[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFDCSNZBBXCPMXRQM541C6":3,"skill-same:01M6XFDCSNZBBXCPMXRQM541C6":84},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":14,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M6XFDCSNZBBXCPMXRQM541C6","pysam",null,"Genomic file toolkit. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignments, VCF\u002FBCF variants, FASTA\u002FFASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.","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\u002Fbiology\u002Fpysam\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"synthetic","FASTA","sequences","variants","for","processing","biology","vcf","bam","FASTQ","toolkit","coverage","write","read","calculate","data processing pipelines","data processing","data","file","2026-11-10 10:12:30",false,"---\nname: pysam\ndescription: Genomic file toolkit. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignments, VCF\u002FBCF variants, FASTA\u002FFASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.\ncategory: biology\nlicense: MIT license\nmetadata:\n    skill-author: Synthetic Sciences\n---\n\n# Pysam\n\n## Overview\n\nPysam is a Python module for reading, manipulating, and writing genomic datasets. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignment files, VCF\u002FBCF variant files, and FASTA\u002FFASTQ sequences with a Pythonic interface to htslib. Query tabix-indexed files, perform pileup analysis for coverage, and execute samtools\u002Fbcftools commands.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Working with sequencing alignment files (BAM\u002FCRAM)\n- Analyzing genetic variants (VCF\u002FBCF)\n- Extracting reference sequences or gene regions\n- Processing raw sequencing data (FASTQ)\n- Calculating coverage or read depth\n- Implementing bioinformatics analysis pipelines\n- Quality control of sequencing data\n- Variant calling and annotation workflows\n\n## Quick Start\n\n### Installation\n```bash\nuv pip install pysam\n```\n\n### Basic Examples\n\n**Read alignment file:**\n```python\nimport pysam\n\n# Open BAM file and fetch reads in region\nsamfile = pysam.AlignmentFile(\"example.bam\", \"rb\")\nfor read in samfile.fetch(\"chr1\", 1000, 2000):\n    print(f\"{read.query_name}: {read.reference_start}\")\nsamfile.close()\n```\n\n**Read variant file:**\n```python\n# Open VCF file and iterate variants\nvcf = pysam.VariantFile(\"variants.vcf\")\nfor variant in vcf:\n    print(f\"{variant.chrom}:{variant.pos} {variant.ref}>{variant.alts}\")\nvcf.close()\n```\n\n**Query reference sequence:**\n```python\n# Open FASTA and extract sequence\nfasta = pysam.FastaFile(\"reference.fasta\")\nsequence = fasta.fetch(\"chr1\", 1000, 2000)\nprint(sequence)\nfasta.close()\n```\n\n## Core Capabilities\n\n### 1. Alignment File Operations (SAM\u002FBAM\u002FCRAM)\n\nUse the `AlignmentFile` class to work with aligned sequencing reads. This is appropriate for analyzing mapping results, calculating coverage, extracting reads, or quality control.\n\n**Common operations:**\n- Open and read BAM\u002FSAM\u002FCRAM files\n- Fetch reads from specific genomic regions\n- Filter reads by mapping quality, flags, or other criteria\n- Write filtered or modified alignments\n- Calculate coverage statistics\n- Perform pileup analysis (base-by-base coverage)\n- Access read sequences, quality scores, and alignment information\n\n**Reference:** See `references\u002Falignment_files.md` for detailed documentation on:\n- Opening and reading alignment files\n- AlignedSegment attributes and methods\n- Region-based fetching with `fetch()`\n- Pileup analysis for coverage\n- Writing and creating BAM files\n- Coordinate systems and indexing\n- Performance optimization tips\n\n### 2. Variant File Operations (VCF\u002FBCF)\n\nUse the `VariantFile` class to work with genetic variants from variant calling pipelines. This is appropriate for variant analysis, filtering, annotation, or population genetics.