[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-anthropic-nextflow-development":3,"mdc--8tu1su-key":40,"related-org-anthropic-nextflow-development":2124,"related-repo-anthropic-nextflow-development":2313},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":29,"repoUrl":30,"updatedAt":31,"license":32,"forks":33,"topics":34,"repo":35,"sourceUrl":38,"mdContent":39},"nextflow-development","run nf-core bioinformatics pipelines on sequencing data","Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS\u002FWES, or ATAC-seq data—either local FASTQs or public datasets from GEO\u002FSRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE\u002FGSM\u002FSRR accessions, or samplesheet creation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"anthropic","Anthropic","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fanthropic.png","anthropics",[13,17,20,23,26],{"name":14,"slug":15,"type":16},"Nextflow","nextflow","tag",{"name":18,"slug":19,"type":16},"Data Pipeline","data-pipeline",{"name":21,"slug":22,"type":16},"Life Sciences","life-sciences",{"name":24,"slug":25,"type":16},"Bioinformatics","bioinformatics",{"name":27,"slug":28,"type":16},"RNA-seq","rna-seq",537,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Flife-sciences","2026-04-06T17:57:04.014571",null,101,[],{"repoUrl":30,"stars":29,"forks":33,"topics":36,"description":37},[],"Repo for the Claude Code Marketplace to use with the Claude for Life Sciences Launch. This will continue to host the marketplace.json long-term, but not the actual MCP servers.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Flife-sciences\u002Ftree\u002FHEAD\u002Fnextflow-development","---\nname: nextflow-development\ndescription: Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS\u002FWES, or ATAC-seq data—either local FASTQs or public datasets from GEO\u002FSRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE\u002FGSM\u002FSRR accessions, or samplesheet creation.\n---\n\n# nf-core Pipeline Deployment\n\nRun nf-core bioinformatics pipelines on local or public sequencing data.\n\n**Target users:** Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.\n\n## Workflow Checklist\n\n```\n- [ ] Step 0: Acquire data (if from GEO\u002FSRA)\n- [ ] Step 1: Environment check (MUST pass)\n- [ ] Step 2: Select pipeline (confirm with user)\n- [ ] Step 3: Run test profile (MUST pass)\n- [ ] Step 4: Create samplesheet\n- [ ] Step 5: Configure & run (confirm genome with user)\n- [ ] Step 6: Verify outputs\n```\n\n---\n\n## Step 0: Acquire Data (GEO\u002FSRA Only)\n\n**Skip this step if user has local FASTQ files.**\n\nFor public datasets, fetch from GEO\u002FSRA first. See [references\u002Fgeo-sra-acquisition.md](references\u002Fgeo-sra-acquisition.md) for the full workflow.\n\n**Quick start:**\n\n```bash\n# 1. Get study info\npython scripts\u002Fsra_geo_fetch.py info GSE110004\n\n# 2. Download (interactive mode)\npython scripts\u002Fsra_geo_fetch.py download GSE110004 -o .\u002Ffastq -i\n\n# 3. Generate samplesheet\npython scripts\u002Fsra_geo_fetch.py samplesheet GSE110004 --fastq-dir .\u002Ffastq -o samplesheet.csv\n```\n\n**DECISION POINT:** After fetching study info, confirm with user:\n- Which sample subset to download (if multiple data types)\n- Suggested genome and pipeline\n\nThen continue to Step 1.\n\n---\n\n## Step 1: Environment Check\n\n**Run first. Pipeline will fail without passing environment.