[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFJAV7NXNJYNG3W2KT74V0":3,"skill-same:01M6XFJAV7NXNJYNG3W2KT74V0":99},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6XFJAV7NXNJYNG3W2KT74V0","molecule-visualization",null,"Publication-quality molecular visualization. 2D structure drawings (PNG\u002FSVG), molecule grids with property annotations, scaffold highlighting, protein-ligand interaction diagrams, and interactive 3D views.","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\u002Fmolecule-visualization\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"visualization","molecule visualization","Publication","scaffold","Protein-Ligand","Protein","2d","with","interaction","synthetic","SVG","molecular","structure","diagrams","interactive","annotations","quality","png","3D","2026-11-10 10:12:30",false,"---\nname: molecule-visualization\ndescription: Publication-quality molecular visualization. 2D structure drawings (PNG\u002FSVG), molecule grids with property annotations, scaffold highlighting, protein-ligand interaction diagrams, and interactive 3D views.\ncategory: chemistry\nlicense: MIT\nmetadata:\n    skill-author: Synthetic Sciences\n---\n\n# Molecule Visualization\n\nGenerate publication-quality molecular images for drug discovery, medicinal chemistry, and computational chemistry workflows. This skill provides a comprehensive suite of tools for rendering 2D structural drawings, annotated molecule grids, scaffold decomposition views, protein-ligand interaction diagrams, and interactive 3D molecular viewers.\n\n## When to Use\n\n- **2D structure drawings**: Generate clean, high-resolution depictions of small molecules for papers, patents, reports, and presentations.\n- **Molecule grids**: Compare compound series side-by-side with property annotations (QED, LogP, MW, etc.).\n- **Scaffold highlighting**: Visualize SAR by decomposing molecules into core scaffolds and R-groups.\n- **Interaction diagrams**: Summarize protein-ligand binding modes from docking or crystal structures.\n- **3D interactive views**: Create browser-based 3D viewers for proteins, ligands, and complexes.\n\n## Installation\n\nAll scripts require Python 3.8+ and the following packages:\n\n```bash\n# Core (required for all scripts)\npip install rdkit-pypi pillow matplotlib\n\n# For protein-ligand interaction diagrams\npip install biopython\n\n# For 3D interactive views\npip install py3Dmol\n\n# Full installation\npip install rdkit-pypi pillow matplotlib biopython py3Dmol\n```\n\n## Core Workflows\n\n### 1. Single Molecule Drawing (`scripts\u002Fdraw_2d.py`)\n\nRender a single molecule as a high-quality PNG or SVG image.\n\n```bash\n# Basic usage\npython scripts\u002Fdraw_2d.py --smiles \"c1ccccc1\" --output benzene.png\n\n# With atom highlighting and title\npython scripts\u002Fdraw_2d.py \\\n  --smiles \"CC(=O)Oc1ccccc1C(=O)O\" \\\n  --output aspirin.svg \\\n  --title \"Aspirin\" \\\n  --highlight-atoms 0,1,2,3 \\\n  --highlight-color \"#4A90D9\"\n\n# Show atom indices for reference\npython scripts\u002Fdraw_2d.py \\\n  --smiles \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --output benzanilide.png \\\n  --show-atom-indices \\\n  --size 600x400\n```\n\n### 2. Molecule Grid (`scripts\u002Fdraw_grid.py`)\n\nCompare multiple molecules in a grid layout with optional property annotations.\n\n```bash\n# From CSV file\npython scripts\u002Fdraw_grid.py \\\n  --input compounds.csv \\\n  --output grid.png \\\n  --cols 4 \\\n  --properties \"qed,mw,logp\"\n\n# From comma-separated SMILES\npython scripts\u002Fdraw_grid.py \\\n  --input \"c1ccccc1,c1ccncc1,c1ccoc1\" \\\n  --output ring_comparison.png \\\n  --title \"Aromatic Ring Comparison\"\n```\n\n### 3. Scaffold Highlighting (`scripts\u002Fdraw_scaffold.py`)\n\nDecompose molecules into scaffolds and R-groups for SAR analysis.