[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DKZJB5SP3Z2YFSXGYH4TAM":3,"skill-same:01M6DKZJB5SP3Z2YFSXGYH4TAM":57},{"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":24,"linkOnly":25,"markdownContent":26,"markdownBody":27,"frontMatter":28,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M6DKZJB5SP3Z2YFSXGYH4TAM","drugsda-mol2mol-sampling",null,"Generate new molecules sampling from the input molecule.","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\u002Fdrug\u002Fdrugsda-mol2mol-sampling\u002FSKILL.md",[],[19,20,21,22,23],"sampling","drugsda mol2mol sampling","generate","生成","input","2026-11-10 10:12:30",false,"---\nname: drugsda-mol2mol-sampling\ndescription: Generate new molecules sampling from the input molecule.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 基于输入分子生成新分子。\n---\n\n# Molecule Generation\n\n## Usage\n\n### 1. MCP Server Definition\n\n```python\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\n\nclass DrugSDAClient:    \n    def __init__(self, server_url: str):\n        self.server_url = server_url\n        self.session = None\n        \n    async def connect(self):\n        print(f\"server url: {self.server_url}\")\n        try:\n            self.transport = streamablehttp_client(\n                url=self.server_url,\n                headers={\"SCP-HUB-API-KEY\": \"sk-a0033dde-b3cd-413b-adbe-980bc78d6126\"}\n            )\n            self.read, self.write, self.get_session_id = await self.transport.__aenter__()\n            \n            self.session_ctx = ClientSession(self.read, self.write)\n            self.session = await self.session_ctx.__aenter__()\n\n            await self.session.initialize()\n            session_id = self.get_session_id()\n            \n            print(f\"✓ connect success\")\n            return True\n            \n        except Exception as e:\n            print(f\"✗ connect failure: {e}\")\n            import traceback\n            traceback.print_exc()\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            print(\"✓ already disconnect\")\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. Mol2Mol Sampling\n\nThe description of tool *reinvent_mol2mol_sampling*.\n\n```tex\nGenerate new molecules sampling from the input molecule using different priors ('similarity': broad exploration, 'medium_similarity': balanced exploration, 'high_similarity': conservative optimization, 'scaffold': strict scaffold preservation, 'scaffold_generic': generic scaffold preservation, 'mmp': MMP-style local modifications).\nArgs:\n    smiles (str): Input SMILES string\n    n (int): Number of molecules for sampling\n    min_similarity (float): Minimum similarity threshold, default is 0.6\n    prior_type (str): Prior type for generation, options: ['scaffold_generic', 'scaffold', 'mmp', 'similarity', 'high_similarity', 'medium_similarity'], default is 'similarity'\n    lipinski (bool): Whether to apply Lipinski's rule of five filtering, default is True\n    filter_preset (str): Filter preset, options: ['none', 'minimal', 'default', 'strict'], default is 'default'\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    save_smiles_file (str): Path to the saved SMILES file\n    output_smiles_list (List[str]): List of generated SMILES strings\n```\n\nHow to use tool *reinvent_denovo_sampling* :\n\n```python\nclient = DrugSDAClient(\"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F2\u002FDrugSDA-Tool\")\nif not await client.connect():\n    print(\"connection failed\")\n    return\n\nresponse = await client.session.call_tool(\n    \"reinvent_mol2mol_sampling\",\n    arguments={\n        \"smiles\": smiles,\n        \"n\": n,\n        \"min_similarity\": min_similarity,\n        \"prior_type\": prior_type,\n        \"lipinski\": True,\n        \"filter_preset\": filter_type\n    }\n)\nresult = client.parse_result(response)\noutput_smiles_list = result[\"output_smiles_list\"]\n\nawait client.disconnect() \n```\n\n","# Molecule Generation\n\n## Usage\n\n### 1. MCP Server Definition\n\n```python\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\n\nclass DrugSDAClient:    \n    def __init__(self, server_url: str):\n        self.server_url = server_url\n        self.session = None\n        \n    async def connect(self):\n        print(f\"server url: {self.server_url}\")\n        try:\n            self.transport = streamablehttp_client(\n                url=self.server_url,\n                headers={\"SCP-HUB-API-KEY\": \"sk-a0033dde-b3cd-413b-adbe-980bc78d6126\"}\n            )\n            self.read, self.write, self.get_session_id = await self.transport.__aenter__()\n            \n            self.session_ctx = ClientSession(self.read, self.write)\n            self.session = await self.session_ctx.__aenter__()\n\n            await self.session.initialize()\n            session_id = self.get_session_id()\n            \n            print(f\"✓ connect success\")\n            return True\n            \n        except Exception as e:\n            print(f\"✗ connect failure: {e}\")\n            import traceback\n            traceback.print_exc()\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            print(\"✓ already disconnect\")\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. Mol2Mol Sampling\n\nThe description of tool *reinvent_mol2mol_sampling*.