[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DMEFJS79SN08R6M3XPBQNR":3,"skill-same:01M6DMEFJS79SN08R6M3XPBQNR":58},{"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":18,"publishedAt":25,"linkOnly":26,"markdownContent":27,"markdownBody":28,"frontMatter":29,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":39,"repositoryHtmlUrl":40,"repositoryHomepageUrl":6,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":42,"updatedAt":43,"install":44,"resources":57},"01M6DMEFJS79SN08R6M3XPBQNR","measurement-error-analysis",null,"Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.","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\u002Fscience\u002Fphysics\u002Fmeasurement-error-analysis\u002FSKILL.md",[],[19,20,21,22,23,24],"analysis","measurement error analysis","data","analyze","quality","control","2026-11-10 10:12:30",false,"---\nname: measurement-error-analysis\ndescription: Analyze measurement errors, uncertainties, and statistical variations\n  in experimental data for quality control.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 分析测量误差与不确定性。\n---\n\n# Measurement Error Analysis\n\n## Usage\n\n```python\nimport asyncio\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\nimport numpy as np\n\nclass AnalysisClient:\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(url=self.server_url, headers={\"SCP-HUB-API-KEY\": self.api_key})\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:\n            return False\n\n    async def disconnect(self):\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\n    def parse_result(self, result):\n        try:\n            if hasattr(result, 'content') and result.content:\n                return json.loads(result.content[0].text)\n            return str(result)\n        except:\n            return {\"error\": \"parse error\"}\n\n## Initialize and use\nclient = AnalysisClient(\"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F26\u002FData_processing_and_statistical_analysis\", \"\u003Cyour-api-key>\")\nawait client.connect()\n\n# Analyze measurement errors\nmeasurements = [10.2, 10.5, 10.1, 10.4, 10.3]\nmean = np.mean(measurements)\nstd_dev = np.std(measurements, ddof=1)\nstd_error = std_dev \u002F np.sqrt(len(measurements))\n\nprint(f\"Mean: {mean:.2f}\")\nprint(f\"Standard deviation: {std_dev:.3f}\")\nprint(f\"Standard error: {std_error:.3f}\")\nprint(f\"Result: {mean:.2f} ± {std_error:.3f}\")\n\nawait client.disconnect()\n```\n\n### Use Cases\n- Experimental physics, quality control, calibration, uncertainty quantification\n","# Measurement Error Analysis\n\n## Usage\n\n```python\nimport asyncio\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\nimport numpy as np\n\nclass AnalysisClient:\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(url=self.server_url, headers={\"SCP-HUB-API-KEY\": self.api_key})\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:\n            return False\n\n    async def disconnect(self):\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\n    def parse_result(self, result):\n        try:\n            if hasattr(result, 'content') and result.content:\n                return json.loads(result.content[0].text)\n            return str(result)\n        except:\n            return {\"error\": \"parse error\"}\n\n## Initialize and use\nclient = AnalysisClient(\"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F26\u002FData_processing_and_statistical_analysis\", \"\u003Cyour-api-key>\")\nawait client.connect()\n\n# Analyze measurement errors\nmeasurements = [10.2, 10.5, 10.1, 10.4, 10.3]\nmean = np.mean(measurements)\nstd_dev = np.std(measurements, ddof=1)\nstd_error = std_dev \u002F np.sqrt(len(measurements))\n\nprint(f\"Mean: {mean:.2f}\")\nprint(f\"Standard deviation: {std_dev:.3f}\")\nprint(f\"Standard error: {std_error:.3f}\")\nprint(f\"Result: {mean:.2f} ± {std_error:.3f}\")\n\nawait client.disconnect()\n```\n\n### Use Cases\n- Experimental physics, quality control, calibration, uncertainty quantification\n",{"i18n":30,"name":5,"license":33,"metadata":34,"description":7},{"zh":31},{"description":32},"分析测量误差与不确定性。","MIT license",{"skill-author":35},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fscience\u002Fphysics\u002Fmeasurement-error-analysis",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":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add InternScience\u002FDrClaw --skill measurement-error-analysis",{"tool":50,"command":51},"bunx","bunx skills add InternScience\u002FDrClaw --skill measurement-error-analysis",{"tool":53,"command":54},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill measurement-error-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fphysics\u002Fmeasurement-error-analysis\u002FSKILL.md",true,[],[59,94,116,134,152,167],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":75,"publishedAt":25},"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",[65,70],{"categoryNo":66,"categoryName":67,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":71,"categoryName":72,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[76,77,78,79,80,81,82,19,83,84,23,85,86,87,88,89,90,91,92,93],"critical","scientific critical thinking","thinking","design","writing","review","risk","peer review","Bash","read","write","edit","use","evidence","peer","frameworks","teaching","bias",{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":25},"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",[],[101,102,103,104,105,106,107,108,109,110,111,112,113,21,114,115,88],"brainstorming","scientific brainstorming","research ideation","stage","connections","generation","exploration","do","planning","creative","ideation","research","sessions","hypothesis","open",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":128,"publishedAt":25},"01M6DMPV4AMCCTWARGPSFHDWJB","scientific-literature-search","Search scientific literature and research papers using FlowSearch to find relevant academic articles and publications.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fscientific-literature-search\u002FSKILL.md",[122,125],{"categoryNo":123,"categoryName":124,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":126,"categoryName":127,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000023","Skill与智能体开发",[129,130,112,131,132,133],"search","scientific literature search","find","Academic","搜索",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":146,"publishedAt":25},"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",[140,143],{"categoryNo":141,"categoryName":142,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":144,"categoryName":145,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000036","内容创作者",[147,148,129,133,149,150,151],"pubmed article search","article","database","pubmed database","biomedical",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":159,"publishedAt":25},"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",[158],{"categoryNo":123,"categoryName":124,"taxonomyType":68,"taxonomyTypeDesc":69},[160,151,161,129,162,163,112,164,165,133,166],"biomedical web search","web search","web","tavily","content","engine","clinical",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":25},"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",[173],{"categoryNo":126,"categoryName":127,"taxonomyType":68,"taxonomyTypeDesc":69},[175,149,19,176,177,178,179],"uniprot protein retrieval","uniprot database","bioinformatics","name","workflows"]