[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFENQ538F97855QXSBHDEP":3,"skill-same:01M6AFENQ538F97855QXSBHDEP":113},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6AFENQ538F97855QXSBHDEP","gget",null,"CLI\u002FPython toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl\u002FUniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST\u002Fbatch processing, use biopython. For multi-database integration, use bioservices.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"access","gene","for","advanced","quick","processing","enrichr","info","queries","databases","use","cli","bioservices","biopython","downloads","integration","batch","database","toolkit","python","2026-11-10 10:12:30",false,"---\nname: gget\ndescription: \"CLI\u002FPython toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl\u002FUniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST\u002Fbatch processing, use biopython. For multi-database integration, use bioservices.\"\n---\n\n# gget\n\n## Overview\n\ngget is a command-line bioinformatics tool and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequence analysis, protein structures, expression data, and disease associations through a consistent interface. All gget modules work both as command-line tools and as Python functions.\n\n**Important**: The databases queried by gget are continuously updated, which sometimes changes their structure. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.\n\n## Installation\n\nInstall gget in a clean virtual environment to avoid conflicts:\n\n```bash\n# Using uv (recommended)\nuv uv pip install gget\n\n# Or using pip\nuv pip install --upgrade gget\n\n# In Python\u002FJupyter\nimport gget\n```\n\n## Quick Start\n\nBasic usage pattern for all modules:\n\n```bash\n# Command-line\ngget \u003Cmodule> [arguments] [options]\n\n# Python\ngget.module(arguments, options)\n```\n\nMost modules return:\n- **Command-line**: JSON (default) or CSV with `-csv` flag\n- **Python**: DataFrame or dictionary\n\nCommon flags across modules:\n- `-o\u002F--out`: Save results to file\n- `-q\u002F--quiet`: Suppress progress information\n- `-csv`: Return CSV format (command-line only)\n\n## Module Categories\n\n### 1. Reference & Gene Information\n\n#### gget ref - Reference Genome Downloads\n\nRetrieve download links and metadata for Ensembl reference genomes.\n\n**Parameters**:\n- `species`: Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'\n- `-w\u002F--which`: Specify return types (gtf, cdna, dna, cds, cdrna, pep). Default: all\n- `-r\u002F--release`: Ensembl release number (default: latest)\n- `-l\u002F--list_species`: List available vertebrate species\n- `-liv\u002F--list_iv_species`: List available invertebrate species\n- `-ftp`: Return only FTP links\n- `-d\u002F--download`: Download files (requires curl)\n\n**Examples**:\n```bash\n# List available species\ngget ref --list_species\n\n# Get all reference files for human\ngget ref homo_sapiens\n\n# Download only GTF annotation for mouse\ngget ref -w gtf -d mouse\n```\n\n```python\n# Python\ngget.ref(\"homo_sapiens\")\ngget.ref(\"mus_musculus\", which=\"gtf\", download=True)\n```\n\n#### gget search - Gene Search\n\nLocate genes by name or description across species.\n\n**Parameters**:\n- `searchwords`: One or more search terms (case-insensitive)\n- `-s\u002F--species`: Target species (e.g., 'homo_sapiens', 'mouse')\n- `-r\u002F--release`: Ensembl release number\n- `-t\u002F--id_type`: Return 'gene' (default) or 'transcript'\n- `-ao\u002F--andor`: 'or' (default) finds ANY searchword; 'and' requires ALL\n- `-l\u002F--limit`: Maximum results to return\n\n**Returns**: ensembl_id, gene_name, ensembl_description, ext_ref_description, biotype, URL\n\n**Examples**:\n```bash\n# Search for GABA-related genes in human\ngget search -s human gaba gamma-aminobutyric\n\n# Find specific gene, require all terms\ngget search -s mouse -ao and pax7 transcription\n```\n\n```python\n# Python\ngget.search([\"gaba\", \"gamma-aminobutyric\"], species=\"homo_sapiens\")\n```\n\n#### gget info - Gene\u002FTranscript Information\n\nRetrieve comprehensive gene and transcript metadata from Ensembl, UniProt, and NCBI.\n\n**Parameters**:\n- `ens_ids`: One or more Ensembl IDs (also supports WormBase, Flybase IDs). Limit: ~1000 IDs\n- `-n\u002F--ncbi`: Disable NCBI data retrieval\n- `-u\u002F--uniprot`: Disable UniProt data retrieval\n- `-pdb`: Include PDB identifiers (increases runtime)\n\n**Returns**: UniProt ID, NCBI gene ID, primary gene name, synonyms, protein names, descriptions, biotype, canonical transcript\n\n**Examples**:\n```bash\n# Get info for multiple genes\ngget info ENSG00000034713 ENSG00000104853 ENSG00000170296\n\n# Include PDB IDs\ngget info ENSG00000034713 -pdb\n```\n\n```python\n# Python\ngget.info([\"ENSG00000034713\", \"ENSG00000104853\"], pdb=True)\n```\n\n#### gget seq - Sequence Retrieval\n\nFetch nucleotide or amino acid sequences for genes and transcripts.