\n\n```bash\n# Manual scaffold specification\npython scripts\u002Fdraw_scaffold.py \\\n  --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n  --scaffold \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --output scaffold.png\n\n# Automatic Murcko scaffold detection\npython scripts\u002Fdraw_scaffold.py \\\n  --smiles \"CC(=O)Oc1ccccc1C(=O)O\" \\\n  --scaffold auto \\\n  --output murcko.png\n\n# R-group decomposition across analogs\npython scripts\u002Fdraw_scaffold.py \\\n  --smiles \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --scaffold \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --analogs analogs.csv \\\n  --output rgroup_table.png\n```\n\n### 4. Protein-Ligand Interaction Diagram (`scripts\u002Fdraw_interactions.py`)\n\nGenerate 2D interaction diagrams from protein-ligand complexes.\n\n```bash\npython scripts\u002Fdraw_interactions.py \\\n  --protein receptor.pdb \\\n  --ligand ligand.sdf \\\n  --output interactions.png \\\n  --distance-cutoff 4.0\n```\n\n### 5. Interactive 3D View (`scripts\u002Frender_3d.py`)\n\nCreate self-contained HTML files with interactive 3D molecular viewers.\n\n```bash\n# Protein with cartoon representation\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --output view.html \\\n  --style cartoon \\\n  --color chain\n\n# Protein-ligand complex\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --ligand ligand.sdf \\\n  --output complex.html \\\n  --style cartoon\n```\n\n### 6. Pocket Visualization (`scripts\u002Frender_3d.py --mode pockets`)\n\nVisualize detected binding pockets as colored spheres on the protein surface. Sphere color indicates druggability: green (>0.7), orange (0.4-0.7), red (\u003C0.4). Sphere size is proportional to pocket volume.\n\n```bash\n# After pocket-detection\u002Fdetect.py or druggability.py\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --pockets druggability.json \\\n  --mode pockets \\\n  --output pocket_view.html\n\n# With specific residues highlighted\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --pockets pockets.json \\\n  --mode pockets \\\n  --highlight-residues \"189,195,57\" \\\n  --output pocket_view.html\n```\n\n### 7. Docking Results Visualization (`scripts\u002Frender_3d.py --mode docking-results`)\n\nOverlay top docked poses on the protein, colored by rank (green = best, red = worst). The top-ranked pose gets a translucent surface highlight.\n\n```bash\n# After molecular-docking\u002Fdock.py\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --poses dock_results\u002Fposes.sdf \\\n  --mode docking-results \\\n  --top-n 5 \\\n  --output docking_results.html\n```\n\n## Script Reference\n\n| Script | Purpose | Key Inputs |\n|--------|---------|------------|\n| `draw_2d.py` | Single molecule 2D drawing | SMILES, output path |\n| `draw_grid.py` | Multi-molecule grid | CSV or SMILES list, output path |\n| `draw_scaffold.py` | Scaffold and R-group analysis | SMILES, scaffold, output path |\n| `draw_interactions.py` | Protein-ligand interactions | PDB, SDF, output path |\n| `render_3d.py` | Interactive 3D viewer | PDB\u002FSDF\u002FSMILES, output HTML |\n\n## Style Guide\n\nSee `references\u002Fstyle_guide.md` for detailed guidance on:\n\n- **Colors**: CPK atom coloring scheme (C=gray, N=blue, O=red, S=yellow, Cl=green, etc.)\n- **Resolution**: 300 DPI minimum for print; 150 DPI for screen. Vector (SVG) preferred for publications.\n- **Font sizes**: 12pt minimum for labels in figures; 8pt minimum for atom indices.\n- **Image dimensions**: Single molecule 400x300px default; grid cells 300x250px; interaction diagrams 800x800px.\n- **Interaction colors**: Green for H-bonds, gray for hydrophobic, orange for pi-stacking, red for salt bridges.\n- **Colorblind-friendly**: Prefer blue\u002Forange instead of red\u002Fgreen when accessibility is a concern.\n- **2D vs 3D**: Use 2D for SAR tables, patent figures, and print publications. Use 3D for binding mode analysis, presentations, and supplementary material.\n","# Molecule Visualization\n\nGenerate publication-quality molecular images for drug discovery, medicinal chemistry, and computational chemistry workflows. This skill provides a comprehensive suite of tools for rendering 2D structural drawings, annotated molecule grids, scaffold decomposition views, protein-ligand interaction diagrams, and interactive 3D molecular viewers.\n\n## When to Use\n\n- **2D structure drawings**: Generate clean, high-resolution depictions of small molecules for papers, patents, reports, and presentations.\n- **Molecule grids**: Compare compound series side-by-side with property annotations (QED, LogP, MW, etc.).