[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFFM7AQ9593CDGGY3WJJFM":3,"skill-same:01M6XFFM7AQ9593CDGGY3WJJFM":77},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6XFFM7AQ9593CDGGY3WJJFM","admet-reasoning",null,"Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-reasoning\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"reasoning","admet reasoning","synthetic","interpretation","toxicity","ADMET","2026","with","2025","pathways","drug-discovery","pandas","structural","numpy","maps","park","discovery","rdkit","analysis","pypi","2026-11-10 10:12:30",false,"---\nname: admet-reasoning\ndescription: Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).\ncategory: chemistry\nlicense: MIT\nmetadata:\n    skill-author: Synthetic Sciences\nversion: 1.0.0\ntags: [drug-discovery, ADMET, toxicity, interpretation, reasoning]\ndependencies: [\"rdkit-pypi\", \"numpy\", \"pandas\"]\n---\n\n# ADMET Reasoning\n\n## Overview\n\nStandard ADMET prediction tools output scores (e.g., \"hERG = 0.85\") without explaining why. This skill adds mechanistic reasoning — mapping each ADMET liability to its structural cause, the biological mechanism it affects, and a suggested structural fix.\n\nBased on:\n- **CoTox** (Park et al., 2025): Chain-of-thought toxicity reasoning with structural + biological context improved F1 from 0.37 to 0.66\n- **DrugR** (Liu et al., 2026): Explicit liability reasoning before optimization improved scores 18×\n\n## When to Use This Skill\n\n- **After ADMET prediction**: Interpret flagged liabilities with structural explanations\n- **Lead optimization planning**: Understand which structural features to modify and why\n- **Toxicity reports**: Generate interpretable toxicity assessments for medicinal chemistry teams\n- **Design review**: Evaluate whether proposed modifications address the right liabilities\n\n**Do NOT use this skill for:**\n- Raw ADMET score computation (use `admet-prediction` instead)\n- Molecular optimization (use `molecular-optimization` instead)\n\n### Related Skills\n- **admet-prediction**: Compute ADMET properties (run this first)\n- **molecular-optimization**: Iterative optimization using liability analysis\n- **rdkit**: Core molecular operations\n\n## Installation\n\n### Required dependencies\n\n```bash\npip install rdkit-pypi numpy pandas\n```\n\n## Core Workflows\n\n### 1. Full Liability Report\n\nGenerate interpretable ADMET analysis for a molecule:\n\n```bash\npython scripts\u002Freason_admet.py --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" --output report.json\n```\n\n### 2. Batch Liability Analysis\n\n```bash\npython scripts\u002Freason_admet.py --input compounds.csv --output liability_report.csv\n```\n\n### 3. Targeted Toxicity Reasoning\n\nFocus on specific endpoints:\n\n```bash\npython scripts\u002Freason_admet.py --smiles \"CCN1CCCC1\" --endpoints hERG,DILI,CYP --output tox_report.json\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `reason_admet.py` | Full ADMET reasoning with structural explanations | JSON report with liabilities, causes, mechanisms, fixes |\n","# ADMET Reasoning\n\n## Overview\n\nStandard ADMET prediction tools output scores (e.g., \"hERG = 0.85\") without explaining why. This skill adds mechanistic reasoning — mapping each ADMET liability to its structural cause, the biological mechanism it affects, and a suggested structural fix.\n\nBased on:\n- **CoTox** (Park et al., 2025): Chain-of-thought toxicity reasoning with structural + biological context improved F1 from 0.37 to 0.66\n- **DrugR** (Liu et al., 2026): Explicit liability reasoning before optimization improved scores 18×\n\n## When to Use This Skill\n\n- **After ADMET prediction**: Interpret flagged liabilities with structural explanations\n- **Lead optimization planning**: Understand which structural features to modify and why\n- **Toxicity reports**: Generate interpretable toxicity assessments for medicinal chemistry teams\n- **Design review**: Evaluate whether proposed modifications address the right liabilities\n\n**Do NOT use this skill for:**\n- Raw ADMET score computation (use `admet-prediction` instead)\n- Molecular optimization (use `molecular-optimization` instead)\n\n### Related Skills\n- **admet-prediction**: Compute ADMET properties (run this first)\n- **molecular-optimization**: Iterative optimization using liability analysis\n- **rdkit**: Core molecular operations\n\n## Installation\n\n### Required dependencies\n\n```bash\npip install rdkit-pypi numpy pandas\n```\n\n## Core Workflows\n\n### 1. Full Liability Report\n\nGenerate interpretable ADMET analysis for a molecule:\n\n```bash\npython scripts\u002Freason_admet.py --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" --output report.json\n```\n\n### 2. Batch Liability Analysis\n\n```bash\npython scripts\u002Freason_admet.py --input compounds.csv --output liability_report.csv\n```\n\n### 3. Targeted Toxicity Reasoning\n\nFocus on specific endpoints:\n\n```bash\npython scripts\u002Freason_admet.py --smiles \"CCN1CCCC1\" --endpoints hERG,DILI,CYP --output tox_report.json\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `reason_admet.py` | Full ADMET reasoning with structural explanations | JSON report with liabilities, causes, mechanisms, fixes |\n",{"name":5,"tags":44,"license":15,"version":45,"category":46,"metadata":47,"description":7,"dependencies":49},[29,24,23,22,19],"1.0.0","chemistry",{"skill-author":48},"Synthetic Sciences",[50,32,30],"rdkit-pypi","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-reasoning","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add fadrienne\u002FMyGitHub --skill admet-reasoning",{"tool":63,"command":64},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill admet-reasoning",{"tool":66,"command":67},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill admet-reasoning","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-reasoning\u002FSKILL.md",true,[71],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"scripts\u002Freason_admet.py","SCRIPT","脚本",11447,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-reasoning\u002Fscripts\u002Freason_admet.py",[78,109,135,169,196,223],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":89,"publishedAt":39},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[84],{"categoryNo":85,"categoryName":86,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[90,91,92,93,94,95,96,97,98,99,100,37,101,102,103,104,105,106,107,108],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":118,"publishedAt":39},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[115],{"categoryNo":116,"categoryName":117,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000008","测试与质量",[37,119,90,120,121,122,98,123,124,125,126,127,128,26,129,130,131,132,133,134],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","test","research","results","statsmodels","models","help",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":150,"publishedAt":39},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[141,144,145],{"categoryNo":142,"categoryName":143,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":85,"categoryName":86,"taxonomyType":87,"taxonomyTypeDesc":88},{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[151,152,153,154,155,156,157,158,159,160,99,161,162,163,164,165,166,167,124,168],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"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":179,"publishedAt":39},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[175,178],{"categoryNo":176,"categoryName":177,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":85,"categoryName":86,"taxonomyType":87,"taxonomyTypeDesc":88},[180,152,181,182,183,184,185,186,187,159,98,188,189,190,191,192,193,194,126,195],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":208,"publishedAt":39},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[202,205],{"categoryNo":203,"categoryName":204,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":206,"categoryName":207,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000030","开发工程师",[209,210,130,211,212,213,214,97,215,98,99,37,216,217,218,219,220,221,167,222],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":227,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":236,"publishedAt":39},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[230,233,234,235],{"categoryNo":231,"categoryName":232,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":142,"categoryName":143,"taxonomyType":87,"taxonomyTypeDesc":88},{"categoryNo":85,"categoryName":86,"taxonomyType":87,"taxonomyTypeDesc":88},{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},[151,237,238,239,240,241,180,242,243,98,99,37,244,245,246,247,248,249,152,167],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]