\n\n**Parameters**:\n- `ens_ids`: One or more Ensembl identifiers\n- `-t\u002F--translate`: Fetch amino acid sequences instead of nucleotide\n- `-iso\u002F--isoforms`: Return all transcript variants (gene IDs only)\n\n**Returns**: FASTA format sequences\n\n**Examples**:\n```bash\n# Get nucleotide sequences\ngget seq ENSG00000034713 ENSG00000104853\n\n# Get all protein isoforms\ngget seq -t -iso ENSG00000034713\n```\n\n```python\n# Python\ngget.seq([\"ENSG00000034713\"], translate=True, isoforms=True)\n```\n\n### 2. Sequence Analysis & Alignment\n\n#### gget blast - BLAST Searches\n\nBLAST nucleotide or amino acid sequences against standard databases.\n\n**Parameters**:\n- `sequence`: Sequence string or path to FASTA\u002F.txt file\n- `-p\u002F--program`: blastn, blastp, blastx, tblastn, tblastx (auto-detected)\n- `-db\u002F--database`:\n  - Nucleotide: nt, refseq_rna, pdbnt\n  - Protein: nr, swissprot, pdbaa, refseq_protein\n- `-l\u002F--limit`: Max hits (default: 50)\n- `-e\u002F--expect`: E-value cutoff (default: 10.0)\n- `-lcf\u002F--low_comp_filt`: Enable low complexity filtering\n- `-mbo\u002F--megablast_off`: Disable MegaBLAST (blastn only)\n\n**Examples**:\n```bash\n# BLAST protein sequence\ngget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR\n\n# BLAST from file with specific database\ngget blast sequence.fasta -db swissprot -l 10\n```\n\n```python\n# Python\ngget.blast(\"MKWMFK...\", database=\"swissprot\", limit=10)\n```\n\n#### gget blat - BLAT Searches\n\nLocate genomic positions of sequences using UCSC BLAT.\n\n**Parameters**:\n- `sequence`: Sequence string or path to FASTA\u002F.txt file\n- `-st\u002F--seqtype`: 'DNA', 'protein', 'translated%20RNA', 'translated%20DNA' (auto-detected)\n- `-a\u002F--assembly`: Target assembly (default: 'human'\u002Fhg38; options: 'mouse'\u002Fmm39, 'zebrafinch'\u002FtaeGut2, etc.)\n\n**Returns**: genome, query size, alignment positions, matches, mismatches, alignment percentage\n\n**Examples**:\n```bash\n# Find genomic location in human\ngget blat ATCGATCGATCGATCG\n\n# Search in different assembly\ngget blat -a mm39 ATCGATCGATCGATCG\n```\n\n```python\n# Python\ngget.blat(\"ATCGATCGATCGATCG\", assembly=\"mouse\")\n```\n\n#### gget muscle - Multiple Sequence Alignment\n\nAlign multiple nucleotide or amino acid sequences using Muscle5.\n\n**Parameters**:\n- `fasta`: Sequences or path to FASTA\u002F.txt file\n- `-s5\u002F--super5`: Use Super5 algorithm for faster processing (large datasets)\n\n**Returns**: Aligned sequences in ClustalW format or aligned FASTA (.afa)\n\n**Examples**:\n```bash\n# Align sequences from file\ngget muscle sequences.fasta -o aligned.afa\n\n# Use Super5 for large dataset\ngget muscle large_dataset.fasta -s5\n```\n\n```python\n# Python\ngget.muscle(\"sequences.fasta\", save=True)\n```\n\n#### gget diamond - Local Sequence Alignment\n\nPerform fast local protein or translated DNA alignment using DIAMOND.\n\n**Parameters**:\n- Query: Sequences (string\u002Flist) or FASTA file path\n- `--reference`: Reference sequences (string\u002Flist) or FASTA file path (required)\n- `--sensitivity`: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive\n- `--threads`: CPU threads (default: 1)\n- `--diamond_db`: Save database for reuse\n- `--translated`: Enable nucleotide-to-amino acid alignment\n\n**Returns**: Identity percentage, sequence lengths, match positions, gap openings, E-values, bit scores\n\n**Examples**:\n```bash\n# Align against reference\ngget diamond GGETISAWESQME -ref reference.fasta --threads 4\n\n# Save database for reuse\ngget diamond query.fasta -ref ref.fasta --diamond_db my_db.dmnd\n```\n\n```python\n# Python\ngget.diamond(\"GGETISAWESQME\", reference=\"reference.fasta\", threads=4)\n```\n\n### 3. Structural & Protein Analysis\n\n#### gget pdb - Protein Structures\n\nQuery RCSB Protein Data Bank for structure and metadata.\n\n**Parameters**:\n- `pdb_id`: PDB identifier (e.g., '7S7U')\n- `-r\u002F--resource`: Data type (pdb, entry, pubmed, assembly, entity types)\n- `-i\u002F--identifier`: Assembly, entity, or chain ID\n\n**Returns**: PDB format (structures) or JSON (metadata)\n\n**Examples**:\n```bash\n# Download PDB structure\ngget pdb 7S7U -o 7S7U.pdb\n\n# Get metadata\ngget pdb 7S7U -r entry\n```\n\n```python\n# Python\ngget.pdb(\"7S7U\", save=True)\n```\n\n#### gget alphafold - Protein Structure Prediction\n\nPredict 3D protein structures using simplified AlphaFold2.\n\n**Setup Required**:\n```bash\n# Install OpenMM first\nuv pip install openmm\n\n# Then setup AlphaFold\ngget setup alphafold\n```\n\n**Parameters**:\n- `sequence`: Amino acid sequence (string), multiple sequences (list), or FASTA file. Multiple sequences trigger multimer modeling\n- `-mr\u002F--multimer_recycles`: Recycling iterations (default: 3; recommend 20 for accuracy)\n- `-mfm\u002F--multimer_for_monomer`: Apply multimer model to single proteins\n- `-r\u002F--relax`: AMBER relaxation for top-ranked model\n- `plot`: Python-only; generate interactive 3D visualization (default: True)\n- `show_sidechains`: Python-only; include side chains (default: True)\n\n**Returns**: PDB structure file, JSON alignment error data, optional 3D visualization\n\n**Examples**:\n```bash\n# Predict single protein structure\ngget alphafold MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR\n\n# Predict multimer with higher accuracy\ngget alphafold sequence1.fasta -mr 20 -r\n```\n\n```python\n# Python with visualization\ngget.alphafold(\"MKWMFK...