\n- **Scaffold highlighting**: Visualize SAR by decomposing molecules into core scaffolds and R-groups.\n- **Interaction diagrams**: Summarize protein-ligand binding modes from docking or crystal structures.\n- **3D interactive views**: Create browser-based 3D viewers for proteins, ligands, and complexes.\n\n## Installation\n\nAll scripts require Python 3.8+ and the following packages:\n\n```bash\n# Core (required for all scripts)\npip install rdkit-pypi pillow matplotlib\n\n# For protein-ligand interaction diagrams\npip install biopython\n\n# For 3D interactive views\npip install py3Dmol\n\n# Full installation\npip install rdkit-pypi pillow matplotlib biopython py3Dmol\n```\n\n## Core Workflows\n\n### 1. Single Molecule Drawing (`scripts\u002Fdraw_2d.py`)\n\nRender a single molecule as a high-quality PNG or SVG image.\n\n```bash\n# Basic usage\npython scripts\u002Fdraw_2d.py --smiles \"c1ccccc1\" --output benzene.png\n\n# With atom highlighting and title\npython scripts\u002Fdraw_2d.py \\\n  --smiles \"CC(=O)Oc1ccccc1C(=O)O\" \\\n  --output aspirin.svg \\\n  --title \"Aspirin\" \\\n  --highlight-atoms 0,1,2,3 \\\n  --highlight-color \"#4A90D9\"\n\n# Show atom indices for reference\npython scripts\u002Fdraw_2d.py \\\n  --smiles \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --output benzanilide.png \\\n  --show-atom-indices \\\n  --size 600x400\n```\n\n### 2. Molecule Grid (`scripts\u002Fdraw_grid.py`)\n\nCompare multiple molecules in a grid layout with optional property annotations.\n\n```bash\n# From CSV file\npython scripts\u002Fdraw_grid.py \\\n  --input compounds.csv \\\n  --output grid.png \\\n  --cols 4 \\\n  --properties \"qed,mw,logp\"\n\n# From comma-separated SMILES\npython scripts\u002Fdraw_grid.py \\\n  --input \"c1ccccc1,c1ccncc1,c1ccoc1\" \\\n  --output ring_comparison.png \\\n  --title \"Aromatic Ring Comparison\"\n```\n\n### 3. Scaffold Highlighting (`scripts\u002Fdraw_scaffold.py`)\n\nDecompose molecules into scaffolds and R-groups for SAR analysis.\n\n```bash\n# Manual scaffold specification\npython scripts\u002Fdraw_scaffold.py \\\n  --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n  --scaffold \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --output scaffold.png\n\n# Automatic Murcko scaffold detection\npython scripts\u002Fdraw_scaffold.py \\\n  --smiles \"CC(=O)Oc1ccccc1C(=O)O\" \\\n  --scaffold auto \\\n  --output murcko.png\n\n# R-group decomposition across analogs\npython scripts\u002Fdraw_scaffold.py \\\n  --smiles \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --scaffold \"c1ccc(NC(=O)c2ccccc2)cc1\" \\\n  --analogs analogs.csv \\\n  --output rgroup_table.png\n```\n\n### 4. Protein-Ligand Interaction Diagram (`scripts\u002Fdraw_interactions.py`)\n\nGenerate 2D interaction diagrams from protein-ligand complexes.\n\n```bash\npython scripts\u002Fdraw_interactions.py \\\n  --protein receptor.pdb \\\n  --ligand ligand.sdf \\\n  --output interactions.png \\\n  --distance-cutoff 4.0\n```\n\n### 5. Interactive 3D View (`scripts\u002Frender_3d.py`)\n\nCreate self-contained HTML files with interactive 3D molecular viewers.\n\n```bash\n# Protein with cartoon representation\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --output view.html \\\n  --style cartoon \\\n  --color chain\n\n# Protein-ligand complex\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --ligand ligand.sdf \\\n  --output complex.html \\\n  --style cartoon\n```\n\n### 6. Pocket Visualization (`scripts\u002Frender_3d.py --mode pockets`)\n\nVisualize detected binding pockets as colored spheres on the protein surface. Sphere color indicates druggability: green (>0.7), orange (0.4-0.7), red (\u003C0.4). Sphere size is proportional to pocket volume.\n\n```bash\n# After pocket-detection\u002Fdetect.py or druggability.py\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --pockets druggability.json \\\n  --mode pockets \\\n  --output pocket_view.html\n\n# With specific residues highlighted\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --pockets pockets.json \\\n  --mode pockets \\\n  --highlight-residues \"189,195,57\" \\\n  --output pocket_view.html\n```\n\n### 7. Docking Results Visualization (`scripts\u002Frender_3d.py --mode docking-results`)\n\nOverlay top docked poses on the protein, colored by rank (green = best, red = worst). The top-ranked pose gets a translucent surface highlight.