**\n\n```bash\npython scripts\u002Fcheck_environment.py\n```\n\nAll critical checks must pass. If any fail, provide fix instructions:\n\n### Docker issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed | Install from https:\u002F\u002Fdocs.docker.com\u002Fget-docker\u002F |\n| Permission denied | `sudo usermod -aG docker $USER` then re-login |\n| Daemon not running | `sudo systemctl start docker` |\n\n### Nextflow issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed | `curl -s https:\u002F\u002Fget.nextflow.io \\| bash && mv nextflow ~\u002Fbin\u002F` |\n| Version \u003C 23.04 | `nextflow self-update` |\n\n### Java issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed \u002F \u003C 11 | `sudo apt install openjdk-11-jdk` |\n\n**Do not proceed until all checks pass.** For HPC\u002FSingularity, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n---\n\n## Step 2: Select Pipeline\n\n**DECISION POINT: Confirm with user before proceeding.**\n\n| Data Type | Pipeline | Version | Goal |\n|-----------|----------|---------|------|\n| RNA-seq | `rnaseq` | 3.22.2 | Gene expression |\n| WGS\u002FWES | `sarek` | 3.7.1 | Variant calling |\n| ATAC-seq | `atacseq` | 2.1.2 | Chromatin accessibility |\n\nAuto-detect from data:\n```bash\npython scripts\u002Fdetect_data_type.py \u002Fpath\u002Fto\u002Fdata\n```\n\nFor pipeline-specific details:\n- [references\u002Fpipelines\u002Frnaseq.md](references\u002Fpipelines\u002Frnaseq.md)\n- [references\u002Fpipelines\u002Fsarek.md](references\u002Fpipelines\u002Fsarek.md)\n- [references\u002Fpipelines\u002Fatacseq.md](references\u002Fpipelines\u002Fatacseq.md)\n\n---\n\n## Step 3: Run Test Profile\n\n**Validates environment with small data. MUST pass before real data.**\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> -r \u003Cversion> -profile test,docker --outdir test_output\n```\n\n| Pipeline | Command |\n|----------|---------|\n| rnaseq | `nextflow run nf-core\u002Frnaseq -r 3.22.2 -profile test,docker --outdir test_rnaseq` |\n| sarek | `nextflow run nf-core\u002Fsarek -r 3.7.1 -profile test,docker --outdir test_sarek` |\n| atacseq | `nextflow run nf-core\u002Fatacseq -r 2.1.2 -profile test,docker --outdir test_atacseq` |\n\nVerify:\n```bash\nls test_output\u002Fmultiqc\u002Fmultiqc_report.html\ngrep \"Pipeline completed successfully\" .nextflow.log\n```\n\nIf test fails, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n---\n\n## Step 4: Create Samplesheet\n\n### Generate automatically\n\n```bash\npython scripts\u002Fgenerate_samplesheet.py \u002Fpath\u002Fto\u002Fdata \u003Cpipeline> -o samplesheet.csv\n```\n\nThe script:\n- Discovers FASTQ\u002FBAM\u002FCRAM files\n- Pairs R1\u002FR2 reads\n- Infers sample metadata\n- Validates before writing\n\n**For sarek:** Script prompts for tumor\u002Fnormal status if not auto-detected.\n\n### Validate existing samplesheet\n\n```bash\npython scripts\u002Fgenerate_samplesheet.py --validate samplesheet.csv \u003Cpipeline>\n```\n\n### Samplesheet formats\n\n**rnaseq:**\n```csv\nsample,fastq_1,fastq_2,strandedness\nSAMPLE1,\u002Fabs\u002Fpath\u002FR1.fq.gz,\u002Fabs\u002Fpath\u002FR2.fq.gz,auto\n```\n\n**sarek:**\n```csv\npatient,sample,lane,fastq_1,fastq_2,status\npatient1,tumor,L001,\u002Fabs\u002Fpath\u002Ftumor_R1.fq.gz,\u002Fabs\u002Fpath\u002Ftumor_R2.fq.gz,1\npatient1,normal,L001,\u002Fabs\u002Fpath\u002Fnormal_R1.fq.gz,\u002Fabs\u002Fpath\u002Fnormal_R2.fq.gz,0\n```\n\n**atacseq:**\n```csv\nsample,fastq_1,fastq_2,replicate\nCONTROL,\u002Fabs\u002Fpath\u002Fctrl_R1.fq.gz,\u002Fabs\u002Fpath\u002Fctrl_R2.fq.gz,1\n```\n\n---\n\n## Step 5: Configure & Run\n\n### 5a. Check genome availability\n\n```bash\npython scripts\u002Fmanage_genomes.py check \u003Cgenome>\n# If not installed:\npython scripts\u002Fmanage_genomes.py download \u003Cgenome>\n```\n\nCommon genomes: GRCh38 (human), GRCh37 (legacy), GRCm39 (mouse), R64-1-1 (yeast), BDGP6 (fly)\n\n### 5b. Decision points\n\n**DECISION POINT: Confirm with user:**\n\n1. **Genome:** Which reference to use\n2. **Pipeline-specific options:**\n   - **rnaseq:** aligner (star_salmon recommended, hisat2 for low memory)\n   - **sarek:** tools (haplotypecaller