\n\n```tex\nGenerate new molecules sampling from the input molecule using different priors ('similarity': broad exploration, 'medium_similarity': balanced exploration, 'high_similarity': conservative optimization, 'scaffold': strict scaffold preservation, 'scaffold_generic': generic scaffold preservation, 'mmp': MMP-style local modifications).\nArgs:\n    smiles (str): Input SMILES string\n    n (int): Number of molecules for sampling\n    min_similarity (float): Minimum similarity threshold, default is 0.6\n    prior_type (str): Prior type for generation, options: ['scaffold_generic', 'scaffold', 'mmp', 'similarity', 'high_similarity', 'medium_similarity'], default is 'similarity'\n    lipinski (bool): Whether to apply Lipinski's rule of five filtering, default is True\n    filter_preset (str): Filter preset, options: ['none', 'minimal', 'default', 'strict'], default is 'default'\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    save_smiles_file (str): Path to the saved SMILES file\n    output_smiles_list (List[str]): List of generated SMILES strings\n```\n\nHow to use tool *reinvent_denovo_sampling* :\n\n```python\nclient = DrugSDAClient(\"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F2\u002FDrugSDA-Tool\")\nif not await client.connect():\n    print(\"connection failed\")\n    return\n\nresponse = await client.session.call_tool(\n    \"reinvent_mol2mol_sampling\",\n    arguments={\n        \"smiles\": smiles,\n        \"n\": n,\n        \"min_similarity\": min_similarity,\n        \"prior_type\": prior_type,\n        \"lipinski\": True,\n        \"filter_preset\": filter_type\n    }\n)\nresult = client.parse_result(response)\noutput_smiles_list = result[\"output_smiles_list\"]\n\nawait client.disconnect() \n```\n\n",{"i18n":29,"name":5,"license":32,"metadata":33,"description":7},{"zh":30},{"description":31},"基于输入分子生成新分子。","MIT license",{"skill-author":34},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fscience\u002Fdrug\u002Fdrugsda-mol2mol-sampling",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":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add InternScience\u002FDrClaw --skill drugsda-mol2mol-sampling",{"tool":49,"command":50},"bunx","bunx skills add InternScience\u002FDrClaw --skill drugsda-mol2mol-sampling",{"tool":52,"command":53},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill drugsda-mol2mol-sampling","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fdrug\u002Fdrugsda-mol2mol-sampling\u002FSKILL.md",true,[],[58,95,118,136,154,169],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":74,"publishedAt":24},"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",[64,69],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":70,"categoryName":71,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94],"critical","scientific critical thinking","thinking","design","writing","review","risk","analysis","peer review","Bash","quality","read","write","edit","use","evidence","peer","frameworks","teaching","bias",{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":24},"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",[],[102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,89],"brainstorming","scientific brainstorming","research ideation","stage","connections","generation","exploration","do","planning","creative","ideation","research","sessions","data","hypothesis","open",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":130,"publishedAt":24},"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",[124,127],{"categoryNo":125,"categoryName":126,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":128,"categoryName":129,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000023","Skill与智能体开发",[131,132,113,133,134,135],"search","scientific literature search","find","Academic","搜索",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":148,"publishedAt":24},"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",[142,145],{"categoryNo":143,"categoryName":144,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":146,"categoryName":147,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000036","内容创作者",[149,150,131,135,151,152,153],"pubmed article search","article","database","pubmed database","biomedical",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":161,"publishedAt":24},"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",[160],{"categoryNo":125,"categoryName":126,"taxonomyType":67,"taxonomyTypeDesc":68},[162,153,163,131,164,165,113,166,167,135,168],"biomedical web search","web search","web","tavily","content","engine","clinical",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":176,"publishedAt":24},"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",[175],{"categoryNo":128,"categoryName":129,"taxonomyType":67,"taxonomyTypeDesc":68},[177,151,82,178,179,180,181],"uniprot protein retrieval","uniprot database","bioinformatics","name","workflows"]