\n\n**Common operations:**\n- Read and write VCF\u002FBCF files\n- Query variants in specific regions\n- Access variant information (position, alleles, quality)\n- Extract genotype data for samples\n- Filter variants by quality, allele frequency, or other criteria\n- Annotate variants with additional information\n- Subset samples or regions\n\n**Reference:** See `references\u002Fvariant_files.md` for detailed documentation on:\n- Opening and reading variant files\n- VariantRecord attributes and methods\n- Accessing INFO and FORMAT fields\n- Working with genotypes and samples\n- Creating and writing VCF files\n- Filtering and subsetting variants\n- Multi-sample VCF operations\n\n### 3. Sequence File Operations (FASTA\u002FFASTQ)\n\nUse `FastaFile` for random access to reference sequences and `FastxFile` for reading raw sequencing data. This is appropriate for extracting gene sequences, validating variants against reference, or processing raw reads.\n\n**Common operations:**\n- Query reference sequences by genomic coordinates\n- Extract sequences for genes or regions of interest\n- Read FASTQ files with quality scores\n- Validate variant reference alleles\n- Calculate sequence statistics\n- Filter reads by quality or length\n- Convert between FASTA and FASTQ formats\n\n**Reference:** See `references\u002Fsequence_files.md` for detailed documentation on:\n- FASTA file access and indexing\n- Extracting sequences by region\n- Handling reverse complement for genes\n- Reading FASTQ files sequentially\n- Quality score conversion and filtering\n- Working with tabix-indexed files (BED, GTF, GFF)\n- Common sequence processing patterns\n\n### 4. Integrated Bioinformatics Workflows\n\nPysam excels at integrating multiple file types for comprehensive genomic analyses. Common workflows combine alignment files, variant files, and reference sequences.\n\n**Common workflows:**\n- Calculate coverage statistics for specific regions\n- Validate variants against aligned reads\n- Annotate variants with coverage information\n- Extract sequences around variant positions\n- Filter alignments or variants based on multiple criteria\n- Generate coverage tracks for visualization\n- Quality control across multiple data types\n\n**Reference:** See `references\u002Fcommon_workflows.md` for detailed examples of:\n- Quality control workflows (BAM statistics, reference consistency)\n- Coverage analysis (per-base coverage, low coverage detection)\n- Variant analysis (annotation, filtering by read support)\n- Sequence extraction (variant contexts, gene sequences)\n- Read filtering and subsetting\n- Integration patterns (BAM+VCF, VCF+BED, etc.)\n- Performance optimization for complex workflows\n\n## Key Concepts\n\n### Coordinate Systems\n\n**Critical:** Pysam uses **0-based, half-open** coordinates (Python convention):\n- Start positions are 0-based (first base is position 0)\n- End positions are exclusive (not included in the range)\n- Region 1000-2000 includes bases 1000-1999 (1000 bases total)\n\n**Exception:** Region strings in `fetch()` follow samtools convention (1-based):\n```python\nsamfile.fetch(\"chr1\", 999, 2000)      # 0-based: positions 999-1999\nsamfile.fetch(\"chr1:1000-2000\")       # 1-based string: positions 1000-2000\n```\n\n**VCF files:** Use 1-based coordinates in the file format, but `VariantRecord.start` is 0-based.\n\n### Indexing Requirements\n\nRandom access to specific genomic regions requires index files:\n- **BAM files**: Require `.bai` index (create with `pysam.index()`)\n- **CRAM files**: Require `.crai` index\n- **FASTA files**: Require `.fai` index (create with `pysam.faidx()`)\n- **VCF.gz files**: Require `.tbi` tabix index (create with `pysam.tabix_index()`)\n- **BCF files**: Require `.csi` index\n\nWithout an index, use `fetch(until_eof=True)` for sequential reading.