for germline, mutect2 for somatic)\n   - **atacseq:** read_length (50, 75, 100, or 150)\n\n### 5c. Run pipeline\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> \\\n    -r \u003Cversion> \\\n    -profile docker \\\n    --input samplesheet.csv \\\n    --outdir results \\\n    --genome \u003Cgenome> \\\n    -resume\n```\n\n**Key flags:**\n- `-r`: Pin version\n- `-profile docker`: Use Docker (or `singularity` for HPC)\n- `--genome`: iGenomes key\n- `-resume`: Continue from checkpoint\n\n**Resource limits (if needed):**\n```bash\n--max_cpus 8 --max_memory '32.GB' --max_time '24.h'\n```\n\n---\n\n## Step 6: Verify Outputs\n\n### Check completion\n\n```bash\nls results\u002Fmultiqc\u002Fmultiqc_report.html\ngrep \"Pipeline completed successfully\" .nextflow.log\n```\n\n### Key outputs by pipeline\n\n**rnaseq:**\n- `results\u002Fstar_salmon\u002Fsalmon.merged.gene_counts.tsv` - Gene counts\n- `results\u002Fstar_salmon\u002Fsalmon.merged.gene_tpm.tsv` - TPM values\n\n**sarek:**\n- `results\u002Fvariant_calling\u002F*\u002F` - VCF files\n- `results\u002Fpreprocessing\u002Frecalibrated\u002F` - BAM files\n\n**atacseq:**\n- `results\u002Fmacs2\u002FnarrowPeak\u002F` - Peak calls\n- `results\u002Fbwa\u002FmergedLibrary\u002Fbigwig\u002F` - Coverage tracks\n\n---\n\n## Quick Reference\n\nFor common exit codes and fixes, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n### Resume failed run\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> -resume\n```\n\n---\n\n## References\n\n- [references\u002Fgeo-sra-acquisition.md](references\u002Fgeo-sra-acquisition.md) - Downloading public GEO\u002FSRA data\n- [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) - Common issues and fixes\n- [references\u002Finstallation.md](references\u002Finstallation.md) - Environment setup\n- [references\u002Fpipelines\u002Frnaseq.md](references\u002Fpipelines\u002Frnaseq.md) - RNA-seq pipeline details\n- [references\u002Fpipelines\u002Fsarek.md](references\u002Fpipelines\u002Fsarek.md) - Variant calling details\n- [references\u002Fpipelines\u002Fatacseq.md](references\u002Fpipelines\u002Fatacseq.md) - ATAC-seq details\n\n---\n\n## Disclaimer\n\nThis skill is provided as a prototype example demonstrating how to integrate nf-core bioinformatics pipelines into Claude Code for automated analysis workflows. The current implementation supports three pipelines (rnaseq, sarek, and atacseq), serving as a foundation that enables the community to expand support to the full set of nf-core pipelines.\n\nIt is intended for educational and research purposes and should not be considered production-ready without appropriate validation for your specific use case. Users are responsible for ensuring their computing environment meets pipeline requirements and for verifying analysis results.\n\nAnthropic does not guarantee the accuracy of bioinformatics outputs, and users should follow standard practices for validating computational analyses. This integration is not officially endorsed by or affiliated with the nf-core community.\n\n## Attribution\n\nWhen publishing results, cite the appropriate pipeline. Citations are available in each nf-core repository's CITATIONS.md file (e.g., https:\u002F\u002Fgithub.com\u002Fnf-core\u002Frnaseq\u002Fblob\u002F3.22.2\u002FCITATIONS.md).