\", plot=True, show_sidechains=True)\n\n# Multimer prediction\ngget.alphafold([\"sequence1\", \"sequence2\"], multimer_recycles=20)\n```\n\n#### gget elm - Eukaryotic Linear Motifs\n\nPredict Eukaryotic Linear Motifs in protein sequences.\n\n**Setup Required**:\n```bash\ngget setup elm\n```\n\n**Parameters**:\n- `sequence`: Amino acid sequence or UniProt Acc\n- `-u\u002F--uniprot`: Indicates sequence is UniProt Acc\n- `-e\u002F--expand`: Include protein names, organisms, references\n- `-s\u002F--sensitivity`: DIAMOND alignment sensitivity (default: \"very-sensitive\")\n- `-t\u002F--threads`: Number of threads (default: 1)\n\n**Returns**: Two outputs:\n1. **ortholog_df**: Linear motifs from orthologous proteins\n2. **regex_df**: Motifs directly matched in input sequence\n\n**Examples**:\n```bash\n# Predict motifs from sequence\ngget elm LIAQSIGQASFV -o results\n\n# Use UniProt accession with expanded info\ngget elm --uniprot Q02410 -e\n```\n\n```python\n# Python\northolog_df, regex_df = gget.elm(\"LIAQSIGQASFV\")\n```\n\n### 4. Expression & Disease Data\n\n#### gget archs4 - Gene Correlation & Tissue Expression\n\nQuery ARCHS4 database for correlated genes or tissue expression data.\n\n**Parameters**:\n- `gene`: Gene symbol or Ensembl ID (with `--ensembl` flag)\n- `-w\u002F--which`: 'correlation' (default, returns 100 most correlated genes) or 'tissue' (expression atlas)\n- `-s\u002F--species`: 'human' (default) or 'mouse' (tissue data only)\n- `-e\u002F--ensembl`: Input is Ensembl ID\n\n**Returns**:\n- **Correlation mode**: Gene symbols, Pearson correlation coefficients\n- **Tissue mode**: Tissue identifiers, min\u002FQ1\u002Fmedian\u002FQ3\u002Fmax expression values\n\n**Examples**:\n```bash\n# Get correlated genes\ngget archs4 ACE2\n\n# Get tissue expression\ngget archs4 -w tissue ACE2\n```\n\n```python\n# Python\ngget.archs4(\"ACE2\", which=\"tissue\")\n```\n\n#### gget cellxgene - Single-Cell RNA-seq Data\n\nQuery CZ CELLxGENE Discover Census for single-cell data.\n\n**Setup Required**:\n```bash\ngget setup cellxgene\n```\n\n**Parameters**:\n- `--gene` (-g): Gene names or Ensembl IDs (case-sensitive! 'PAX7' for human, 'Pax7' for mouse)\n- `--tissue`: Tissue type(s)\n- `--cell_type`: Specific cell type(s)\n- `--species` (-s): 'homo_sapiens' (default) or 'mus_musculus'\n- `--census_version` (-cv): Version (\"stable\", \"latest\", or dated)\n- `--ensembl` (-e): Use Ensembl IDs\n- `--meta_only` (-mo): Return metadata only\n- Additional filters: disease, development_stage, sex, assay, dataset_id, donor_id, ethnicity, suspension_type\n\n**Returns**: AnnData object with count matrices and metadata (or metadata-only dataframes)\n\n**Examples**:\n```bash\n# Get single-cell data for specific genes and cell types\ngget cellxgene --gene ACE2 ABCA1 --tissue lung --cell_type \"mucus secreting cell\" -o lung_data.h5ad\n\n# Metadata only\ngget cellxgene --gene PAX7 --tissue muscle --meta_only -o metadata.csv\n```\n\n```python\n# Python\nadata = gget.cellxgene(gene=[\"ACE2\", \"ABCA1\"], tissue=\"lung\", cell_type=\"mucus secreting cell\")\n```\n\n#### gget enrichr - Enrichment Analysis\n\nPerform ontology enrichment analysis on gene lists using Enrichr.\n\n**Parameters**:\n- `genes`: Gene symbols or Ensembl IDs\n- `-db\u002F--database`: Reference database (supports shortcuts: 'pathway', 'transcription', 'ontology', 'diseases_drugs', 'celltypes')\n- `-s\u002F--species`: human (default), mouse, fly, yeast, worm, fish\n- `-bkg_l\u002F--background_list`: Background genes for comparison\n- `-ko\u002F--kegg_out`: Save KEGG pathway images with highlighted genes\n- `plot`: Python-only; generate graphical results\n\n**Database Shortcuts**:\n- 'pathway' → KEGG_2021_Human\n- 'transcription' → ChEA_2016\n- 'ontology' → GO_Biological_Process_2021\n- 'diseases_drugs' → GWAS_Catalog_2019\n- 'celltypes' → PanglaoDB_Augmented_2021\n\n**Examples**:\n```bash\n# Enrichment analysis for ontology\ngget enrichr -db ontology ACE2 AGT AGTR1\n\n# Save KEGG pathways\ngget enrichr -db pathway ACE2 AGT AGTR1 -ko .\u002Fkegg_images\u002F\n```\n\n```python\n# Python with plot\ngget.enrichr([\"ACE2\", \"AGT\", \"AGTR1\"], database=\"ontology\", plot=True)\n```\n\n#### gget bgee - Orthology & Expression\n\nRetrieve orthology and gene expression data from Bgee database.\n\n**Parameters**:\n- `ens_id`: Ensembl gene ID or NCBI gene ID (for non-Ensembl species). Multiple IDs supported when `type=expression`\n- `-t\u002F--type`: 'orthologs' (default) or 'expression'\n\n**Returns**:\n- **Orthologs mode**: Matching genes across species with IDs, names, taxonomic info\n- **Expression mode**: Anatomical entities, confidence scores, expression status\n\n**Examples**:\n```bash\n# Get orthologs\ngget bgee ENSG00000169194\n\n# Get expression data\ngget bgee ENSG00000169194 -t expression\n\n# Multiple genes\ngget bgee ENSBTAG00000047356 ENSBTAG00000018317 -t expression\n```\n\n```python\n# Python\ngget.bgee(\"ENSG00000169194\", type=\"orthologs\")\n```\n\n#### gget opentargets - Disease & Drug Associations\n\nRetrieve disease and drug associations from OpenTargets.