[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7EDQSMNFFVR85RC56HTP482":3,"skill-same:01M7EDQSMNFFVR85RC56HTP482":48},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":14,"repositoryHtmlUrl":30,"repositoryHomepageUrl":6,"ownerHtmlUrl":31,"ownerBlogUrl":32,"ownerDescription":6,"updatedAt":33,"install":34,"resources":47},"01M7EDQSMNFFVR85RC56HTP482","molclaw-drug-likeness",null,"Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).","sherkevin\u002Fbenchmark","sherkevin","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F78121652?v=4","01M7EDKCMQC9GKR7A7J45YNK7F","Shervin","01M7EDKCNA0A6ZHHG5XJ3VK1E5",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-drug-likeness\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: molclaw-drug-likeness\ndescription: Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format). \nlicense: MIT license\nmetadata:\n    skill-author: PJLab\n---\n\n# Molecular Drug-likeness Metrics Calculation\n\nNote: \n- Local files are not directly accessible by the server. Please upload them to the server using `molclaw-file-transfer` before execution. \n- For PDB file inputs, it is recommended to preprocess them using `molclaw-pdbfixer` before execution.\n- Please refer to skill `molclaw-scp-server` to complete tool invocation.\n\nThe description of tool *calculate_mol_drug_chemistry*.\n\n```tex\nCompute key drug-likeness metrics 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        --qed (float): Quantitative Estimate of Drug-likeness (QED) score\n        --lipinski_rule_of_5_violations (int): Number of violations of Lipinski's Rule of Five\n```\n\nHow to use tool *calculate_mol_drug_chemistry* :\n\n```python\nresponse = await client.session.call_tool(\n    \"calculate_mol_drug_chemistry\",\n    arguments={\n        \"smiles_list\": smiles_list\n    }\n)\nresult = client.parse_result(response)\ndruglikeness_metrics = result[\"metrics\"]\n```\n","# Molecular Drug-likeness Metrics Calculation\n\nNote: \n- Local files are not directly accessible by the server. Please upload them to the server using `molclaw-file-transfer` before execution. \n- For PDB file inputs, it is recommended to preprocess them using `molclaw-pdbfixer` before execution.\n- Please refer to skill `molclaw-scp-server` to complete tool invocation.\n\nThe description of tool *calculate_mol_drug_chemistry*.\n\n```tex\nCompute key drug-likeness metrics 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        --qed (float): Quantitative Estimate of Drug-likeness (QED) score\n        --lipinski_rule_of_5_violations (int): Number of violations of Lipinski's Rule of Five\n```\n\nHow to use tool *calculate_mol_drug_chemistry* :\n\n```python\nresponse = await client.session.call_tool(\n    \"calculate_mol_drug_chemistry\",\n    arguments={\n        \"smiles_list\": smiles_list\n    }\n)\nresult = client.parse_result(response)\ndruglikeness_metrics = result[\"metrics\"]\n```\n",{"name":5,"license":24,"metadata":25,"description":7},"MIT license",{"skill-author":26},"PJLab","codex\u002Fprogress-20260924","c8ee42aa1da4aadc7e9b7e39b01c27ccdeea5cc9","src\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-drug-likeness","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark","https:\u002F\u002Fgithub.com\u002Fsherkevin","https:\u002F\u002Fwww.cnblogs.com\u002Fsherkevin","2026-10-09 16:43:49",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add sherkevin\u002Fbenchmark --skill molclaw-drug-likeness",{"tool":40,"command":41},"bunx","bunx skills add sherkevin\u002Fbenchmark --skill molclaw-drug-likeness",{"tool":43,"command":44},"pnpm","pnpm dlx skills add sherkevin\u002Fbenchmark --skill molclaw-drug-likeness","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-drug-likeness\u002FSKILL.md",true,[],[49,56,63,70,77,84],{"skillNo":50,"name":51,"nameZh":6,"description":52,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":53,"categories":54,"keywords":55,"publishedAt":19},"01M7EDY1139HR68WB7A3B7S4SC","molclaw-smiles-valid-check","Check if the input molecule SMILES string is valid.","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-smiles-valid-check\u002FSKILL.md",[],[],{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":62,"publishedAt":19},"01M7EDXNFE3A53X9RJHZ7MEVR1","molclaw-skill-template-writer","Formats extracted execution patterns into standard MolClaw skill documents. Accepts structured input from the Skill Crystallization Meta-Workflow (L2-12) and outputs a properly formatted L1 or L2 skill document conforming to MolClaw conventions. This skill ensures that auto-generated skills are structurally identical to expert-curated skills, enabling seamless integration into the skill matching and loading pipeline.","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-skill-template-writer\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":19},"01M7EDXJSJESBRR1DQYRCS4DTW","molclaw-sequence-valid-check","Check if the input protein sequence is valid.","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-sequence-valid-check\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7EDXF2M82DJKKFBTPV1FJFN","molclaw-scp-server","All tools utilized within molclaw skills connect via the MCP protocol. This skill serves as a unified guide for using the MCP Server. This skill must be loaded to create the MCP server before invoking any tools.","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-scp-server\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7EDXBSW1SMA87ZMYJVGD1NW","molclaw-run-bioemu","Run BioEmu sequence sampling and extract ensemble structures for downstream conformation analysis.","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-run-bioemu\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M7EDX8VMARAB5WTQ1DDVMY1P","molclaw-rgroup-sampling","Generate new molecules sampling from the input scaffold.","https:\u002F\u002Fgithub.com\u002Fsherkevin\u002Fbenchmark\u002Fblob\u002Fcodex\u002Fprogress-20260924\u002Fsrc\u002Fmolbench_repo\u002Fskills\u002FL1_tools\u002Fmolclaw-rgroup-sampling\u002FSKILL.md",[],[]]