\n\n## Licenses\n\n- **nf-core pipelines:** MIT License (https:\u002F\u002Fnf-co.re\u002Fabout)\n- **Nextflow:** Apache License, Version 2.0 (https:\u002F\u002Fwww.nextflow.io\u002Fabout-us.html)\n- **NCBI SRA Toolkit:** Public Domain (https:\u002F\u002Fgithub.com\u002Fncbi\u002Fsra-tools\u002Fblob\u002Fmaster\u002FLICENSE)\n",{"data":41,"body":42},{"name":4,"description":6},{"type":43,"children":44},"root",[45,54,60,71,78,91,95,101,109,122,130,289,299,314,319,322,328,336,355,360,367,451,457,510,516,553,570,573,579,587,699,704,728,733,760,763,769,777,867,936,941,989,999,1002,1008,1014,1062,1067,1090,1100,1106,1151,1157,1165,1190,1198,1229,1237,1260,1263,1269,1275,1355,1360,1366,1374,1426,1432,1589,1597,1652,1660,1720,1723,1729,1735,1777,1783,1790,1815,1822,1847,1854,1879,1882,1888,1898,1904,1947,1950,1956,2014,2017,2023,2028,2033,2038,2044,2057,2063,2118],{"type":46,"tag":47,"props":48,"children":50},"element","h1",{"id":49},"nf-core-pipeline-deployment",[51],{"type":52,"value":53},"text","nf-core Pipeline Deployment",{"type":46,"tag":55,"props":56,"children":57},"p",{},[58],{"type":52,"value":59},"Run nf-core bioinformatics pipelines on local or public sequencing data.",{"type":46,"tag":55,"props":61,"children":62},{},[63,69],{"type":46,"tag":64,"props":65,"children":66},"strong",{},[67],{"type":52,"value":68},"Target users:",{"type":52,"value":70}," Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.",{"type":46,"tag":72,"props":73,"children":75},"h2",{"id":74},"workflow-checklist",[76],{"type":52,"value":77},"Workflow Checklist",{"type":46,"tag":79,"props":80,"children":84},"pre",{"className":81,"code":83,"language":52},[82],"language-text","- [ ] Step 0: Acquire data (if from GEO\u002FSRA)\n- [ ] Step 1: Environment check (MUST pass)\n- [ ] Step 2: Select pipeline (confirm with user)\n- [ ] Step 3: Run test profile (MUST pass)\n- [ ] Step 4: Create samplesheet\n- [ ] Step 5: Configure & run (confirm genome with user)\n- [ ] Step 6: Verify outputs\n",[85],{"type":46,"tag":86,"props":87,"children":89},"code",{"__ignoreMap":88},"",[90],{"type":52,"value":83},{"type":46,"tag":92,"props":93,"children":94},"hr",{},[],{"type":46,"tag":72,"props":96,"children":98},{"id":97},"step-0-acquire-data-geosra-only",[99],{"type":52,"value":100},"Step 0: Acquire Data (GEO\u002FSRA Only)",{"type":46,"tag":55,"props":102,"children":103},{},[104],{"type":46,"tag":64,"props":105,"children":106},{},[107],{"type":52,"value":108},"Skip this step if user has local FASTQ files.",{"type":46,"tag":55,"props":110,"children":111},{},[112,114,120],{"type":52,"value":113},"For public datasets, fetch from GEO\u002FSRA first. 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Reference",{"type":46,"tag":55,"props":1889,"children":1890},{},[1891,1893,1897],{"type":52,"value":1892},"For common exit codes and fixes, see ",{"type":46,"tag":115,"props":1894,"children":1895},{"href":565},[1896],{"type":52,"value":565},{"type":52,"value":569},{"type":46,"tag":361,"props":1899,"children":1901},{"id":1900},"resume-failed-run",[1902],{"type":52,"value":1903},"Resume failed run",{"type":46,"tag":79,"props":1905,"children":1907},{"className":132,"code":1906,"language":134,"meta":88,"style":88},"nextflow run nf-core\u002F\u003Cpipeline> -resume\n",[1908],{"type":46,"tag":86,"props":1909,"children":1910},{"__ignoreMap":88},[1911],{"type":46,"tag":140,"props":1912,"children":1913},{"class":142,"line":143},[1914,1918,1922,1926,1930,1934,1938,1942],{"type":46,"tag":140,"props":1915,"children":1916},{"style":157},[1917],{"type":52,"value":15},{"type":46,"tag":140,"props":1919,"children":1920},{"style":163},[1921],{"type":52,"value":795},{"type":46,"tag":140,"props":1923,"children":1924},{"style":163},[1925],{"type":52,"value":800},{"type":46,"tag":140,"props":1927,"children":1928},{"style":803},[1929],{"type":52,"value":806},{"type":46,"tag":140,"props":1931,"children":1932},{"style":163},[1933],{"type":52,"value":811},{"type":46,"tag":140,"props":1935,"children":1936},{"style":814},[1937],{"type":52,"value":817},{"type":46,"tag":140,"props":1939,"children":1940},{"style":803},[1941],{"type":52,"value":822},{"type":46,"tag":140,"props":1943,"children":1944},{"style":163},[1945],{"type":52,"value":1946}," -resume\n",{"type":46,"tag":92,"props":1948,"children":1949},{},[],{"type":46,"tag":72,"props":1951,"children":1953},{"id":1952},"references",[1954],{"type":52,"value":1955},"References",{"type":46,"tag":300,"props":1957,"children":1958},{},[1959,1968,1977,1987,1996,2005],{"type":46,"tag":304,"props":1960,"children":1961},{},[1962,1966],{"type":46,"tag":115,"props":1963,"children":1964},{"href":117},[1965],{"type":52,"value":117},{"type":52,"value":1967}," - Downloading