[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XGS5334VGQVM7WFFFZ6W0G":3,"skill-same:01M6XGS5334VGQVM7WFFFZ6W0G":92},{"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":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6XGS5334VGQVM7WFFFZ6W0G","dna-visualization",null,"Publication-quality DNA\u002FRNA visualizations. Plasmid maps (circular\u002Flinear), sequence logos, restriction enzyme maps, GC content plots, and gene feature annotation tracks from GenBank\u002FFASTA.","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\u002Fvisualization\u002Fdna-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],"dna visualization","DNA","visualization","feature","Publication","GenBank","FASTA","sequence","gene","tracks","annotation","synthetic","RNA","maps","gc","quality","visualizations","content","linear","2026-11-10 10:12:30",false,"---\nname: dna-visualization\ndescription: Publication-quality DNA\u002FRNA visualizations. Plasmid maps (circular\u002Flinear), sequence logos, restriction enzyme maps, GC content plots, and gene feature annotation tracks from GenBank\u002FFASTA.\ncategory: visualization\nlicense: MIT\nmetadata:\n    skill-author: Synthetic Sciences\n---\n\n# DNA Visualization\n\nGenerate publication-quality DNA and RNA diagrams for molecular biology, genomics, and synthetic biology workflows. This skill provides tools for rendering annotated plasmid maps, sequence logos from alignments, restriction enzyme site maps, GC content plots, and linear gene feature tracks.\n\n## When to Use\n\n- **Plasmid maps**: Circular or linear plasmid diagrams with annotated features (promoters, genes, origins, terminators) from GenBank files.\n- **Sequence logos**: Consensus visualization from multiple sequence alignments showing positional conservation and variability.\n- **Restriction maps**: Annotate restriction enzyme cut sites on linear or circular DNA sequences.\n- **GC content plots**: Sliding-window GC percentage along a DNA sequence to identify GC-rich\u002FAT-rich regions.\n- **Gene maps**: Linear gene\u002Ffeature annotation tracks from GenBank or GFF files for publication figures.\n\n## Important\n\n**This skill handles DNA\u002FRNA sequences (FASTA, GenBank).** For small-molecule SMILES input, use `molecule-visualization` instead. Never pass a DNA sequence to a SMILES-based tool.\n\n## Installation\n\nAll scripts require Python 3.9+ and the following packages:\n\n```bash\n# Core (required for all scripts)\npip install biopython matplotlib\n\n# For plasmid maps and gene feature tracks\npip install dna_features_viewer\n\n# For sequence logos\npip install logomaker\n\n# Full installation\npip install biopython matplotlib dna_features_viewer logomaker\n```\n\n## Core Workflows\n\n### 1. Plasmid Map (`scripts\u002Fdraw_plasmid.py`)\n\nRender circular or linear plasmid maps with annotated features from a GenBank file.\n\n```bash\n# Circular plasmid map from GenBank\npython scripts\u002Fdraw_plasmid.py \\\n  --input plasmid.gb \\\n  --output plasmid_map.png\n\n# Linear map\npython scripts\u002Fdraw_plasmid.py \\\n  --input plasmid.gb \\\n  --output plasmid_linear.png \\\n  --linear\n\n# Custom feature colors and figure size\npython scripts\u002Fdraw_plasmid.py \\\n  --input plasmid.gb \\\n  --output plasmid_map.svg \\\n  --figsize 10x10 \\\n  --dpi 300\n```\n\n### 2. Sequence Logo (`scripts\u002Fdraw_sequence_logo.py`)\n\nGenerate a sequence logo from a multiple sequence alignment (FASTA or Clustal format).\n\n```bash\n# From aligned FASTA\npython scripts\u002Fdraw_sequence_logo.py \\\n  --input alignment.fasta \\\n  --output logo.png\n\n# Specify logo type (information or probability)\npython scripts\u002Fdraw_sequence_logo.py \\\n  --input alignment.fasta \\\n  --output logo.svg \\\n  --type information \\\n  --title \"Promoter Motif\"\n\n# Show specific positions only\npython scripts\u002Fdraw_sequence_logo.py \\\n  --input alignment.fasta \\\n  --output logo.png \\\n  --start 10 --end 30\n```\n\n### 3. Restriction Map (`scripts\u002Fdraw_restriction_map.py`)\n\nAnnotate restriction enzyme cut sites on a DNA sequence.