\n\n### File Modes\n\nSpecify format when opening files:\n- `\"rb\"` - Read BAM (binary)\n- `\"r\"` - Read SAM (text)\n- `\"rc\"` - Read CRAM\n- `\"wb\"` - Write BAM\n- `\"w\"` - Write SAM\n- `\"wc\"` - Write CRAM\n\n### Performance Considerations\n\n1. **Always use indexed files** for random access operations\n2. **Use `pileup()` for column-wise analysis** instead of repeated fetch operations\n3. **Use `count()` for counting** instead of iterating and counting manually\n4. **Process regions in parallel** when analyzing independent genomic regions\n5. **Close files explicitly** to free resources\n6. **Use `until_eof=True`** for sequential processing without index\n7. **Avoid multiple iterators** unless necessary (use `multiple_iterators=True` if needed)\n\n## Common Pitfalls\n\n1. **Coordinate confusion:** Remember 0-based vs 1-based systems in different contexts\n2. **Missing indices:** Many operations require index files—create them first\n3. **Partial overlaps:** `fetch()` returns reads overlapping region boundaries, not just those fully contained\n4. **Iterator scope:** Keep pileup iterator references alive to avoid \"PileupProxy accessed after iterator finished\" errors\n5. **Quality score editing:** Cannot modify `query_qualities` in place after changing `query_sequence`—create a copy first\n6. **Stream limitations:** Only stdin\u002Fstdout are supported for streaming, not arbitrary Python file objects\n7. **Thread safety:** While GIL is released during I\u002FO, comprehensive thread-safety hasn't been fully validated\n\n## Command-Line Tools\n\nPysam provides access to samtools and bcftools commands:\n\n```python\n# Sort BAM file\npysam.samtools.sort(\"-o\", \"sorted.bam\", \"input.bam\")\n\n# Index BAM\npysam.samtools.index(\"sorted.bam\")\n\n# View specific region\npysam.samtools.view(\"-b\", \"-o\", \"region.bam\", \"input.bam\", \"chr1:1000-2000\")\n\n# BCF tools\npysam.bcftools.view(\"-O\", \"z\", \"-o\", \"output.vcf.gz\", \"input.vcf\")\n```\n\n**Error handling:**\n```python\ntry:\n    pysam.samtools.sort(\"-o\", \"output.bam\", \"input.bam\")\nexcept pysam.SamtoolsError as e:\n    print(f\"Error: {e}\")\n```\n\n## Resources\n\n### references\u002F\n\nDetailed documentation for each major capability:\n\n- **alignment_files.md** - Complete guide to SAM\u002FBAM\u002FCRAM operations, including AlignmentFile class, AlignedSegment attributes, fetch operations, pileup analysis, and writing alignments\n\n- **variant_files.md** - Complete guide to VCF\u002FBCF operations, including VariantFile class, VariantRecord attributes, genotype handling, INFO\u002FFORMAT fields, and multi-sample operations\n\n- **sequence_files.md** - Complete guide to FASTA\u002FFASTQ operations, including FastaFile and FastxFile classes, sequence extraction, quality score handling, and tabix-indexed file access\n\n- **common_workflows.md** - Practical examples of integrated bioinformatics workflows combining multiple file types, including quality control, coverage analysis, variant validation, and sequence extraction\n\n## Getting Help\n\nFor detailed information on specific operations, refer to the appropriate reference document:\n\n- Working with BAM files or calculating coverage → `alignment_files.md`\n- Analyzing variants or genotypes → `variant_files.md`\n- Extracting sequences or processing FASTQ → `sequence_files.md`\n- Complex workflows integrating multiple file types → `common_workflows.md`\n\nOfficial documentation: https:\u002F\u002Fpysam.readthedocs.io\u002F\n\n","# Pysam\n\n## Overview\n\nPysam is a Python module for reading, manipulating, and writing genomic datasets. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignment files, VCF\u002FBCF variant files, and FASTA\u002FFASTQ sequences with a Pythonic interface to htslib. Query tabix-indexed files, perform pileup analysis for coverage, and execute samtools\u002Fbcftools commands.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Working with sequencing alignment files (BAM\u002FCRAM)\n- Analyzing genetic variants (VCF\u002FBCF)\n- Extracting reference sequences or gene regions\n- Processing raw sequencing data (FASTQ)\n- Calculating coverage or read depth\n- Implementing bioinformatics analysis pipelines\n- Quality control of sequencing data\n- Variant calling and annotation workflows\n\n## Quick Start\n\n### Installation\n```bash\nuv pip install pysam\n```\n\n### Basic Examples\n\n**Read alignment file:**\n```python\nimport pysam\n\n# Open BAM file and fetch reads in region\nsamfile = pysam.AlignmentFile(\"example.bam\", \"rb\")\nfor read in samfile.fetch(\"chr1\", 1000, 2000):\n    print(f\"{read.query_name}: {read.reference_start}\")\nsamfile.close()\n```\n\n**Read variant