[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DJT6XRM8VG4X57ABSAFQHT":3,"skill-same:01M6DJT6XRM8VG4X57ABSAFQHT":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":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M6DJT6XRM8VG4X57ABSAFQHT","drugsda-mol-properties",null,"Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological structures, charge distribution, and custom complexity metrics, respectively.","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\u002Fagent_hub\u002Ftemplates\u002Fpharmacy\u002Fskills\u002Fdrugsda-mol-properties\u002FSKILL.md",[],[19,20,21,22,23,24,25,26],"properties","drugsda mol properties","metrics","calculate","complexity","molecular","distribution","basic","2026-11-10 10:12:30",false,"---\nname: drugsda-mol-properties\ndescription: Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological structures, charge distribution, and custom complexity metrics, respectively. \nlicense: MIT license\nmetadata:\n    skill-author: PJLab\n---\n\n# Molecular Properties Calculation\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. Tool Description\n\nTool 1: *calculate_mol_basic_info*\n\n```tex\nCompute a set of basic molecular properties for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --molecular_formula (str): Molecular formula, e.g. \"C9H11NO3\"\n        --exact_molecular_weight (float): Exact molecular weight\n        --molecular_weight (float): Average molecular weight\n        --num_heavy_atoms (int): Number of heavy atoms\n        --num_atoms (int): Number of total atoms\n        --num_bonds (int): Number of bonds\n        --num_valence_electrons (int): Number of valence electrons\n        --formal_charge (int): Number of formal charge\n```\n\nTool 2: calculate_mol_hydrophobicity\n\n```tex\nCompute hydrophobicity-related molecular descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --logp (float): The octanol-water partition coefficient (logP)\n        --molar_refractivity (float): Molar refractivity\n```\n\nTool 3: calculate_mol_hbond\n\n```tex\nCompute hydrogen bonding-related properties for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing several feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --num_h_donors (int): Number of hydrogen bond donors\n        --num_h_acceptors (int): Number of hydrogen bond acceptors\n```\n\nTool 4: calculate_mol_structure_complexity\n\n```tex\nCompute a set of molecular complexity descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --num_rotatable_bonds (int): Number of rotatable bonds\n        --num_rings (int): Number of total rings\n        --num_aromatic_rings (int): Number of aromatic rings\n        --num_aliphatic_rings (int): Number of aliphatic rings\n        --num_saturated_rings (int): Number of saturated rings\n        --num_heteroatoms (int): Number of heteroatoms\n        --fraction_csp3 (float): The fraction of sp³-hybridized carbon atoms (Fsp³)\n        --num_bridgehead_atoms (int): Number of bridgehead atoms\n```\n\nTool 5: calculate_mol_topology\n\n```tex\nCompute a set of topological descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing several feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --tpsa (float): Topological polar surface area\n        --chi0v (float): Non-valence molecular connectivity index\n        --chi1v (float): Non-valence molecular connectivity index\n        --chi2v (float): Non-valence molecular connectivity index\n        --chi3v (float): Non-valence molecular connectivity index\n        --chi4v (float): Non-valence molecular connectivity index\n        --chi0n (float): Non-valence molecular connectivity index\n        --chi1n (float): Non-valence molecular connectivity index\n        --chi2n (float): Non-valence molecular connectivity index\n        --chi3n (float): Non-valence molecular connectivity index\n        --chi4n (float): Non-valence molecular connectivity index\n        --hall_kier_alpha (float): Hall–Kier alpha value\n        --kappa1 (float): Kappa shape index\n        --kappa2 (float): Kappa shape index\n        --kappa3 (float): Kappa shape index\n```\n\nTool 6: calculate_mol_charge\n\n```tex\nCompute Gasteiger partial charges and formal charge for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing several feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --min_gasteiger_charge (float): Minimum of Gasteiger charges\n        --max_gasteiger_charge (float): Maximum of Gasteiger charges\n        --avg_gasteiger_charge (float): Average of Gasteiger charges\n        --gasteiger_charge_range (float): Range of Gasteiger charges\n        --formal_charge (int): Formal charge\n```\n\nTool 7: calculate_mol_complexity\n\n```tex\nCompute custom molecular complexity-related descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --molecular_complexity (int): Molecular complexity\n        --aromatic_proportion (float): Aromatic proportion \n        --asphericity (float): Asphericity\n```\n\n### 3. Example Code\n\nHow to use these tools:\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\n## The tool can be replaced with another based on actual requirements.