\n\n```bash\n# Common enzymes on a GenBank sequence\npython scripts\u002Fdraw_restriction_map.py \\\n  --input sequence.gb \\\n  --output restriction_map.png\n\n# Specific enzymes on a FASTA sequence\npython scripts\u002Fdraw_restriction_map.py \\\n  --input sequence.fasta \\\n  --output restriction_map.png \\\n  --enzymes EcoRI,BamHI,HindIII,NotI\n\n# Linear display with custom figure width\npython scripts\u002Fdraw_restriction_map.py \\\n  --input sequence.gb \\\n  --output restriction_map.svg \\\n  --linear \\\n  --figwidth 16\n```\n\n### 4. GC Content Plot (`scripts\u002Fdraw_gc_content.py`)\n\nPlot sliding-window GC percentage along a DNA sequence.\n\n```bash\n# Default 100bp window\npython scripts\u002Fdraw_gc_content.py \\\n  --input sequence.fasta \\\n  --output gc_plot.png\n\n# Custom window size and step\npython scripts\u002Fdraw_gc_content.py \\\n  --input sequence.fasta \\\n  --output gc_plot.svg \\\n  --window 200 \\\n  --step 50 \\\n  --title \"GC Content — pUC19\"\n\n# Show threshold line\npython scripts\u002Fdraw_gc_content.py \\\n  --input sequence.gb \\\n  --output gc_plot.png \\\n  --threshold 0.5\n```\n\n### 5. Gene Map (`scripts\u002Fdraw_gene_map.py`)\n\nLinear gene\u002Ffeature annotation tracks from GenBank or GFF files.\n\n```bash\n# From GenBank file\npython scripts\u002Fdraw_gene_map.py \\\n  --input genome_region.gb \\\n  --output gene_map.png\n\n# Specific region only\npython scripts\u002Fdraw_gene_map.py \\\n  --input chromosome.gb \\\n  --output region.png \\\n  --start 10000 --end 25000\n\n# Custom figure dimensions\npython scripts\u002Fdraw_gene_map.py \\\n  --input genome_region.gb \\\n  --output gene_map.svg \\\n  --figsize 14x4 \\\n  --dpi 300\n```\n\n## Script Reference\n\n| Script | Purpose | Key Inputs |\n|--------|---------|------------|\n| `draw_plasmid.py` | Circular\u002Flinear plasmid maps | GenBank file, output path |\n| `draw_sequence_logo.py` | Sequence logo from MSA | Aligned FASTA\u002FClustal, output path |\n| `draw_restriction_map.py` | Restriction enzyme cut sites | GenBank\u002FFASTA, enzyme list, output |\n| `draw_gc_content.py` | Sliding-window GC% plot | FASTA\u002FGenBank, window size, output |\n| `draw_gene_map.py` | Linear gene feature tracks | GenBank\u002FGFF, output path |\n\n## Input Formats\n\n| Format | Extension | How to Provide |\n|--------|-----------|---------------|\n| GenBank | `.gb`, `.gbk`, `.genbank` | File path (contains sequence + feature annotations) |\n| FASTA | `.fasta`, `.fa`, `.fna` | File path (sequence only, no annotations) |\n| Clustal | `.aln`, `.clustal` | File path (for sequence logos from alignments) |\n| Raw sequence | inline | `--sequence ATCGATCG...` (some scripts support this) |\n\n## Style Guide\n\n- **Resolution**: 300 DPI for print; SVG preferred for publications.\n- **Colors**: Use colorblind-safe palettes. Default feature colors follow standard conventions: blue for CDS\u002Fgenes, green for promoters, red for terminators, orange for origins of replication.\n- **Font sizes**: 10pt minimum for feature labels; 8pt for nucleotide positions.\n- **Figure dimensions**: Plasmid maps 8x8 inches default; linear maps 14x4 inches; logos 10x3 inches.\n- **Sequence logos**: Use bits (information content) for conservation analysis; use probability for frequency visualization.\n","# DNA Visualization\n\nGenerate publication-quality DNA and RNA diagrams for molecular biology, genomics, and synthetic biology workflows. This skill provides tools for rendering annotated plasmid maps, sequence logos from alignments, restriction enzyme site maps, GC content plots, and linear gene feature tracks.\n\n## When to Use\n\n- **Plasmid maps**: Circular or linear plasmid diagrams with annotated features (promoters, genes, origins, terminators) from GenBank files.\n- **Sequence logos**: Consensus visualization from multiple sequence alignments showing positional conservation and variability.\n- **Restriction maps**: Annotate restriction enzyme cut sites on linear or circular DNA sequences.\n- **GC content plots**: Sliding-window GC percentage along a DNA sequence to identify GC-rich\u002FAT-rich regions.\n- **Gene maps**: Linear gene\u002Ffeature annotation tracks from GenBank or GFF files for publication figures.\n\n## Important\n\n**This skill handles DNA\u002FRNA sequences (FASTA, GenBank).** For small-molecule SMILES input, use `molecule-visualization` instead. Never pass a DNA sequence to a SMILES-based tool.