\n\n**Parameters**:\n- Ensembl gene ID (required)\n- `-r\u002F--resource`: diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions\n- `-l\u002F--limit`: Cap results count\n- Filter arguments (vary by resource):\n  - drugs: `--filter_disease`\n  - pharmacogenetics: `--filter_drug`\n  - expression\u002Fdepmap: `--filter_tissue`, `--filter_anat_sys`, `--filter_organ`\n  - interactions: `--filter_protein_a`, `--filter_protein_b`, `--filter_gene_b`\n\n**Examples**:\n```bash\n# Get associated diseases\ngget opentargets ENSG00000169194 -r diseases -l 5\n\n# Get associated drugs\ngget opentargets ENSG00000169194 -r drugs -l 10\n\n# Get tissue expression\ngget opentargets ENSG00000169194 -r expression --filter_tissue brain\n```\n\n```python\n# Python\ngget.opentargets(\"ENSG00000169194\", resource=\"diseases\", limit=5)\n```\n\n#### gget cbio - cBioPortal Cancer Genomics\n\nPlot cancer genomics heatmaps using cBioPortal data.\n\n**Two subcommands**:\n\n**search** - Find study IDs:\n```bash\ngget cbio search breast lung\n```\n\n**plot** - Generate heatmaps:\n\n**Parameters**:\n- `-s\u002F--study_ids`: Space-separated cBioPortal study IDs (required)\n- `-g\u002F--genes`: Space-separated gene names or Ensembl IDs (required)\n- `-st\u002F--stratification`: Column to organize data (tissue, cancer_type, cancer_type_detailed, study_id, sample)\n- `-vt\u002F--variation_type`: Data type (mutation_occurrences, cna_nonbinary, sv_occurrences, cna_occurrences, Consequence)\n- `-f\u002F--filter`: Filter by column value (e.g., 'study_id:msk_impact_2017')\n- `-dd\u002F--data_dir`: Cache directory (default: .\u002Fgget_cbio_cache)\n- `-fd\u002F--figure_dir`: Output directory (default: .\u002Fgget_cbio_figures)\n- `-dpi`: Resolution (default: 100)\n- `-sh\u002F--show`: Display plot in window\n- `-nc\u002F--no_confirm`: Skip download confirmations\n\n**Examples**:\n```bash\n# Search for studies\ngget cbio search esophag ovary\n\n# Create heatmap\ngget cbio plot -s msk_impact_2017 -g AKT1 ALK BRAF -st tissue -vt mutation_occurrences\n```\n\n```python\n# Python\ngget.cbio_search([\"esophag\", \"ovary\"])\ngget.cbio_plot([\"msk_impact_2017\"], [\"AKT1\", \"ALK\"], stratification=\"tissue\")\n```\n\n#### gget cosmic - COSMIC Database\n\nSearch COSMIC (Catalogue Of Somatic Mutations In Cancer) database.\n\n**Important**: License fees apply for commercial use. Requires COSMIC account credentials.\n\n**Parameters**:\n- `searchterm`: Gene name, Ensembl ID, mutation notation, or sample ID\n- `-ctp\u002F--cosmic_tsv_path`: Path to downloaded COSMIC TSV file (required for querying)\n- `-l\u002F--limit`: Maximum results (default: 100)\n\n**Database download flags**:\n- `-d\u002F--download_cosmic`: Activate download mode\n- `-gm\u002F--gget_mutate`: Create version for gget mutate\n- `-cp\u002F--cosmic_project`: Database type (cancer, census, cell_line, resistance, genome_screen, targeted_screen)\n- `-cv\u002F--cosmic_version`: COSMIC version\n- `-gv\u002F--grch_version`: Human reference genome (37 or 38)\n- `--email`, `--password`: COSMIC credentials\n\n**Examples**:\n```bash\n# First download database\ngget cosmic -d --email user@example.com --password xxx -cp cancer\n\n# Then query\ngget cosmic EGFR -ctp cosmic_data.tsv -l 10\n```\n\n```python\n# Python\ngget.cosmic(\"EGFR\", cosmic_tsv_path=\"cosmic_data.tsv\", limit=10)\n```\n\n### 5. Additional Tools\n\n#### gget mutate - Generate Mutated Sequences\n\nGenerate mutated nucleotide sequences from mutation annotations.\n\n**Parameters**:\n- `sequences`: FASTA file path or direct sequence input (string\u002Flist)\n- `-m\u002F--mutations`: CSV\u002FTSV file or DataFrame with mutation data (required)\n- `-mc\u002F--mut_column`: Mutation column name (default: 'mutation')\n- `-sic\u002F--seq_id_column`: Sequence ID column (default: 'seq_ID')\n- `-mic\u002F--mut_id_column`: Mutation ID column\n- `-k\u002F--k`: Length of flanking sequences (default: 30 nucleotides)\n\n**Returns**: Mutated sequences in FASTA format\n\n**Examples**:\n```bash\n# Single mutation\ngget mutate ATCGCTAAGCT -m \"c.4G>T\"\n\n# Multiple sequences with mutations from file\ngget mutate sequences.fasta -m mutations.csv -o mutated.fasta\n```\n\n```python\n# Python\nimport pandas as pd\nmutations_df = pd.DataFrame({\"seq_ID\": [\"seq1\"], \"mutation\": [\"c.4G>T\"]})\ngget.mutate([\"ATCGCTAAGCT\"], mutations=mutations_df)\n```\n\n#### gget gpt - OpenAI Text Generation\n\nGenerate natural language text using OpenAI's API.\n\n**Setup Required**:\n```bash\ngget setup gpt\n```\n\n**Important**: Free tier limited to 3 months after account creation. Set monthly billing limits.