file:**\n```python\n# Open VCF file and iterate variants\nvcf = pysam.VariantFile(\"variants.vcf\")\nfor variant in vcf:\n    print(f\"{variant.chrom}:{variant.pos} {variant.ref}>{variant.alts}\")\nvcf.close()\n```\n\n**Query reference sequence:**\n```python\n# Open FASTA and extract sequence\nfasta = pysam.FastaFile(\"reference.fasta\")\nsequence = fasta.fetch(\"chr1\", 1000, 2000)\nprint(sequence)\nfasta.close()\n```\n\n## Core Capabilities\n\n### 1. Alignment File Operations (SAM\u002FBAM\u002FCRAM)\n\nUse the `AlignmentFile` class to work with aligned sequencing reads. This is appropriate for analyzing mapping results, calculating coverage, extracting reads, or quality control.\n\n**Common operations:**\n- Open and read BAM\u002FSAM\u002FCRAM files\n- Fetch reads from specific genomic regions\n- Filter reads by mapping quality, flags, or other criteria\n- Write filtered or modified alignments\n- Calculate coverage statistics\n- Perform pileup analysis (base-by-base coverage)\n- Access read sequences, quality scores, and alignment information\n\n**Reference:** See `references\u002Falignment_files.md` for detailed documentation on:\n- Opening and reading alignment files\n- AlignedSegment attributes and methods\n- Region-based fetching with `fetch()`\n- Pileup analysis for coverage\n- Writing and creating BAM files\n- Coordinate systems and indexing\n- Performance optimization tips\n\n### 2. Variant File Operations (VCF\u002FBCF)\n\nUse the `VariantFile` class to work with genetic variants from variant calling pipelines. This is appropriate for variant analysis, filtering, annotation, or population genetics.\n\n**Common operations:**\n- Read and write VCF\u002FBCF files\n- Query variants in specific regions\n- Access variant information (position, alleles, quality)\n- Extract genotype data for samples\n- Filter variants by quality, allele frequency, or other criteria\n- Annotate variants with additional information\n- Subset samples or regions\n\n**Reference:** See `references\u002Fvariant_files.md` for detailed documentation on:\n- Opening and reading variant files\n- VariantRecord attributes and methods\n- Accessing INFO and FORMAT fields\n- Working with genotypes and samples\n- Creating and writing VCF files\n- Filtering and subsetting variants\n- Multi-sample VCF operations\n\n### 3. Sequence File Operations (FASTA\u002FFASTQ)\n\nUse `FastaFile` for random access to reference sequences and `FastxFile` for reading raw sequencing data. This is appropriate for extracting gene sequences, validating variants against reference, or processing raw reads.\n\n**Common operations:**\n- Query reference sequences by genomic coordinates\n- Extract sequences for genes or regions of interest\n- Read FASTQ files with quality scores\n- Validate variant reference alleles\n- Calculate sequence statistics\n- Filter reads by quality or length\n- Convert between FASTA and FASTQ formats\n\n**Reference:** See `references\u002Fsequence_files.md` for detailed documentation on:\n- FASTA file access and indexing\n- Extracting sequences by region\n- Handling reverse complement for genes\n- Reading FASTQ files sequentially\n- Quality score conversion and filtering\n- Working with tabix-indexed files (BED, GTF, GFF)\n- Common sequence processing patterns\n\n### 4. Integrated Bioinformatics Workflows\n\nPysam excels at integrating multiple file types for comprehensive genomic analyses. Common workflows combine alignment files, variant files, and reference sequences.\n\n**Common workflows:**\n- Calculate coverage statistics for specific regions\n- Validate variants against aligned reads\n- Annotate variants with coverage information\n- Extract sequences around variant positions\n- Filter alignments or variants based on multiple criteria\n- Generate coverage tracks for visualization\n- Quality control across multiple data types\n\n**Reference:** See `references\u002Fcommon_workflows.md` for detailed examples of:\n- Quality control workflows (BAM statistics, reference consistency)\n- Coverage analysis (per-base coverage, low coverage detection)\n- Variant analysis (annotation, filtering by read support)\n- Sequence extraction (variant contexts, gene sequences)\n- Read filtering and subsetting\n- Integration patterns (BAM+VCF, VCF+BED, etc.)