[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DMPV4AMCCTWARGPSFHDWJB":3,"skill-same:01M6DMPV4AMCCTWARGPSFHDWJB":66},{"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":26,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6DMPV4AMCCTWARGPSFHDWJB","scientific-literature-search",null,"Search scientific literature and research papers using FlowSearch to find relevant academic articles and publications.","InternScience\u002FDrClaw","InternScience","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F154449623?v=4","01M6DHY1ZF02QASNAVMNPT7NP6","Intern Science","01M6DHY2523FY56Q5572NKAAD0",172,"MIT","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fscientific-literature-search\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32],"search","scientific literature search","research","find","Academic","搜索","2026-11-10 10:12:30",false,"---\nname: scientific-literature-search\ndescription: Search scientific literature and research papers using FlowSearch to\n  find relevant academic articles and publications.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 使用FlowSearch搜索科。\n---\n\n# Scientific Literature Search\n\n## Usage\n\n### 1. MCP Server Definition\n\n```python\nimport asyncio\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\n\nclass InternAgentClient:\n    \"\"\"InternAgent MCP Client\"\"\"\n\n    def __init__(self, server_url: str, api_key: str):\n        self.server_url = server_url\n        self.api_key = api_key\n        self.session = None\n\n    async def connect(self):\n        try:\n            self.transport = streamablehttp_client(\n                url=self.server_url,\n                headers={\"SCP-HUB-API-KEY\": self.api_key}\n            )\n            self.read, self.write, self.get_session_id = await self.transport.__aenter__()\n            self.session_ctx = ClientSession(self.read, self.write)\n            self.session = await self.session_ctx.__aenter__()\n            await self.session.initialize()\n            return True\n        except Exception as e:\n            print(f\"✗ connect failure: {e}\")\n            return False\n\n    async def disconnect(self):\n        try:\n            if self.session:\n                await self.session_ctx.__aexit__(None, None, None)\n            if hasattr(self, 'transport'):\n                await self.transport.__aexit__(None, None, None)\n        except Exception as e:\n            print(f\"✗ disconnect error: {e}\")\n\n    def parse_result(self, result):\n        try:\n            if hasattr(result, 'content') and result.content:\n                content = result.content[0]\n                if hasattr(content, 'text'):\n                    return json.loads(content.text)\n            return str(result)\n        except Exception as e:\n            return {\"error\": f\"parse error: {e}\", \"raw\": str(result)}\n```\n\n### 2. Literature Search Workflow\n\nSearch and analyze scientific literature on a research topic.\n\n**Workflow Steps:**\n\n1. **Define Query** - Specify research question or topic\n2. **Execute Search** - Query scientific databases\n3. **Analyze Results** - Extract key findings and trends\n\n**Implementation:**\n\n```python\n## Initialize client\nclient = InternAgentClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F28\u002FInternAgent\",\n    \"\u003Cyour-api-key>\"\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\n## Input: Research query\nprompt = \"Analyze the latest trends in AI research for drug discovery\"\n\n## Execute literature search\nresult = await client.session.call_tool(\n    \"FlowSearch\",\n    arguments={\n        \"prompt\": prompt,\n        \"file_list\": None\n    }\n)\n\ndata = client.parse_result(result)\n\nif data.get('success'):\n    print(\"✅ Literature search completed\")\n    print(f\"\\nResults:\\n{data['result']}\")\nelse:\n    print(f\"❌ Search failed: {data.get('error', 'Unknown error')}\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**InternAgent Server:**\n- `FlowSearch`: Search and analyze scientific literature\n  - Args:\n    - `prompt` (str): Research query or question\n    - `file_list` (list, optional): Additional files to analyze\n  - Returns:\n    - `success` (bool): Search status\n    - `result` (str): Search results and analysis\n\n### Use Cases\n\n- Literature review for research papers\n- Trend analysis in scientific fields\n- Systematic literature searches\n- Citation and reference discovery\n- Research gap identification\n\n### Performance Notes\n\n- **Execution time**: 10-60 seconds depending on query complexity\n- **Data sources**: Multiple scientific databases\n- **Output**: Comprehensive analysis with key findings\n","# Scientific Literature Search\n\n## Usage\n\n### 1. MCP Server Definition\n\n```python\nimport asyncio\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\n\nclass InternAgentClient:\n    \"\"\"InternAgent MCP Client\"\"\"\n\n    def __init__(self, server_url: str, api_key: str):\n        self.server_url = server_url\n        self.api_key = api_key\n        self.session = None\n\n    async def connect(self):\n        try:\n            self.transport = streamablehttp_client(\n                url=self.server_url,\n                headers={\"SCP-HUB-API-KEY\": self.api_key}\n            )\n            self.read, self.write, self.get_session_id = await self.transport.__aenter__()\n            self.session_ctx = ClientSession(self.read, self.write)\n            self.session = await self.session_ctx.__aenter__()\n            await self.session.initialize()\n            return True\n        except Exception as e:\n            print(f\"✗ connect failure: {e}\")\n            return False\n\n    async def disconnect(self):\n        try:\n            if self.session:\n                await self.session_ctx.__aexit__(None, None, None)\n            if hasattr(self, 'transport'):\n                await self.transport.