\n\n**Parameters**:\n- `prompt`: Text input for generation (required)\n- `api_key`: OpenAI authentication (required)\n- Model configuration: temperature, top_p, max_tokens, frequency_penalty, presence_penalty\n- Default model: gpt-3.5-turbo (configurable)\n\n**Examples**:\n```bash\ngget gpt \"Explain CRISPR\" --api_key your_key_here\n```\n\n```python\n# Python\ngget.gpt(\"Explain CRISPR\", api_key=\"your_key_here\")\n```\n\n#### gget setup - Install Dependencies\n\nInstall\u002Fdownload third-party dependencies for specific modules.\n\n**Parameters**:\n- `module`: Module name requiring dependency installation\n- `-o\u002F--out`: Output folder path (elm module only)\n\n**Modules requiring setup**:\n- `alphafold` - Downloads ~4GB of model parameters\n- `cellxgene` - Installs cellxgene-census (may not support latest Python)\n- `elm` - Downloads local ELM database\n- `gpt` - Configures OpenAI integration\n\n**Examples**:\n```bash\n# Setup AlphaFold\ngget setup alphafold\n\n# Setup ELM with custom directory\ngget setup elm -o \u002Fpath\u002Fto\u002Felm_data\n```\n\n```python\n# Python\ngget.setup(\"alphafold\")\n```\n\n## Common Workflows\n\n### Workflow 1: Gene Discovery to Sequence Analysis\n\nFind and analyze genes of interest:\n\n```python\n# 1. Search for genes\nresults = gget.search([\"GABA\", \"receptor\"], species=\"homo_sapiens\")\n\n# 2. Get detailed information\ngene_ids = results[\"ensembl_id\"].tolist()\ninfo = gget.info(gene_ids[:5])\n\n# 3. Retrieve sequences\nsequences = gget.seq(gene_ids[:5], translate=True)\n```\n\n### Workflow 2: Sequence Alignment and Structure\n\nAlign sequences and predict structures:\n\n```python\n# 1. Align multiple sequences\nalignment = gget.muscle(\"sequences.fasta\")\n\n# 2. Find similar sequences\nblast_results = gget.blast(my_sequence, database=\"swissprot\", limit=10)\n\n# 3. Predict structure\nstructure = gget.alphafold(my_sequence, plot=True)\n\n# 4. Find linear motifs\northolog_df, regex_df = gget.elm(my_sequence)\n```\n\n### Workflow 3: Gene Expression and Enrichment\n\nAnalyze expression patterns and functional enrichment:\n\n```python\n# 1. Get tissue expression\ntissue_expr = gget.archs4(\"ACE2\", which=\"tissue\")\n\n# 2. Find correlated genes\ncorrelated = gget.archs4(\"ACE2\", which=\"correlation\")\n\n# 3. Get single-cell data\nadata = gget.cellxgene(gene=[\"ACE2\"], tissue=\"lung\", cell_type=\"epithelial cell\")\n\n# 4. Perform enrichment analysis\ngene_list = correlated[\"gene_symbol\"].tolist()[:50]\nenrichment = gget.enrichr(gene_list, database=\"ontology\", plot=True)\n```\n\n### Workflow 4: Disease and Drug Analysis\n\nInvestigate disease associations and therapeutic targets:\n\n```python\n# 1. Search for genes\ngenes = gget.search([\"breast cancer\"], species=\"homo_sapiens\")\n\n# 2. Get disease associations\ndiseases = gget.opentargets(\"ENSG00000169194\", resource=\"diseases\")\n\n# 3. Get drug associations\ndrugs = gget.opentargets(\"ENSG00000169194\", resource=\"drugs\")\n\n# 4. Query cancer genomics data\nstudy_ids = gget.cbio_search([\"breast\"])\ngget.cbio_plot(study_ids[:2], [\"BRCA1\", \"BRCA2\"], stratification=\"cancer_type\")\n\n# 5. Search COSMIC for mutations\ncosmic_results = gget.cosmic(\"BRCA1\", cosmic_tsv_path=\"cosmic.tsv\")\n```\n\n### Workflow 5: Comparative Genomics\n\nCompare proteins across species:\n\n```python\n# 1. Get orthologs\northologs = gget.bgee(\"ENSG00000169194\", type=\"orthologs\")\n\n# 2. Get sequences for comparison\nhuman_seq = gget.seq(\"ENSG00000169194\", translate=True)\nmouse_seq = gget.seq(\"ENSMUSG00000026091\", translate=True)\n\n# 3. Align sequences\nalignment = gget.muscle([human_seq, mouse_seq])\n\n# 4. Compare structures\nhuman_structure = gget.pdb(\"7S7U\")\nmouse_structure = gget.alphafold(mouse_seq)\n```\n\n### Workflow 6: Building Reference Indices\n\nPrepare reference data for downstream analysis (e.g., kallisto|bustools):\n\n```bash\n# 1. List available species\ngget ref --list_species\n\n# 2. Download reference files\ngget ref -w gtf -w cdna -d homo_sapiens\n\n# 3. Build kallisto index\nkallisto index -i transcriptome.idx transcriptome.fasta\n\n# 4. Download genome for alignment\ngget ref -w dna -d homo_sapiens\n```\n\n## Best Practices\n\n### Data Retrieval\n- Use `--limit` to control result sizes for large queries\n- Save results with `-o\u002F--out` for reproducibility\n- Check database versions\u002Freleases for consistency across analyses\n- Use `--quiet` in production scripts to reduce output\n\n### Sequence Analysis\n- For BLAST\u002FBLAT, start with default parameters, then adjust sensitivity\n- Use `gget diamond` with `--threads` for faster local alignment\n- Save DIAMOND databases with `--diamond_db` for repeated queries\n- For multiple sequence alignment, use `-s5\u002F--super5` for large datasets\n\n### Expression and Disease Data\n- Gene symbols are case-sensitive in cellxgene (e.g., 'PAX7' vs 'Pax7')\n- Run `gget setup` before first use of alphafold, cellxgene, elm, gpt\n- For enrichment analysis, use database shortcuts for convenience\n- Cache cBioPortal data with `-dd` to avoid repeated downloads\n\n### Structure Prediction\n- AlphaFold multimer