\n- Performance optimization for complex workflows\n\n## Key Concepts\n\n### Coordinate Systems\n\n**Critical:** Pysam uses **0-based, half-open** coordinates (Python convention):\n- Start positions are 0-based (first base is position 0)\n- End positions are exclusive (not included in the range)\n- Region 1000-2000 includes bases 1000-1999 (1000 bases total)\n\n**Exception:** Region strings in `fetch()` follow samtools convention (1-based):\n```python\nsamfile.fetch(\"chr1\", 999, 2000)      # 0-based: positions 999-1999\nsamfile.fetch(\"chr1:1000-2000\")       # 1-based string: positions 1000-2000\n```\n\n**VCF files:** Use 1-based coordinates in the file format, but `VariantRecord.start` is 0-based.\n\n### Indexing Requirements\n\nRandom access to specific genomic regions requires index files:\n- **BAM files**: Require `.bai` index (create with `pysam.index()`)\n- **CRAM files**: Require `.crai` index\n- **FASTA files**: Require `.fai` index (create with `pysam.faidx()`)\n- **VCF.gz files**: Require `.tbi` tabix index (create with `pysam.tabix_index()`)\n- **BCF files**: Require `.csi` index\n\nWithout an index, use `fetch(until_eof=True)` for sequential reading.\n\n### File Modes\n\nSpecify format when opening files:\n- `\"rb\"` - Read BAM (binary)\n- `\"r\"` - Read SAM (text)\n- `\"rc\"` - Read CRAM\n- `\"wb\"` - Write BAM\n- `\"w\"` - Write SAM\n- `\"wc\"` - Write CRAM\n\n### Performance Considerations\n\n1. **Always use indexed files** for random access operations\n2. **Use `pileup()` for column-wise analysis** instead of repeated fetch operations\n3. **Use `count()` for counting** instead of iterating and counting manually\n4. **Process regions in parallel** when analyzing independent genomic regions\n5. **Close files explicitly** to free resources\n6. **Use `until_eof=True`** for sequential processing without index\n7. **Avoid multiple iterators** unless necessary (use `multiple_iterators=True` if needed)\n\n## Common Pitfalls\n\n1. **Coordinate confusion:** Remember 0-based vs 1-based systems in different contexts\n2. **Missing indices:** Many operations require index files—create them first\n3. **Partial overlaps:** `fetch()` returns reads overlapping region boundaries, not just those fully contained\n4. **Iterator scope:** Keep pileup iterator references alive to avoid \"PileupProxy accessed after iterator finished\" errors\n5. **Quality score editing:** Cannot modify `query_qualities` in place after changing `query_sequence`—create a copy first\n6. **Stream limitations:** Only stdin\u002Fstdout are supported for streaming, not arbitrary Python file objects\n7. **Thread safety:** While GIL is released during I\u002FO, comprehensive thread-safety hasn't been fully validated\n\n## Command-Line Tools\n\nPysam provides access to samtools and bcftools commands:\n\n```python\n# Sort BAM file\npysam.samtools.sort(\"-o\", \"sorted.bam\", \"input.bam\")\n\n# Index BAM\npysam.samtools.index(\"sorted.bam\")\n\n# View specific region\npysam.samtools.view(\"-b\", \"-o\", \"region.bam\", \"input.bam\", \"chr1:1000-2000\")\n\n# BCF tools\npysam.bcftools.view(\"-O\", \"z\", \"-o\", \"output.vcf.gz\", \"input.vcf\")\n```\n\n**Error handling:**\n```python\ntry:\n    pysam.samtools.sort(\"-o\", \"output.bam\", \"input.bam\")\nexcept pysam.SamtoolsError as e:\n    print(f\"Error: {e}\")\n```\n\n## Resources\n\n### references\u002F\n\nDetailed documentation for each major capability:\n\n- **alignment_files.md** - Complete guide to SAM\u002FBAM\u002FCRAM operations, including AlignmentFile class, AlignedSegment attributes, fetch operations, pileup analysis, and writing alignments\n\n- **variant_files.md** - Complete guide to VCF\u002FBCF operations, including VariantFile class, VariantRecord attributes, genotype handling, INFO\u002FFORMAT fields, and multi-sample operations\n\n- **sequence_files.md** - Complete guide to FASTA\u002FFASTQ operations, including FastaFile and FastxFile classes, sequence extraction, quality score handling, and tabix-indexed file access\n\n- **common_workflows.md** - Practical