[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFJ4J5PB8EYTP23B54YF4M":3,"skill-same:01M6XFJ4J5PB8EYTP23B54YF4M":79},{"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":53,"commitSha":54,"skillDirectory":55,"forksCount":14,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6XFJ4J5PB8EYTP23B54YF4M","molecular-rag",null,"Retrieve structurally similar compounds with known properties from ChEMBL\u002FZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).","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\u002Fmolecular-rag\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"molecular rag","molecular","rag","chembl","analog-search","requests","grounding","with","2025","retrieval","predictions","drug-discovery","synthetic","properties","discovery","rdkit","Optimization","pypi","pandas","search","2026-11-10 10:12:30",false,"---\nname: molecular-rag\ndescription: Retrieve structurally similar compounds with known properties from ChEMBL\u002FZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).\ncategory: chemistry\nlicense: MIT\nmetadata:\n    skill-author: Synthetic Sciences\nversion: 1.0.0\ntags: [drug-discovery, RAG, retrieval, ChEMBL, analog-search, grounding]\ndependencies: [\"rdkit-pypi\", \"requests\", \"pandas\"]\n---\n\n# Molecular RAG (Retrieval-Augmented Generation)\n\n## Overview\n\nLLMs hallucinate molecular properties. This skill grounds predictions by retrieving structurally similar compounds with experimentally measured properties from ChEMBL and ZINC. When the agent says \"this compound should have good hERG safety,\" it can now check what happened with similar compounds in real assays.\n\nBased on:\n- **MolRAG** (Xian et al., 2025, ACL): RAG for molecular property prediction — retrieves similar compounds to ground LLM predictions\n\n## When to Use This Skill\n\n- **Before property prediction**: Retrieve analogs with known properties for context\n- **Lead optimization**: Find what modifications worked for similar scaffolds\n- **Novelty assessment**: Check if your generated molecule is truly novel or already known\n- **SAR grounding**: Ground structure-activity reasoning in experimental data\n\n**Do NOT use this skill for:**\n- Bulk database queries (use `chembl-database` or `pubchem-database` directly)\n- De novo generation (use `denovo-design`)\n\n### Related Skills\n- **chembl-database**: Direct ChEMBL API access\n- **pubchem-database**: PubChem compound lookup\n- **zinc-database**: ZINC compound search\n- **admet-reasoning**: Interpret properties of retrieved analogs\n\n## Installation\n\n```bash\npip install rdkit-pypi requests pandas\n```\n\n## Core Workflows\n\n### 1. Find Similar Compounds with Known Properties\n\n```bash\npython scripts\u002Fretrieve_analogs.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --similarity-threshold 0.6 \\\n    --max-results 20 \\\n    --output analogs.json\n```\n\n### 2. Target-Specific Analog Search\n\n```bash\npython scripts\u002Fretrieve_analogs.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --target CHEMBL25 \\\n    --output target_analogs.json\n```\n\n### 3. SAR Context for Optimization\n\n```bash\npython scripts\u002Fretrieve_analogs.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --include-activities \\\n    --output sar_context.json\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `retrieve_analogs.py` | Find similar compounds with experimental data | JSON with analogs, similarities, bioactivities |\n","# Molecular RAG (Retrieval-Augmented Generation)\n\n## Overview\n\nLLMs hallucinate molecular properties. This skill grounds predictions by retrieving structurally similar compounds with experimentally measured properties from ChEMBL and ZINC. When the agent says \"this compound should have good hERG safety,\" it can now check what happened with similar compounds in real assays.\n\nBased on:\n- **MolRAG** (Xian et al., 2025, ACL): RAG for molecular property prediction — retrieves similar compounds to ground LLM predictions\n\n## When to Use This Skill\n\n- **Before property prediction**: Retrieve analogs with known properties for context\n- **Lead optimization**: Find what modifications worked for similar scaffolds\n- **Novelty assessment**: Check if your generated molecule is truly novel or already known\n- **SAR grounding**: Ground structure-activity reasoning in experimental data\n\n**Do NOT use this skill for:**\n- Bulk database queries (use `chembl-database` or `pubchem-database` directly)\n- De novo generation (use `denovo-design`)\n\n### Related Skills\n- **chembl-database**: Direct ChEMBL API access\n- **pubchem-database**: PubChem compound lookup\n- **zinc-database**: ZINC compound search\n- **admet-reasoning**: Interpret properties of retrieved analogs\n\n## Installation\n\n```bash\npip install rdkit-pypi requests pandas\n```\n\n## Core Workflows\n\n### 1. Find Similar Compounds with Known Properties\n\n```bash\npython scripts\u002Fretrieve_analogs.