public GEO\u002FSRA data",{"type":46,"tag":304,"props":1969,"children":1970},{},[1971,1975],{"type":46,"tag":115,"props":1972,"children":1973},{"href":565},[1974],{"type":52,"value":565},{"type":52,"value":1976}," - Common issues and fixes",{"type":46,"tag":304,"props":1978,"children":1979},{},[1980,1985],{"type":46,"tag":115,"props":1981,"children":1983},{"href":1982},"references\u002Finstallation.md",[1984],{"type":52,"value":1982},{"type":52,"value":1986}," - Environment setup",{"type":46,"tag":304,"props":1988,"children":1989},{},[1990,1994],{"type":46,"tag":115,"props":1991,"children":1992},{"href":741},[1993],{"type":52,"value":741},{"type":52,"value":1995}," - RNA-seq pipeline details",{"type":46,"tag":304,"props":1997,"children":1998},{},[1999,2003],{"type":46,"tag":115,"props":2000,"children":2001},{"href":749},[2002],{"type":52,"value":749},{"type":52,"value":2004}," - Variant calling details",{"type":46,"tag":304,"props":2006,"children":2007},{},[2008,2012],{"type":46,"tag":115,"props":2009,"children":2010},{"href":757},[2011],{"type":52,"value":757},{"type":52,"value":2013}," - ATAC-seq details",{"type":46,"tag":92,"props":2015,"children":2016},{},[],{"type":46,"tag":72,"props":2018,"children":2020},{"id":2019},"disclaimer",[2021],{"type":52,"value":2022},"Disclaimer",{"type":46,"tag":55,"props":2024,"children":2025},{},[2026],{"type":52,"value":2027},"This skill is provided as a prototype example demonstrating how to integrate nf-core bioinformatics pipelines into Claude Code for automated analysis workflows. The current implementation supports three pipelines (rnaseq, sarek, and atacseq), serving as a foundation that enables the community to expand support to the full set of nf-core pipelines.",{"type":46,"tag":55,"props":2029,"children":2030},{},[2031],{"type":52,"value":2032},"It is intended for educational and research purposes and should not be considered production-ready without appropriate validation for your specific use case. Users are responsible for ensuring their computing environment meets pipeline requirements and for verifying analysis results.",{"type":46,"tag":55,"props":2034,"children":2035},{},[2036],{"type":52,"value":2037},"Anthropic does not guarantee the accuracy of bioinformatics outputs, and users should follow standard practices for validating computational analyses. This integration is not officially endorsed by or affiliated with the nf-core community.",{"type":46,"tag":72,"props":2039,"children":2041},{"id":2040},"attribution",[2042],{"type":52,"value":2043},"Attribution",{"type":46,"tag":55,"props":2045,"children":2046},{},[2047,2049,2055],{"type":52,"value":2048},"When publishing results, cite the appropriate pipeline. Citations are available in each nf-core repository's CITATIONS.md file (e.g., ",{"type":46,"tag":115,"props":2050,"children":2053},{"href":2051,"rel":2052},"https:\u002F\u002Fgithub.com\u002Fnf-core\u002Frnaseq\u002Fblob\u002F3.22.2\u002FCITATIONS.md",[412],[2054],{"type":52,"value":2051},{"type":52,"value":2056},").",{"type":46,"tag":72,"props":2058,"children":2060},{"id":2059},"licenses",[2061],{"type":52,"value":2062},"Licenses",{"type":46,"tag":300,"props":2064,"children":2065},{},[2066,2084,2101],{"type":46,"tag":304,"props":2067,"children":2068},{},[2069,2074,2076,2082],{"type":46,"tag":64,"props":2070,"children":2071},{},[2072],{"type":52,"value":2073},"nf-core