\nresponse = await client.session.call_tool(\n    \"calculate_mol_basic_info\",\t\t\t\n    arguments={\n        \"smiles_list\": smiles_list\n    }\n)\nresult = client.parse_result(response)\nmetrics = result[\"metrics\"]\n\nawait client.disconnect() \n```\n\n","# Molecular Properties Calculation\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. Tool Description\n\nTool 1: *calculate_mol_basic_info*\n\n```tex\nCompute a set of basic molecular properties for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --molecular_formula (str): Molecular formula, e.g. \"C9H11NO3\"\n        --exact_molecular_weight (float): Exact molecular weight\n        --molecular_weight (float): Average molecular weight\n        --num_heavy_atoms (int): Number of heavy atoms\n        --num_atoms (int): Number of total atoms\n        --num_bonds (int): Number of bonds\n        --num_valence_electrons (int): Number of valence electrons\n        --formal_charge (int): Number of formal charge\n```\n\nTool 2: calculate_mol_hydrophobicity\n\n```tex\nCompute hydrophobicity-related molecular descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --logp (float): The octanol-water partition coefficient (logP)\n        --molar_refractivity (float): Molar refractivity\n```\n\nTool 3: calculate_mol_hbond\n\n```tex\nCompute hydrogen bonding-related properties for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing several feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --num_h_donors (int): Number of hydrogen bond donors\n        --num_h_acceptors (int): Number of hydrogen bond acceptors\n```\n\nTool 4: calculate_mol_structure_complexity\n\n```tex\nCompute a set of molecular complexity descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --num_rotatable_bonds (int): Number of rotatable bonds\n        --num_rings (int): Number of total rings\n        --num_aromatic_rings (int): Number of aromatic rings\n        --num_aliphatic_rings (int): Number of aliphatic rings\n        --num_saturated_rings (int): Number of saturated rings\n        --num_heteroatoms (int): Number of heteroatoms\n        --fraction_csp3 (float): The fraction of sp³-hybridized carbon atoms (Fsp³)\n        --num_bridgehead_atoms (int): Number of bridgehead atoms\n```\n\nTool 5: calculate_mol_topology\n\n```tex\nCompute a set of topological descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing several feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --tpsa (float): Topological polar surface area\n        --chi0v (float): Non-valence molecular connectivity index\n        --chi1v (float): Non-valence molecular connectivity index\n        --chi2v (float): Non-valence molecular connectivity index\n        --chi3v (float): Non-valence molecular connectivity index\n        --chi4v (float): Non-valence molecular connectivity index\n        --chi0n (float): Non-valence molecular connectivity index\n        --chi1n (float): Non-valence molecular connectivity index\n        --chi2n (float): Non-valence molecular connectivity index\n        --chi3n (float): Non-valence molecular connectivity index\n        --chi4n (float): Non-valence molecular connectivity index\n        --hall_kier_alpha (float): Hall–Kier alpha value\n        --kappa1 (float): Kappa shape index\n        --kappa2 (float): Kappa shape index\n        --kappa3 (float): Kappa shape index\n```\n\nTool 6: calculate_mol_charge\n\n```tex\nCompute Gasteiger partial charges and formal charge for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing several feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --min_gasteiger_charge (float): Minimum of Gasteiger charges\n        --max_gasteiger_charge (float): Maximum of Gasteiger charges\n        --avg_gasteiger_charge (float): Average of Gasteiger charges\n        --gasteiger_charge_range (float): Range of Gasteiger charges\n        --formal_charge (int): Formal charge\n```\n\nTool 7: calculate_mol_complexity\n\n```tex\nCompute custom molecular complexity-related descriptors for each SMILES.\nArgs:\n    smiles_list (List[str]): List of input SMILES strings, (e.g., [\"N[C@@H](Cc1ccc(O)cc1)C(=O)O\", \"CC(C)C1=CC=CC=C1\"])\nReturn:\n    status (str): success\u002Ferror\n    msg (str): message\n    metrics (List[dict]): List of dict, each containing feature keys.\n        --smiles (str): A SMILES string of smiles_list\n        --molecular_complexity (int): Molecular complexity\n        --aromatic_proportion (float): Aromatic proportion \n        --asphericity (float): Asphericity\n```\n\n### 3. Example Code\n\nHow to use these tools:\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\n## The tool can be replaced with another based on actual requirements.\nresponse = await client.session.call_tool(\n    \"calculate_mol_basic_info\",\t\t\t\n    arguments={\n        \"smiles_list\": smiles_list\n    }\n)\nresult = client.parse_result(response)\nmetrics = result[\"metrics\"]\n\nawait client.disconnect() \n```\n\n",{"name":5,"license":32,"metadata":33,"description":7},"MIT license",{"skill-author":34},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Fagent_hub\u002Ftemplates\u002Fpharmacy\u002Fskills\u002Fdrugsda-mol-properties",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-mol-properties",{"tool":49,"command":50},"bunx","bunx skills add InternScience\u002FDrClaw --skill drugsda-mol-properties",{"tool":52,"command":53},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill drugsda-mol-properties","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Fagent_hub\u002Ftemplates\u002Fpharmacy\u002Fskills\u002Fdrugsda-mol-properties\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":27},"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":27},"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":27},"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":27},"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":27},"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":27},"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"]