examples of integrated bioinformatics workflows combining multiple file types, including quality control, coverage analysis, variant validation, and sequence extraction\n\n## Getting Help\n\nFor detailed information on specific operations, refer to the appropriate reference document:\n\n- Working with BAM files or calculating coverage → `alignment_files.md`\n- Analyzing variants or genotypes → `variant_files.md`\n- Extracting sequences or processing FASTQ → `sequence_files.md`\n- Complex workflows integrating multiple file types → `common_workflows.md`\n\nOfficial documentation: https:\u002F\u002Fpysam.readthedocs.io\u002F\n\n",{"name":5,"license":43,"category":25,"metadata":44,"description":7},"MIT license",{"skill-author":45},"Synthetic Sciences","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add fadrienne\u002FMyGitHub --skill pysam",{"tool":58,"command":59},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill pysam",{"tool":61,"command":62},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill pysam","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002FSKILL.md",true,[66,72,76,80],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"references\u002Falignment_files.md","REFERENCE","参考文档",8560,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002Freferences\u002Falignment_files.md",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fcommon_workflows.md",15350,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002Freferences\u002Fcommon_workflows.md",{"path":77,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fsequence_files.md",11597,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002Freferences\u002Fsequence_files.md",{"path":81,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fvariant_files.md",9487,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002Freferences\u002Fvariant_files.md",[85,117,142,175,202,228],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":96,"publishedAt":38},"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",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":126,"publishedAt":38},"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",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000008","测试与质量",[108,127,97,128,129,130,105,131,132,36,23,133,134,135,136,137,138,139,140,141],"statistical analysis","academic research","implementing","selection","you","reporting","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":157,"publishedAt":38},"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",[148,151,152],{"categoryNo":149,"categoryName":150,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":153,"categoryName":154,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[158,159,160,161,162,163,164,165,166,167,106,168,169,170,171,172,173,32,132,174],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","features",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":185,"publishedAt":38},"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",[181,184],{"categoryNo":182,"categoryName":183,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},[186,159,187,188,189,190,191,192,193,166,105,194,195,196,197,198,199,200,23,201],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":214,"publishedAt":38},"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",[208,211],{"categoryNo":209,"categoryName":210,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":212,"categoryName":213,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000030","开发工程师",[215,216,137,217,218,219,220,104,221,105,106,108,222,223,224,225,226,227,32,31],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":232,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":241,"publishedAt":38},"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",[235,238,239,240],{"categoryNo":236,"categoryName":237,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":149,"categoryName":150,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":153,"categoryName":154,"taxonomyType":155,"taxonomyTypeDesc":156},[158,242,243,244,245,246,186,247,248,105,106,108,249,250,251,252,253,254,159,32],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]