[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFKFD0WD67PCQB4XGX2SK9":3,"skill-same:01M6XFKFD0WD67PCQB4XGX2SK9":73},{"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":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":14,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M6XFKFD0WD67PCQB4XGX2SK9","smiles-validation",null,"Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.","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\u002Fsmiles-validation\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"validation","SMILES","smiles validation","verification","pypi","rdkit","quality","generated","cheminformatics","structural","control","invalid","synthetic","comparison","quality-control","Molecules","2026-11-10 10:12:30",false,"---\nname: smiles-validation\ndescription: Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.\ncategory: chemistry\nlicense: MIT\nmetadata:\n    skill-author: Synthetic Sciences\nversion: 1.0.0\ntags: [cheminformatics, validation, SMILES, quality-control]\ndependencies: [\"rdkit-pypi\"]\n---\n\n# SMILES Validation\n\n## Overview\n\nLLMs frequently generate invalid SMILES or produce molecules that don't match their stated reasoning. This skill provides strict validation, structural comparison, and modification verification.\n\nKey checks:\n- **Parse validation**: RDKit sanitization, valence checking, parenthesis\u002Fbracket balance\n- **Structural comparison**: Tanimoto similarity, scaffold preservation, MCS analysis\n- **Modification verification**: Confirm claimed structural changes exist in the actual molecule\n- **Classification**: Categorize changes as optimization (>0.6 similarity), significant modification (0.4-0.6), or de novo design (\u003C0.4)\n\n## When to Use This Skill\n\n- **After molecule generation**: Validate every SMILES before reporting results\n- **Optimization verification**: Confirm proposed modifications match the actual structure\n- **Batch validation**: Check a library of generated molecules for validity\n- **Quality control**: Ensure reproducibility of molecular designs\n\n## Installation\n\n```bash\npip install rdkit-pypi\n```\n\n## Core Workflows\n\n### 1. Validate a Single SMILES\n\n```bash\npython scripts\u002Fvalidate.py --smiles \"c1ccccc1\"\n```\n\n### 2. Compare Original vs Modified\n\n```bash\npython scripts\u002Fvalidate.py --original \"c1ccccc1\" --proposed \"c1ccc(O)cc1\" --check-modification \"Added hydroxyl group\"\n```\n\n### 3. Batch Validation\n\n```bash\npython scripts\u002Fvalidate.py --input generated_molecules.csv --output validation_report.json\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `validate.py` | SMILES validation, comparison, and modification checking | JSON report with validity, similarity, scaffold match, modification verification |\n","# SMILES Validation\n\n## Overview\n\nLLMs frequently generate invalid SMILES or produce molecules that don't match their stated reasoning. This skill provides strict validation, structural comparison, and modification verification.\n\nKey checks:\n- **Parse validation**: RDKit sanitization, valence checking, parenthesis\u002Fbracket balance\n- **Structural comparison**: Tanimoto similarity, scaffold preservation, MCS analysis\n- **Modification verification**: Confirm claimed structural changes exist in the actual molecule\n- **Classification**: Categorize changes as optimization (>0.6 similarity), significant modification (0.4-0.6), or de novo design (\u003C0.4)\n\n## When to Use This Skill\n\n- **After molecule generation**: Validate every SMILES before reporting results\n- **Optimization verification**: Confirm proposed modifications match the actual structure\n- **Batch validation**: Check a library of generated molecules for validity\n- **Quality control**: Ensure reproducibility of molecular designs\n\n## Installation\n\n```bash\npip install rdkit-pypi\n```\n\n## Core Workflows\n\n### 1. Validate a Single SMILES\n\n```bash\npython scripts\u002Fvalidate.py --smiles \"c1ccccc1\"\n```\n\n### 2. Compare Original vs Modified\n\n```bash\npython scripts\u002Fvalidate.py --original \"c1ccccc1\" --proposed \"c1ccc(O)cc1\" --check-modification \"Added hydroxyl group\"\n```\n\n### 3. Batch Validation\n\n```bash\npython scripts\u002Fvalidate.py --input generated_molecules.csv --output validation_report.json\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `validate.py` | SMILES validation, comparison, and modification checking | JSON report with validity, similarity, scaffold match, modification verification |\n",{"name":5,"tags":40,"license":15,"version":41,"category":42,"metadata":43,"description":7,"dependencies":45},[27,19,20,33],"1.0.0","chemistry",{"skill-author":44},"Synthetic Sciences",[46],"rdkit-pypi","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fsmiles-validation","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add fadrienne\u002FMyGitHub --skill smiles-validation",{"tool":59,"command":60},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill smiles-validation",{"tool":62,"command":63},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill smiles-validation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fsmiles-validation\u002FSKILL.md",true,[67],{"path":68,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":71,"sourceHtmlUrl":72},"scripts\u002Fvalidate.py","SCRIPT","脚本",8997,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fsmiles-validation\u002Fscripts\u002Fvalidate.py",[74,106,133,167,194,221],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":85,"publishedAt":35},"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",[80],{"categoryNo":81,"categoryName":82,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":115,"publishedAt":35},"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",[112],{"categoryNo":113,"categoryName":114,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000008","测试与质量",[97,116,86,117,118,119,94,120,121,122,123,124,125,126,127,128,129,130,131,132],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":148,"publishedAt":35},"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",[139,142,143],{"categoryNo":140,"categoryName":141,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":81,"categoryName":82,"taxonomyType":83,"taxonomyTypeDesc":84},{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[149,150,151,152,153,154,155,156,157,158,95,159,160,161,162,163,164,165,121,166],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":177,"publishedAt":35},"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",[173,176],{"categoryNo":174,"categoryName":175,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":81,"categoryName":82,"taxonomyType":83,"taxonomyTypeDesc":84},[178,150,179,180,181,182,183,184,185,157,94,186,187,188,189,190,191,192,123,193],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":206,"publishedAt":35},"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",[200,203],{"categoryNo":201,"categoryName":202,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":204,"categoryName":205,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000030","开发工程师",[207,208,128,209,210,211,212,93,213,94,95,97,214,215,216,217,218,219,165,220],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":225,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":234,"publishedAt":35},"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",[228,231,232,233],{"categoryNo":229,"categoryName":230,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":140,"categoryName":141,"taxonomyType":83,"taxonomyTypeDesc":84},{"categoryNo":81,"categoryName":82,"taxonomyType":83,"taxonomyTypeDesc":84},{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},[149,235,236,237,238,239,178,240,241,94,95,97,242,243,244,245,246,247,150,165],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]