predictions: use `-mr 20` for higher accuracy\n- Use `-r` flag for AMBER relaxation of final structures\n- Visualize results in Python with `plot=True`\n- Check PDB database first before running AlphaFold predictions\n\n### Error Handling\n- Database structures change; update gget regularly: `uv pip install --upgrade gget`\n- Process max ~1000 Ensembl IDs at once with gget info\n- For large-scale analyses, implement rate limiting for API queries\n- Use virtual environments to avoid dependency conflicts\n\n## Output Formats\n\n### Command-line\n- Default: JSON\n- CSV: Add `-csv` flag\n- FASTA: gget seq, gget mutate\n- PDB: gget pdb, gget alphafold\n- PNG: gget cbio plot\n\n### Python\n- Default: DataFrame or dictionary\n- JSON: Add `json=True` parameter\n- Save to file: Add `save=True` or specify `out=\"filename\"`\n- AnnData: gget cellxgene\n\n## Resources\n\nThis skill includes reference documentation for detailed module information:\n\n### references\u002F\n- `module_reference.md` - Comprehensive parameter reference for all modules\n- `database_info.md` - Information about queried databases and their update frequencies\n- `workflows.md` - Extended workflow examples and use cases\n\nFor additional help:\n- Official documentation: https:\u002F\u002Fpachterlab.github.io\u002Fgget\u002F\n- GitHub issues: https:\u002F\u002Fgithub.com\u002Fpachterlab\u002Fgget\u002Fissues\n- Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtac836\n","# gget\n\n## Overview\n\ngget is a command-line bioinformatics tool and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequence analysis, protein structures, expression data, and disease associations through a consistent interface. All gget modules work both as command-line tools and as Python functions.\n\n**Important**: The databases queried by gget are continuously updated, which sometimes changes their structure. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.\n\n## Installation\n\nInstall gget in a clean virtual environment to avoid conflicts:\n\n```bash\n# Using uv (recommended)\nuv uv pip install gget\n\n# Or using pip\nuv pip install --upgrade gget\n\n# In Python\u002FJupyter\nimport gget\n```\n\n## Quick Start\n\nBasic usage pattern for all modules:\n\n```bash\n# Command-line\ngget \u003Cmodule> [arguments] [options]\n\n# Python\ngget.module(arguments, options)\n```\n\nMost modules return:\n- **Command-line**: JSON (default) or CSV with `-csv` flag\n- **Python**: DataFrame or dictionary\n\nCommon flags across modules:\n- `-o\u002F--out`: Save results to file\n- `-q\u002F--quiet`: Suppress progress information\n- `-csv`: Return CSV format (command-line only)\n\n## Module Categories\n\n### 1. Reference & Gene Information\n\n#### gget ref - Reference Genome Downloads\n\nRetrieve download links and metadata for Ensembl reference genomes.\n\n**Parameters**:\n- `species`: Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'\n- `-w\u002F--which`: Specify return types (gtf, cdna, dna, cds, cdrna, pep). Default: all\n- `-r\u002F--release`: Ensembl release number (default: latest)\n- `-l\u002F--list_species`: List available vertebrate species\n- `-liv\u002F--list_iv_species`: List available invertebrate species\n- `-ftp`: Return only FTP links\n- `-d\u002F--download`: Download files (requires curl)\n\n**Examples**:\n```bash\n# List available species\ngget ref --list_species\n\n# Get all reference files for human\ngget ref homo_sapiens\n\n# Download only GTF annotation for mouse\ngget ref -w gtf -d mouse\n```\n\n```python\n# Python\ngget.ref(\"homo_sapiens\")\ngget.ref(\"mus_musculus\", which=\"gtf\", download=True)\n```\n\n#### gget search - Gene Search\n\nLocate genes by name or description across species.\n\n**Parameters**:\n- `searchwords`: One or more search terms (case-insensitive)\n- `-s\u002F--species`: Target species (e.g., 'homo_sapiens', 'mouse')\n- `-r\u002F--release`: Ensembl release number\n- `-t\u002F--id_type`: Return 'gene' (default) or 'transcript'\n- `-ao\u002F--andor`: 'or' (default) finds ANY searchword; 'and' requires ALL\n- `-l\u002F--limit`: Maximum results to return\n\n**Returns**: ensembl_id, gene_name, ensembl_description, ext_ref_description, biotype, URL\n\n**Examples**:\n```bash\n# Search for GABA-related genes in human\ngget search -s human gaba gamma-aminobutyric\n\n# Find specific gene, require all terms\ngget search -s mouse -ao and pax7 transcription\n```\n\n```python\n# Python\ngget.search([\"gaba\", \"gamma-aminobutyric\"], species=\"homo_sapiens\")\n```\n\n#### gget info - Gene\u002FTranscript Information\n\nRetrieve comprehensive gene and transcript metadata from Ensembl, UniProt, and NCBI.\n\n**Parameters**:\n- `ens_ids`: One or more Ensembl IDs (also supports WormBase, Flybase IDs). Limit: ~1000 IDs\n- `-n\u002F--ncbi`: Disable NCBI data retrieval\n- `-u\u002F--uniprot`: Disable UniProt data retrieval\n- `-pdb`: Include PDB identifiers (increases runtime)\n\n**Returns**: UniProt ID, NCBI gene ID, primary gene name, synonyms, protein names, descriptions, biotype, canonical transcript\n\n**Examples**:\n```bash\n# Get info for multiple genes\ngget info ENSG00000034713 ENSG00000104853 ENSG00000170296\n\n# Include PDB IDs\ngget info ENSG00000034713 -pdb\n```\n\n```python\n# Python\ngget.info([\"ENSG00000034713\", \"ENSG00000104853\"], pdb=True)\n```\n\n#### gget seq - Sequence Retrieval\n\nFetch nucleotide or amino acid sequences for genes and transcripts.