\n\n## Installation\n\nAll scripts require Python 3.9+ and the following packages:\n\n```bash\n# Core (required for all scripts)\npip install biopython matplotlib\n\n# For plasmid maps and gene feature tracks\npip install dna_features_viewer\n\n# For sequence logos\npip install logomaker\n\n# Full installation\npip install biopython matplotlib dna_features_viewer logomaker\n```\n\n## Core Workflows\n\n### 1. Plasmid Map (`scripts\u002Fdraw_plasmid.py`)\n\nRender circular or linear plasmid maps with annotated features from a GenBank file.\n\n```bash\n# Circular plasmid map from GenBank\npython scripts\u002Fdraw_plasmid.py \\\n  --input plasmid.gb \\\n  --output plasmid_map.png\n\n# Linear map\npython scripts\u002Fdraw_plasmid.py \\\n  --input plasmid.gb \\\n  --output plasmid_linear.png \\\n  --linear\n\n# Custom feature colors and figure size\npython scripts\u002Fdraw_plasmid.py \\\n  --input plasmid.gb \\\n  --output plasmid_map.svg \\\n  --figsize 10x10 \\\n  --dpi 300\n```\n\n### 2. Sequence Logo (`scripts\u002Fdraw_sequence_logo.py`)\n\nGenerate a sequence logo from a multiple sequence alignment (FASTA or Clustal format).\n\n```bash\n# From aligned FASTA\npython scripts\u002Fdraw_sequence_logo.py \\\n  --input alignment.fasta \\\n  --output logo.png\n\n# Specify logo type (information or probability)\npython scripts\u002Fdraw_sequence_logo.py \\\n  --input alignment.fasta \\\n  --output logo.svg \\\n  --type information \\\n  --title \"Promoter Motif\"\n\n# Show specific positions only\npython scripts\u002Fdraw_sequence_logo.py \\\n  --input alignment.fasta \\\n  --output logo.png \\\n  --start 10 --end 30\n```\n\n### 3. Restriction Map (`scripts\u002Fdraw_restriction_map.py`)\n\nAnnotate restriction enzyme cut sites on a DNA sequence.\n\n```bash\n# Common enzymes on a GenBank sequence\npython scripts\u002Fdraw_restriction_map.py \\\n  --input sequence.gb \\\n  --output restriction_map.png\n\n# Specific enzymes on a FASTA sequence\npython scripts\u002Fdraw_restriction_map.py \\\n  --input sequence.fasta \\\n  --output restriction_map.png \\\n  --enzymes EcoRI,BamHI,HindIII,NotI\n\n# Linear display with custom figure width\npython scripts\u002Fdraw_restriction_map.py \\\n  --input sequence.gb \\\n  --output restriction_map.svg \\\n  --linear \\\n  --figwidth 16\n```\n\n### 4. GC Content Plot (`scripts\u002Fdraw_gc_content.py`)\n\nPlot sliding-window GC percentage along a DNA sequence.\n\n```bash\n# Default 100bp window\npython scripts\u002Fdraw_gc_content.py \\\n  --input sequence.fasta \\\n  --output gc_plot.png\n\n# Custom window size and step\npython scripts\u002Fdraw_gc_content.py \\\n  --input sequence.fasta \\\n  --output gc_plot.svg \\\n  --window 200 \\\n  --step 50 \\\n  --title \"GC Content — pUC19\"\n\n# Show threshold line\npython scripts\u002Fdraw_gc_content.py \\\n  --input sequence.gb \\\n  --output gc_plot.png \\\n  --threshold 0.5\n```\n\n### 5. Gene Map (`scripts\u002Fdraw_gene_map.py`)\n\nLinear gene\u002Ffeature annotation tracks from GenBank or GFF files.\n\n```bash\n# From GenBank file\npython scripts\u002Fdraw_gene_map.py \\\n  --input genome_region.gb \\\n  --output gene_map.png\n\n# Specific region only\npython scripts\u002Fdraw_gene_map.py \\\n  --input chromosome.gb \\\n  --output region.png \\\n  --start 10000 --end 25000\n\n# Custom figure dimensions\npython scripts\u002Fdraw_gene_map.py \\\n  --input genome_region.gb \\\n  --output gene_map.svg \\\n  --figsize 14x4 \\\n  --dpi 300\n```\n\n## Script Reference\n\n| Script | Purpose | Key Inputs |\n|--------|---------|------------|\n| `draw_plasmid.py` | Circular\u002Flinear plasmid maps | GenBank file, output path |\n| `draw_sequence_logo.py` | Sequence logo from MSA | Aligned FASTA\u002FClustal, output path |\n| `draw_restriction_map.py` | Restriction enzyme cut sites | GenBank\u002FFASTA, enzyme list, output |\n| `draw_gc_content.py` | Sliding-window GC% plot | FASTA\u002FGenBank, window size, output |\n| `draw_gene_map.py` | Linear gene feature tracks | GenBank\u002FGFF, output path |\n\n## Input Formats\n\n| Format | Extension | How to Provide |\n|--------|-----------|---------------|\n| GenBank | `.gb`, `.gbk`, `.genbank` | File path (contains sequence + feature annotations) |\n| FASTA | `.fasta`, `.fa`, `.fna` | File path (sequence only, no annotations) |\n| Clustal | `.aln`, `.clustal` | File path (for sequence logos from alignments) |\n| Raw sequence | inline | `--sequence ATCGATCG...