pipelines:",{"type":52,"value":2075}," MIT License (",{"type":46,"tag":115,"props":2077,"children":2080},{"href":2078,"rel":2079},"https:\u002F\u002Fnf-co.re\u002Fabout",[412],[2081],{"type":52,"value":2078},{"type":52,"value":2083},")",{"type":46,"tag":304,"props":2085,"children":2086},{},[2087,2092,2094,2100],{"type":46,"tag":64,"props":2088,"children":2089},{},[2090],{"type":52,"value":2091},"Nextflow:",{"type":52,"value":2093}," Apache License, Version 2.0 (",{"type":46,"tag":115,"props":2095,"children":2098},{"href":2096,"rel":2097},"https:\u002F\u002Fwww.nextflow.io\u002Fabout-us.html",[412],[2099],{"type":52,"value":2096},{"type":52,"value":2083},{"type":46,"tag":304,"props":2102,"children":2103},{},[2104,2109,2111,2117],{"type":46,"tag":64,"props":2105,"children":2106},{},[2107],{"type":52,"value":2108},"NCBI SRA Toolkit:",{"type":52,"value":2110}," Public Domain (",{"type":46,"tag":115,"props":2112,"children":2115},{"href":2113,"rel":2114},"https:\u002F\u002Fgithub.com\u002Fncbi\u002Fsra-tools\u002Fblob\u002Fmaster\u002FLICENSE",[412],[2116],{"type":52,"value":2113},{"type":52,"value":2083},{"type":46,"tag":2119,"props":2120,"children":2121},"style",{},[2122],{"type":52,"value":2123},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":2125,"total":2312},[2126,2147,2161,2173,2192,2205,2226,2246,2260,2275,2283,2296],{"slug":2127,"name":2127,"fn":2128,"description":2129,"org":2130,"tags":2131,"stars":2144,"repoUrl":2145,"updatedAt":2146},"algorithmic-art","create algorithmic art with p5.js","Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2132,2135,2138,2141],{"name":2133,"slug":2134,"type":16},"Creative","creative",{"name":2136,"slug":2137,"type":16},"Design","design",{"name":2139,"slug":2140,"type":16},"Generative Art","generative-art",{"name":2142,"slug":2143,"type":16},"JavaScript","javascript",161831,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fskills","2026-04-06T17:56:15.455818",{"slug":2148,"name":2148,"fn":2149,"description":2150,"org":2151,"tags":2152,"stars":2144,"repoUrl":2145,"updatedAt":2160},"brand-guidelines","apply Anthropic brand colors and typography","Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2153,2156,2157],{"name":2154,"slug":2155,"type":16},"Branding","branding",{"name":2136,"slug":2137,"type":16},{"name":2158,"slug":2159,"type":16},"Typography","typography","2026-04-06T17:56:05.042852",{"slug":2162,"name":2162,"fn":2163,"description":2164,"org":2165,"tags":2166,"stars":2144,"repoUrl":2145,"updatedAt":2172},"canvas-design","create posters and visual art as PNG or PDF","Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2167,2168,2169],{"name":2133,"slug":2134,"type":16},{"name":2136,"slug":2137,"type":16},{"name":2170,"slug":2171,"type":16},"PDF","pdf","2026-04-06T17:56:03.794732",{"slug":2174,"name":2174,"fn":2175,"description":2176,"org":2177,"tags":2178,"stars":2144,"repoUrl":2145,"updatedAt":2191},"claude-api","build apps with the Claude API","Reference for the Claude API \u002F Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude\u002FAnthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing\u002Fmodel choice\u002Flimits\u002Fcaching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent\u002FMCP\u002Ftool-definition\u002Fmulti-agent\u002FRAG\u002FLLM-judge\u002Fcomputer-use; generate\u002Fsummarize\u002Fextract\u002Fclassify\u002Frewrite\u002Fconverse over NL; debugging refusals\u002Fcutoffs\u002Fstreaming\u002Ftool-calls\u002Ftokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI\u002FGPT\u002FGemini\u002FLlama\u002FMistral\u002FCohere\u002FOllama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2179,2182,2183,2186,2188],{"name":2180,"slug":2181,"type":16},"Agents","agents",{"name":9,"slug":8,"type":16},{"name":2184,"slug":2185,"type":16},"Anthropic