\n\n**Parameters**:\n- `ens_ids`: One or more Ensembl identifiers\n- `-t\u002F--translate`: Fetch amino acid sequences instead of nucleotide\n- `-iso\u002F--isoforms`: Return all transcript variants (gene IDs only)\n\n**Returns**: FASTA format sequences\n\n**Examples**:\n```bash\n# Get nucleotide sequences\ngget seq ENSG00000034713 ENSG00000104853\n\n# Get all protein isoforms\ngget seq -t -iso ENSG00000034713\n```\n\n```python\n# Python\ngget.seq([\"ENSG00000034713\"], translate=True, isoforms=True)\n```\n\n### 2. Sequence Analysis & Alignment\n\n#### gget blast - BLAST Searches\n\nBLAST nucleotide or amino acid sequences against standard databases.\n\n**Parameters**:\n- `sequence`: Sequence string or path to FASTA\u002F.txt file\n- `-p\u002F--program`: blastn, blastp, blastx, tblastn, tblastx (auto-detected)\n- `-db\u002F--database`:\n  - Nucleotide: nt, refseq_rna, pdbnt\n  - Protein: nr, swissprot, pdbaa, refseq_protein\n- `-l\u002F--limit`: Max hits (default: 50)\n- `-e\u002F--expect`: E-value cutoff (default: 10.0)\n- `-lcf\u002F--low_comp_filt`: Enable low complexity filtering\n- `-mbo\u002F--megablast_off`: Disable MegaBLAST (blastn only)\n\n**Examples**:\n```bash\n# BLAST protein sequence\ngget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR\n\n# BLAST from file with specific database\ngget blast sequence.fasta -db swissprot -l 10\n```\n\n```python\n# Python\ngget.blast(\"MKWMFK...\", database=\"swissprot\", limit=10)\n```\n\n#### gget blat - BLAT Searches\n\nLocate genomic positions of sequences using UCSC BLAT.\n\n**Parameters**:\n- `sequence`: Sequence string or path to FASTA\u002F.txt file\n- `-st\u002F--seqtype`: 'DNA', 'protein', 'translated%20RNA', 'translated%20DNA' (auto-detected)\n- `-a\u002F--assembly`: Target assembly (default: 'human'\u002Fhg38; options: 'mouse'\u002Fmm39, 'zebrafinch'\u002FtaeGut2, etc.)\n\n**Returns**: genome, query size, alignment positions, matches, mismatches, alignment percentage\n\n**Examples**:\n```bash\n# Find genomic location in human\ngget blat ATCGATCGATCGATCG\n\n# Search in different assembly\ngget blat -a mm39 ATCGATCGATCGATCG\n```\n\n```python\n# Python\ngget.blat(\"ATCGATCGATCGATCG\", assembly=\"mouse\")\n```\n\n#### gget muscle - Multiple Sequence Alignment\n\nAlign multiple nucleotide or amino acid sequences using Muscle5.\n\n**Parameters**:\n- `fasta`: Sequences or path to FASTA\u002F.txt file\n- `-s5\u002F--super5`: Use Super5 algorithm for faster processing (large datasets)\n\n**Returns**: Aligned sequences in ClustalW format or aligned FASTA (.afa)\n\n**Examples**:\n```bash\n# Align sequences from file\ngget muscle sequences.fasta -o aligned.afa\n\n# Use Super5 for large dataset\ngget muscle large_dataset.fasta -s5\n```\n\n```python\n# Python\ngget.muscle(\"sequences.fasta\", save=True)\n```\n\n#### gget diamond - Local Sequence Alignment\n\nPerform fast local protein or translated DNA alignment using DIAMOND.\n\n**Parameters**:\n- Query: Sequences (string\u002Flist) or FASTA file path\n- `--reference`: Reference sequences (string\u002Flist) or FASTA file path (required)\n- `--sensitivity`: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive\n- `--threads`: CPU threads (default: 1)\n- `--diamond_db`: Save database for reuse\n- `--translated`: Enable nucleotide-to-amino acid alignment\n\n**Returns**: Identity percentage, sequence lengths, match positions, gap openings, E-values, bit scores\n\n**Examples**:\n```bash\n# Align against reference\ngget diamond GGETISAWESQME -ref reference.fasta --threads 4\n\n# Save database for reuse\ngget diamond query.fasta -ref ref.fasta --diamond_db my_db.dmnd\n```\n\n```python\n# Python\ngget.diamond(\"GGETISAWESQME\", reference=\"reference.fasta\", threads=4)\n```\n\n### 3. Structural & Protein Analysis\n\n#### gget pdb - Protein Structures\n\nQuery RCSB Protein Data Bank for structure and metadata.\n\n**Parameters**:\n- `pdb_id`: PDB identifier (e.g., '7S7U')\n- `-r\u002F--resource`: Data type (pdb, entry, pubmed, assembly, entity types)\n- `-i\u002F--identifier`: Assembly, entity, or chain ID\n\n**Returns**: PDB format (structures) or JSON (metadata)\n\n**Examples**:\n```bash\n# Download PDB structure\ngget pdb 7S7U -o 7S7U.pdb\n\n# Get metadata\ngget pdb 7S7U -r entry\n```\n\n```python\n# Python\ngget.pdb(\"7S7U\", save=True)\n```\n\n#### gget alphafold - Protein Structure Prediction\n\nPredict 3D protein structures using simplified AlphaFold2.