__aexit__(None, None, None)\n        except Exception as e:\n            print(f\"✗ disconnect error: {e}\")\n\n    def parse_result(self, result):\n        try:\n            if hasattr(result, 'content') and result.content:\n                content = result.content[0]\n                if hasattr(content, 'text'):\n                    return json.loads(content.text)\n            return str(result)\n        except Exception as e:\n            return {\"error\": f\"parse error: {e}\", \"raw\": str(result)}\n```\n\n### 2. Literature Search Workflow\n\nSearch and analyze scientific literature on a research topic.\n\n**Workflow Steps:**\n\n1. **Define Query** - Specify research question or topic\n2. **Execute Search** - Query scientific databases\n3. **Analyze Results** - Extract key findings and trends\n\n**Implementation:**\n\n```python\n## Initialize client\nclient = InternAgentClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F28\u002FInternAgent\",\n    \"\u003Cyour-api-key>\"\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\n## Input: Research query\nprompt = \"Analyze the latest trends in AI research for drug discovery\"\n\n## Execute literature search\nresult = await client.session.call_tool(\n    \"FlowSearch\",\n    arguments={\n        \"prompt\": prompt,\n        \"file_list\": None\n    }\n)\n\ndata = client.parse_result(result)\n\nif data.get('success'):\n    print(\"✅ Literature search completed\")\n    print(f\"\\nResults:\\n{data['result']}\")\nelse:\n    print(f\"❌ Search failed: {data.get('error', 'Unknown error')}\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**InternAgent Server:**\n- `FlowSearch`: Search and analyze scientific literature\n  - Args:\n    - `prompt` (str): Research query or question\n    - `file_list` (list, optional): Additional files to analyze\n  - Returns:\n    - `success` (bool): Search status\n    - `result` (str): Search results and analysis\n\n### Use Cases\n\n- Literature review for research papers\n- Trend analysis in scientific fields\n- Systematic literature searches\n- Citation and reference discovery\n- Research gap identification\n\n### Performance Notes\n\n- **Execution time**: 10-60 seconds depending on query complexity\n- **Data sources**: Multiple scientific databases\n- **Output**: Comprehensive analysis with key findings\n",{"i18n":38,"name":5,"license":41,"metadata":42,"description":7},{"zh":39},{"description":40},"使用FlowSearch搜索科。","MIT license",{"skill-author":43},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fsearch\u002Fscientific-literature-search",10,"https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw","https:\u002F\u002Fgithub.com\u002FInternScience","Shanghai Artificial Intelligence Laboratory","2026-03-20 17:06:10",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add InternScience\u002FDrClaw --skill scientific-literature-search",{"tool":58,"command":59},"bunx","bunx skills add InternScience\u002FDrClaw --skill scientific-literature-search",{"tool":61,"command":62},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill scientific-literature-search","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fscientific-literature-search\u002FSKILL.md",true,[],[67,102,124,142,157,170],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":81,"publishedAt":33},"01M6DMQESTSBW8VJW03QWHMT85","scientific-critical-thinking","Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fthinking\u002Fscientific-critical-thinking\u002FSKILL.md",[73,76],{"categoryNo":74,"categoryName":75,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101],"critical","thinking","scientific critical thinking","analysis","design","writing","review","risk","bias","teaching","peer review","Bash","quality","read","write","edit","use","evidence","peer","frameworks",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":33},"01M6DMQ77Z121463E049G2DG8K","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fthinking\u002Fscientific-brainstorming\u002FSKILL.md",[],[109,110,111,112,113,114,115,116,117,118,29,119,120,121,122,123,98],"brainstorming","scientific brainstorming","ideation","do","exploration","connections","research ideation","stage","generation","creative","sessions","planning","data","hypothesis","open",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":136,"publishedAt":33},"01M6DMPQJP8HVFV54V05D6K9HA","pubmed-article-search","Search PubMed database for scientific articles and publications to retrieve biomedical literature.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fpubmed-article-search\u002FSKILL.md",[130,133],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":134,"categoryName":135,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000036","内容创作者",[137,138,27,139,140,32,141],"pubmed article search","article","pubmed database","database","biomedical",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":149,"publishedAt":33},"01M6DMPK4H3JQ185M97BRB9KQJ","biomedical-web-search","Search biomedical literature and web content using Tavily search engine for research and clinical information.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fbiomedical-web-search\u002FSKILL.md",[148],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[150,141,151,27,152,153,29,154,155,32,156],"biomedical web search","web search","web","tavily","content","engine","clinical",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":164,"publishedAt":33},"01M6DMNNJH9V1S5PQHZRX0N934","uniprot-protein-retrieval","Retrieve protein sequences and functional information from UniProt database by protein name, enabling protein analysis and bioinformatics workflows.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Funiprot-protein-retrieval\u002FSKILL.md",[163],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[165,166,167,168,85,140,169],"uniprot protein retrieval","name","bioinformatics","uniprot database","workflows",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":33},"01M6DMNA7DTM1DQ1VQ3M23QCGT","string-ppi-enrichment","Analyze protein-protein interaction enrichment using STRING database to identify functional networks and pathway associations.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Fstring-ppi-enrichment\u002FSKILL.md",[],[177,178,179,180,181,140,182],"enrichment","string ppi enrichment","string database","networks","interaction","analyze"]