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --similarity-threshold 0.6 \\\n    --max-results 20 \\\n    --output analogs.json\n```\n\n### 2. Target-Specific Analog Search\n\n```bash\npython scripts\u002Fretrieve_analogs.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --target CHEMBL25 \\\n    --output target_analogs.json\n```\n\n### 3. SAR Context for Optimization\n\n```bash\npython scripts\u002Fretrieve_analogs.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --include-activities \\\n    --output sar_context.json\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `retrieve_analogs.py` | Find similar compounds with experimental data | JSON with analogs, similarities, bioactivities |\n",{"name":5,"tags":44,"license":15,"version":47,"category":48,"metadata":49,"description":7,"dependencies":51},[30,45,28,46,23,25],"RAG","ChEMBL","1.0.0","chemistry",{"skill-author":50},"Synthetic Sciences",[52,24,37],"rdkit-pypi","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fmolecular-rag","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add fadrienne\u002FMyGitHub --skill molecular-rag",{"tool":65,"command":66},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill molecular-rag",{"tool":68,"command":69},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill molecular-rag","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fmolecular-rag\u002FSKILL.md",true,[73],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"scripts\u002Fretrieve_analogs.py","SCRIPT","脚本",7884,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fmolecular-rag\u002Fscripts\u002Fretrieve_analogs.py",[80,112,138,172,199,226],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":91,"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",[86],{"categoryNo":87,"categoryName":88,"taxonomyType":89,"taxonomyTypeDesc":90},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":121,"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",[118],{"categoryNo":119,"categoryName":120,"taxonomyType":89,"taxonomyTypeDesc":90},"01JBSEEDCAT000000000000008","测试与质量",[103,122,92,123,124,125,100,126,127,128,129,130,131,26,132,133,134,135,136,137],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","test","research","results","statsmodels","models","help",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":153,"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",[144,147,148],{"categoryNo":145,"categoryName":146,"taxonomyType":89,"taxonomyTypeDesc":90},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":87,"categoryName":88,"taxonomyType":89,"taxonomyTypeDesc":90},{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[154,155,156,157,158,159,160,161,162,163,101,164,165,166,167,168,169,170,127,171],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":182,"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",[178,181],{"categoryNo":179,"categoryName":180,"taxonomyType":89,"taxonomyTypeDesc":90},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":87,"categoryName":88,"taxonomyType":89,"taxonomyTypeDesc":90},[183,155,184,185,186,187,188,189,190,162,100,191,192,193,194,195,196,197,129,198],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":211,"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",[205,208],{"categoryNo":206,"categoryName":207,"taxonomyType":89,"taxonomyTypeDesc":90},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":209,"categoryName":210,"taxonomyType":151,"taxonomyTypeDesc":152},"01JBSEEDCAT000000000000030","开发工程师",[212,213,133,214,215,216,217,99,218,100,101,103,219,220,221,222,223,224,170,225],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":230,"licenseFileUrl":6,"sourceHtmlUrl":231,"categories":232,"keywords":239,"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",[233,236,237,238],{"categoryNo":234,"categoryName":235,"taxonomyType":89,"taxonomyTypeDesc":90},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":145,"categoryName":146,"taxonomyType":89,"taxonomyTypeDesc":90},{"categoryNo":87,"categoryName":88,"taxonomyType":89,"taxonomyTypeDesc":90},{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},[154,240,241,242,243,244,183,245,246,100,101,103,247,248,249,250,251,252,155,170],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]