SDK","anthropic-sdk",{"name":2187,"slug":2174,"type":16},"Claude API",{"name":2189,"slug":2190,"type":16},"LLM","llm","2026-07-28T05:36:08.213335",{"slug":2193,"name":2193,"fn":2194,"description":2195,"org":2196,"tags":2197,"stars":2144,"repoUrl":2145,"updatedAt":2204},"doc-coauthoring","co-author documentation and technical specs","Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2198,2201],{"name":2199,"slug":2200,"type":16},"Documentation","documentation",{"name":2202,"slug":2203,"type":16},"Technical Writing","technical-writing","2026-04-06T17:56:14.18897",{"slug":2206,"name":2206,"fn":2207,"description":2208,"org":2209,"tags":2210,"stars":2144,"repoUrl":2145,"updatedAt":2225},"docx","create and edit Word documents","Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2211,2214,2216,2219,2222],{"name":2212,"slug":2213,"type":16},"Documents","documents",{"name":2215,"slug":2206,"type":16},"DOCX",{"name":2217,"slug":2218,"type":16},"Office","office",{"name":2220,"slug":2221,"type":16},"Templates","templates",{"name":2223,"slug":2224,"type":16},"Word","word","2026-07-18T05:16:23.136271",{"slug":2227,"name":2227,"fn":2228,"description":2229,"org":2230,"tags":2231,"stars":2144,"repoUrl":2145,"updatedAt":2245},"frontend-design","design production-grade frontend interfaces","Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2232,2233,2236,2239,2242],{"name":2136,"slug":2137,"type":16},{"name":2234,"slug":2235,"type":16},"Frontend","frontend",{"name":2237,"slug":2238,"type":16},"React","react",{"name":2240,"slug":2241,"type":16},"Tailwind CSS","tailwind-css",{"name":2243,"slug":2244,"type":16},"UI Components","ui-components","2026-04-06T17:56:16.723469",{"slug":2247,"name":2247,"fn":2248,"description":2249,"org":2250,"tags":2251,"stars":2144,"repoUrl":2145,"updatedAt":2259},"internal-comms","write internal company communications","A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2252,2255,2256],{"name":2253,"slug":2254,"type":16},"Communications","communications",{"name":2220,"slug":2221,"type":16},{"name":2257,"slug":2258,"type":16},"Writing","writing","2026-04-06T17:56:20.695522",{"slug":2261,"name":2261,"fn":2262,"description":2263,"org":2264,"tags":2265,"stars":2144,"repoUrl":2145,"updatedAt":2274},"mcp-builder","build MCP servers","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node\u002FTypeScript (MCP SDK).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2266,2267,2270,2271],{"name":2180,"slug":2181,"type":16},{"name":2268,"slug":2269,"type":16},"API Development","api-development",{"name":2189,"slug":2190,"type":16},{"name":2272,"slug":2273,"type":16},"MCP","mcp","2026-04-06T17:56:10.357665",{"slug":2171,"name":2171,"fn":2276,"description":2277,"org":2278,"tags":2279,"stars":2144,"repoUrl":2145,"updatedAt":2282},"read edit and manipulate PDF files","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2280,2281],{"name":2212,"slug":2213,"type":16},{"name":2170,"slug":2171,"type":16},"2026-04-06T17:56:02.483316",{"slug":2284,"name":2284,"fn":2285,"description":2286,"org":2287,"tags":2288,"stars":2144,"repoUrl":2145,"updatedAt":2295},"pptx","create and edit PowerPoint presentations","Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2289,2292],{"name":2290,"slug":2291,"type":16},"PowerPoint","powerpoint",{"name":2293,"slug":2294,"type":16},"Presentations","presentations","2026-07-18T05:16:24.1471",{"slug":2297,"name":2297,"fn":2298,"description":2299,"org":2300,"tags":2301,"stars":2144,"repoUrl":2145,"updatedAt":2311},"skill-creator","create and optimize agent