` (some scripts support this) |\n\n## Style Guide\n\n- **Resolution**: 300 DPI for print; SVG preferred for publications.\n- **Colors**: Use colorblind-safe palettes. Default feature colors follow standard conventions: blue for CDS\u002Fgenes, green for promoters, red for terminators, orange for origins of replication.\n- **Font sizes**: 10pt minimum for feature labels; 8pt for nucleotide positions.\n- **Figure dimensions**: Plasmid maps 8x8 inches default; linear maps 14x4 inches; logos 10x3 inches.\n- **Sequence logos**: Use bits (information content) for conservation analysis; use probability for frequency visualization.\n",{"name":5,"license":15,"category":26,"metadata":48,"description":7},{"skill-author":49},"Synthetic Sciences","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add fadrienne\u002FMyGitHub --skill dna-visualization",{"tool":62,"command":63},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill dna-visualization",{"tool":65,"command":66},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill dna-visualization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization\u002FSKILL.md",true,[70,76,80,84,88],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"scripts\u002Fdraw_gc_content.py","SCRIPT","脚本",4237,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization\u002Fscripts\u002Fdraw_gc_content.py",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"scripts\u002Fdraw_gene_map.py",4609,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization\u002Fscripts\u002Fdraw_gene_map.py",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"scripts\u002Fdraw_plasmid.py",4078,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization\u002Fscripts\u002Fdraw_plasmid.py",{"path":85,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002Fdraw_restriction_map.py",4978,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization\u002Fscripts\u002Fdraw_restriction_map.py",{"path":89,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":90,"sourceHtmlUrl":91},"scripts\u002Fdraw_sequence_logo.py",4689,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fvisualization\u002Fdna-visualization\u002Fscripts\u002Fdraw_sequence_logo.py",[93,123,150,184,207,234],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":102,"publishedAt":43},"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",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":132,"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",[129],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[114,133,103,134,135,136,111,137,138,139,140,141,142,143,144,145,146,147,148,149],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":165,"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",[156,159,160],{"categoryNo":157,"categoryName":158,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":161,"categoryName":162,"taxonomyType":163,"taxonomyTypeDesc":164},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[166,167,168,169,170,171,172,173,174,175,112,176,177,178,179,180,181,182,138,183],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":192,"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",[190,191],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},[26,167,193,194,195,196,197,198,199,174,111,200,201,202,203,28,204,205,140,206],"scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","figures","panel","submission",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":219,"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",[213,216],{"categoryNo":214,"categoryName":215,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":217,"categoryName":218,"taxonomyType":163,"taxonomyTypeDesc":164},"01JBSEEDCAT000000000000030","开发工程师",[220,221,145,222,223,224,225,110,226,111,112,114,227,228,229,230,231,232,182,233],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":238,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":247,"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",[241,244,245,246],{"categoryNo":242,"categoryName":243,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":157,"categoryName":158,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":161,"categoryName":162,"taxonomyType":163,"taxonomyTypeDesc":164},[166,248,249,250,251,252,26,253,254,111,112,114,255,256,257,258,259,260,167,182],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]