns):\n        response = client.messages.create(\n            model=\"claude-sonnet-4-6\",\n            max_tokens=4096,\n            tools=TOOLS,\n            messages=messages\n        )\n        messages.append({\"role\": \"assistant\", \"content\": response.content})\n        if response.stop_reason == \"end_turn\":\n            return next((b.text for b in response.content if hasattr(b, 'text')), \"\")\n        if response.stop_reason == \"tool_use\":\n            results = [\n                {\"type\": \"tool_result\", \"tool_use_id\": b.id,\n                  \"content\": handle_tool(b.name, b.input)}\n                for b in response.content if b.type == \"tool_use\"\n            ]\n            messages.append({\"role\": \"user\", \"content\": results})\n    return \"Max turns reached\"\n\nif __name__ == \"__main__\":\n    result = run_arxiv_skill_learning(\"ArxivSkillLearning task: analyze and report\")\n    print(result)\n```\n\n### Configuration & Setup\n```python\n# Arxiv Skill Learning — Configuration\n# Author: luo-kai (Lous Creations)\n\nconfig = {\n    \"name\": \"arxiv-skill-learning\",\n    \"version\": \"1.0.0\",\n    \"author\": \"luo-kai\",\n    \"enabled\": True,\n    \"debug\": False,\n    \"timeout_seconds\": 30,\n    \"max_retries\": 3,\n}\n```\n\n### Error Handling\n```python\n# Robust error handling pattern\nimport logging\nlogger = logging.getLogger(\"arxiv-skill-learning\")\n\ndef safe_run(func, *args, **kwargs):\n    try:\n        return func(*args, **kwargs)\n    except Exception as e:\n        logger.error(f\"arxiv-skill-learning error: {e}\", exc_info=True)\n        raise\n```\n\n---\n\n## Best Practices\n\n- **Fail fast with clear errors** — raise descriptive exceptions with context\n- **Log at appropriate levels** — DEBUG for dev, INFO for ops, ERROR for problems\n- **Validate inputs** — never trust external data without validation\n- **Use type annotations** — improves IDE support and catches bugs early\n- **Handle cleanup** — use context managers and `finally` blocks\n- **Test edge cases** — empty inputs, nulls, max values, concurrent access\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Problem | Fix |\n|---------|---------|-----|\n| No error handling | Silent failures in production | Wrap with try\u002Fexcept + logging |\n| Hardcoded values | Not portable across environments | Use config\u002Fenv vars |\n| Missing timeouts | Hangs indefinitely | Always set timeout values |\n| No retry logic | Single failure = broken workflow | Add exponential backoff |\n| No cleanup on exit | Resource leaks | Use context managers |\n\n---\n\n## Related Skills\n\n- python-expert\n- arxiv-skill-learning-advanced\n- performance-optimization\n- error-handling\n- testing-expert\n","# Arxiv Skill Learning\n\nYou are an expert python engineer. Orchestrates the continuous learning of new skills from arXiv papers.\n\n## Before Starting\n\n1. **Goal** — what specific outcome do you need?\n2. **Environment** — versions, platform, existing setup?\n3. **Constraints** — performance, security, compatibility requirements?\n4. **Integration** — what systems does this connect to?\n5. **Output format** — code, config, script, or documentation?\n\n---\n\n## Core Expertise Areas\n\n- **Core implementation** — full working code for Arxiv Skill Learning\n- **Error handling** — robust error recovery and logging\n- **Performance** — optimized patterns for production use\n- **Testing** — unit and integration test strategies\n- **Configuration** — environment-specific setup and tuning\n- **Security** — secure coding patterns and best practices\n- **Documentation** — clear API and usage documentation\n\n---\n\n## Key Patterns & Code\n\n### Core Implementation\n\n```python\nimport anthropic\nimport json\n\nclient = anthropic.Anthropic()\n\nTOOLS = [\n    {\n        \"name\": \"search\",\n        \"description\": \"Search for information on a topic\",\n        \"input_schema\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"query\": {\"type\": \"string\", \"description\": \"Search query\"}\n            },\n            \"required\": [\"query\"]\n        }\n    }\n]\n\ndef handle_tool(name: str, inputs: dict) -> str:\n    if name == \"search\":\n        return f\"Search results for: {inputs['query']}\"\n    return f\"Unknown tool: {name}\"\n\ndef run_arxiv_skill_learning(task: str, max_turns: int = 10) -> str:\n    messages = [{\"role\": \"user\", \"content\": task}]\n    for _ in range(max_turns):\n        response = client.messages.create(\n            model=\"claude-sonnet-4-6\",\n            max_tokens=4096,\n            tools=TOOLS,\n            messages=messages\n        )\n        messages.append({\"role\": \"assistant\", \"content\": response.content})\n        if response.stop_reason == \"end_turn\":\n            return next((b.text for b in response.content if hasattr(b, 'text')), \"\")\n        if response.stop_reason == \"tool_use\":\n            results = [\n                {\"type\": \"tool_result\", \"tool_use_id\": b.id,\n                  \"content\": handle_tool(b.name, b.input)}\n                for b in response.content if b.type == \"tool_use\"\n            ]\n            messages.append({\"role\": \"user\", \"content\": results})\n    return \"Max turns reached\"\n\nif __name__ == \"__main__\":\n    result = run_arxiv_skill_learning(\"ArxivSkillLearning task: analyze and report\")\n    print(result)\n```\n\n### Configuration & Setup\n```python\n# Arxiv Skill Learning — Configuration\n# Author: luo-kai (Lous Creations)\n\nconfig = {\n    \"name\": \"arxiv-skill-learning\",\n    \"version\": \"1.0.0\",\n    \"author\": \"luo-kai\",\n    \"enabled\": True,\n    \"debug\": False,\n    \"timeout_seconds\": 30,\n    \"max_retries\": 3,\n}\n```\n\n### Error Handling\n```python\n# Robust error handling pattern\nimport logging\nlogger = logging.getLogger(\"arxiv-skill-learning\")\n\ndef safe_run(func, *args, **kwargs):\n    try:\n        return func(*args, **kwargs)\n    except Exception as e:\n        logger.error(f\"arxiv-skill-learning error: {e}\", exc_info=True)\n        raise\n```\n\n---\n\n## Best Practices\n\n- **Fail fast with clear errors** — raise descriptive exceptions with context\n- **Log at appropriate levels** — DEBUG for dev, INFO for ops, ERROR for problems\n- **Validate inputs** — never trust external data without validation\n- **Use type annotations** — improves IDE support and catches bugs early\n- **Handle cleanup** — use context managers and `finally` blocks\n- **Test edge cases** — empty inputs, nulls, max values, concurrent access\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Problem | Fix |\n|---------|---------|-----|\n| No error handling | Silent failures in production | Wrap with try\u002Fexcept + logging |\n| Hardcoded values | Not portable across environments | Use config\u002Fenv vars |\n| Missing timeouts | Hangs indefinitely | Always set timeout values |\n| No retry logic | Single failure = broken workflow | Add exponential backoff |\n| No cleanup on exit | Resource leaks | Use context managers |\n\n---\n\n## Related Skills\n\n- python-expert\n- arxiv-skill-learning-advanced\n- performance-optimization\n- error-handling\n- testing-expert\n",{"name":5,"author":35,"license":15,"version":36,"metadata":37,"description":7},"luo-kai","1.0.0",{"author":9,"version":38,"category":39},"1.0","python-tools","main","4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c","ai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Foc-arxiv-skill-learning",1,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai","https:\u002F\u002Fgithub.com\u002Fluokai0","a solo builder.","2026-05-06 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