\n\n```bash\n# After molecular-docking\u002Fdock.py\npython scripts\u002Frender_3d.py \\\n  --input protein.pdb \\\n  --poses dock_results\u002Fposes.sdf \\\n  --mode docking-results \\\n  --top-n 5 \\\n  --output docking_results.html\n```\n\n## Script Reference\n\n| Script | Purpose | Key Inputs |\n|--------|---------|------------|\n| `draw_2d.py` | Single molecule 2D drawing | SMILES, output path |\n| `draw_grid.py` | Multi-molecule grid | CSV or SMILES list, output path |\n| `draw_scaffold.py` | Scaffold and R-group analysis | SMILES, scaffold, output path |\n| `draw_interactions.py` | Protein-ligand interactions | PDB, SDF, output path |\n| `render_3d.py` | Interactive 3D viewer | PDB\u002FSDF\u002FSMILES, output HTML |\n\n## Style Guide\n\nSee `references\u002Fstyle_guide.md` for detailed guidance on:\n\n- **Colors**: CPK atom coloring scheme (C=gray, N=blue, O=red, S=yellow, Cl=green, etc.)\n- **Resolution**: 300 DPI minimum for print; 150 DPI for screen. Vector (SVG) preferred for publications.\n- **Font sizes**: 12pt minimum for labels in figures; 8pt minimum for atom indices.\n- **Image dimensions**: Single molecule 400x300px default; grid cells 300x250px; interaction diagrams 800x800px.\n- **Interaction colors**: Green for H-bonds, gray for hydrophobic, orange for pi-stacking, red for salt bridges.\n- **Colorblind-friendly**: Prefer blue\u002Forange instead of red\u002Fgreen when accessibility is a concern.\n- **2D vs 3D**: Use 2D for SAR tables, patent figures, and print publications. 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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",[106],{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":139,"publishedAt":43},"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",[136],{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[121,140,110,141,142,143,118,144,145,146,147,148,149,31,150,151,152,153,154,155],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","test","research","results","statsmodels","models","help",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":171,"publishedAt":43},"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",[162,165,166],{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":167,"categoryName":168,"taxonomyType":169,"taxonomyTypeDesc":170},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[172,173,174,175,176,177,178,179,180,181,119,182,183,36,184,185,186,187,145,188],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","openrouter","Outlines","core skill","read","features",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":197,"publishedAt":43},"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",[195,196],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},[24,173,198,199,200,201,202,203,204,180,118,205,206,39,207,26,208,209,147,210],"scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","formatting","figures","panel","submission",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":223,"publishedAt":43},"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",[217,220],{"categoryNo":218,"categoryName":219,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":221,"categoryName":222,"taxonomyType":169,"taxonomyTypeDesc":170},"01JBSEEDCAT000000000000030","开发工程师",[224,225,151,226,227,228,229,117,230,118,119,121,231,232,233,234,235,236,187,237],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":239,"name":240,"nameZh":6,"description":241,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":242,"licenseFileUrl":6,"sourceHtmlUrl":243,"categories":244,"keywords":251,"publishedAt":43},"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",[245,248,249,250],{"categoryNo":246,"categoryName":247,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":167,"categoryName":168,"taxonomyType":169,"taxonomyTypeDesc":170},[172,252,253,254,255,37,24,256,257,118,119,121,258,259,260,261,262,263,173,187],"markdown mermaid writing","mermaid","markdown","community","project","default","team","guides","text","documentation","references","diagram"]