skills","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2302,2303,2304,2307,2310],{"name":2180,"slug":2181,"type":16},{"name":2199,"slug":2200,"type":16},{"name":2305,"slug":2306,"type":16},"Evals","evals",{"name":2308,"slug":2309,"type":16},"Performance","performance",{"name":2202,"slug":2203,"type":16},"2026-04-19T06:45:40.804",490,{"items":2314,"total":235},[2315,2332,2349,2357,2368,2385],{"slug":2316,"name":2316,"fn":2317,"description":2318,"org":2319,"tags":2320,"stars":29,"repoUrl":30,"updatedAt":2331},"clinical-trial-protocol-skill","generate clinical trial protocols for FDA submissions","Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say \"Create a clinical trial protocol\", \"Generate protocol for [device\u002Fdrug]\", \"Help me design a clinical study\", \"Research similar trials for [intervention]\", or when developing FDA submission documentation for investigational products.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2321,2324,2327,2328],{"name":2322,"slug":2323,"type":16},"Clinical Trials","clinical-trials",{"name":2325,"slug":2326,"type":16},"FDA","fda",{"name":21,"slug":22,"type":16},{"name":2329,"slug":2330,"type":16},"Regulatory Compliance","regulatory-compliance","2026-04-06T17:57:05.297219",{"slug":2333,"name":2333,"fn":2334,"description":2335,"org":2336,"tags":2337,"stars":29,"repoUrl":30,"updatedAt":2348},"instrument-data-to-allotrope","convert instrument data to Allotrope ASM","Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS\u002FELN systems, or generating parser code for production pipelines.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2338,2341,2344,2347],{"name":2339,"slug":2340,"type":16},"Allotrope","allotrope",{"name":2342,"slug":2343,"type":16},"Data Cleaning","data-cleaning",{"name":2345,"slug":2346,"type":16},"Laboratory","laboratory",{"name":21,"slug":22,"type":16},"2026-04-06T17:57:07.996807",{"slug":4,"name":4,"fn":5,"description":6,"org":2350,"tags":2351,"stars":29,"repoUrl":30,"updatedAt":31},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2352,2353,2354,2355,2356],{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":21,"slug":22,"type":16},{"name":14,"slug":15,"type":16},{"name":27,"slug":28,"type":16},{"slug":2358,"name":2358,"fn":2359,"description":2360,"org":2361,"tags":2362,"stars":29,"repoUrl":30,"updatedAt":2367},"scientific-problem-selection","guide scientific research problem selection","This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include \"I have an idea for a project\", \"I'm stuck on my research\", \"help me evaluate this project\", \"what should I work on\", or \"I need strategic advice about my research\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2363,2364],{"name":21,"slug":22,"type":16},{"name":2365,"slug":2366,"type":16},"Research","research","2026-04-06T17:57:06.62424",{"slug":2369,"name":2369,"fn":2370,"description":2371,"org":2372,"tags":2373,"stars":29,"repoUrl":30,"updatedAt":2384},"scvi-tools","analyze single-cell data with scvi-tools","Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI\u002FscANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI\u002FscArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2374,2375,2378,2379,2381],{"name":24,"slug":25,"type":16},{"name":2376,"slug":2377,"type":16},"Deep Learning","deep-learning",{"name":21,"slug":22,"type":16},{"name":2380,"slug":160,"type":16},"Python",{"name":2382,"slug":2383,"type":16},"Single-Cell","single-cell","2026-04-06T17:57:02.70437",{"slug":2386,"name":2386,"fn":2387,"description":2388,"org":2389,"tags":2390,"stars":29,"repoUrl":30,"updatedAt":2394},"single-cell-rna-qc","run quality control on single-cell RNA-seq data","Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse\u002Fscanpy best practices for single-cell analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2391,2392,2393],{"name":24,"slug":25,"type":16},{"name":21,"slug":22,"type":16},{"name":27,"slug":28,"type":16},"2026-04-06T17:57:01.442679"]