\n\n**Setup Required**:\n```bash\n# Install OpenMM first\nuv pip install openmm\n\n# Then setup AlphaFold\ngget setup alphafold\n```\n\n**Parameters**:\n- `sequence`: Amino acid sequence (string), multiple sequences (list), or FASTA file. Multiple sequences trigger multimer modeling\n- `-mr\u002F--multimer_recycles`: Recycling iterations (default: 3; recommend 20 for accuracy)\n- `-mfm\u002F--multimer_for_monomer`: Apply multimer model to single proteins\n- `-r\u002F--relax`: AMBER relaxation for top-ranked model\n- `plot`: Python-only; generate interactive 3D visualization (default: True)\n- `show_sidechains`: Python-only; include side chains (default: True)\n\n**Returns**: PDB structure file, JSON alignment error data, optional 3D visualization\n\n**Examples**:\n```bash\n# Predict single protein structure\ngget alphafold MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR\n\n# Predict multimer with higher accuracy\ngget alphafold sequence1.fasta -mr 20 -r\n```\n\n```python\n# Python with visualization\ngget.alphafold(\"MKWMFK...\", plot=True, show_sidechains=True)\n\n# Multimer prediction\ngget.alphafold([\"sequence1\", \"sequence2\"], multimer_recycles=20)\n```\n\n#### gget elm - Eukaryotic Linear Motifs\n\nPredict Eukaryotic Linear Motifs in protein sequences.\n\n**Setup Required**:\n```bash\ngget setup elm\n```\n\n**Parameters**:\n- `sequence`: Amino acid sequence or UniProt Acc\n- `-u\u002F--uniprot`: Indicates sequence is UniProt Acc\n- `-e\u002F--expand`: Include protein names, organisms, references\n- `-s\u002F--sensitivity`: DIAMOND alignment sensitivity (default: \"very-sensitive\")\n- `-t\u002F--threads`: Number of threads (default: 1)\n\n**Returns**: Two outputs:\n1. **ortholog_df**: Linear motifs from orthologous proteins\n2. **regex_df**: Motifs directly matched in input sequence\n\n**Examples**:\n```bash\n# Predict motifs from sequence\ngget elm LIAQSIGQASFV -o results\n\n# Use UniProt accession with expanded info\ngget elm --uniprot Q02410 -e\n```\n\n```python\n# Python\northolog_df, regex_df = gget.elm(\"LIAQSIGQASFV\")\n```\n\n### 4. Expression & Disease Data\n\n#### gget archs4 - Gene Correlation & Tissue Expression\n\nQuery ARCHS4 database for correlated genes or tissue expression data.\n\n**Parameters**:\n- `gene`: Gene symbol or Ensembl ID (with `--ensembl` flag)\n- `-w\u002F--which`: 'correlation' (default, returns 100 most correlated genes) or 'tissue' (expression atlas)\n- `-s\u002F--species`: 'human' (default) or 'mouse' (tissue data only)\n- `-e\u002F--ensembl`: Input is Ensembl ID\n\n**Returns**:\n- **Correlation mode**: Gene symbols, Pearson correlation coefficients\n- **Tissue mode**: Tissue identifiers, min\u002FQ1\u002Fmedian\u002FQ3\u002Fmax expression values\n\n**Examples**:\n```bash\n# Get correlated genes\ngget archs4 ACE2\n\n# Get tissue expression\ngget archs4 -w tissue ACE2\n```\n\n```python\n# Python\ngget.archs4(\"ACE2\", which=\"tissue\")\n```\n\n#### gget cellxgene - Single-Cell RNA-seq Data\n\nQuery CZ CELLxGENE Discover Census for single-cell data.\n\n**Setup Required**:\n```bash\ngget setup cellxgene\n```\n\n**Parameters**:\n- `--gene` (-g): Gene names or Ensembl IDs (case-sensitive! 'PAX7' for human, 'Pax7' for mouse)\n- `--tissue`: Tissue type(s)\n- `--cell_type`: Specific cell type(s)\n- `--species` (-s): 'homo_sapiens' (default) or 'mus_musculus'\n- `--census_version` (-cv): Version (\"stable\", \"latest\", or dated)\n- `--ensembl` (-e): Use Ensembl IDs\n- `--meta_only` (-mo): Return metadata only\n- Additional filters: disease, development_stage, sex, assay, dataset_id, donor_id, ethnicity, suspension_type\n\n**Returns**: AnnData object with count matrices and metadata (or metadata-only dataframes)\n\n**Examples**:\n```bash\n# Get single-cell data for specific genes and cell types\ngget cellxgene --gene ACE2 ABCA1 --tissue lung --cell_type \"mucus secreting cell\" -o lung_data.h5ad\n\n# Metadata only\ngget cellxgene --gene PAX7 --tissue muscle --meta_only -o metadata.csv\n```\n\n```python\n# Python\nadata = gget.cellxgene(gene=[\"ACE2\", \"ABCA1\"], tissue=\"lung\", cell_type=\"mucus secreting cell\")\n```\n\n#### gget enrichr - Enrichment Analysis\n\nPerform ontology enrichment analysis on gene lists using Enrichr.\n\n**Parameters**:\n- `genes`: Gene symbols or Ensembl IDs\n- `-db\u002F--database`: Reference database (supports shortcuts: 'pathway', 'transcription', 'ontology', 'diseases_drugs', 'celltypes')\n- `-s\u002F--species`: human (default), mouse, fly, yeast, worm, fish\n- `-bkg_l\u002F--background_list`: Background genes for comparison\n- `-ko\u002F--kegg_out`: Save KEGG pathway images with highlighted genes\n- `plot`: Python-only; generate graphical results\n\n**Database Shortcuts**:\n- 'pathway' → KEGG_2021_Human\n- 'transcription' → ChEA_2016\n- 'ontology' → GO_Biological_Process_2021\n- 'diseases_drugs' → GWAS_Catalog_2019\n- 'celltypes' → PanglaoDB_Augmented_2021\n\n**Examples**:\n```bash\n# Enrichment analysis for ontology\ngget enrichr -db ontology ACE2 AGT AGTR1\n\n# Save KEGG pathways\ngget enrichr -db pathway ACE2 AGT AGTR1 -ko .\u002Fkegg_images\u002F\n```\n\n```python\n# Python with plot\ngget.enrichr([\"ACE2\", \"AGT\", \"AGTR1\"], database=\"ontology\", plot=True)\n```\n\n#### gget bgee - Orthology & Expression\n\nRetrieve orthology and gene expression data from Bgee database.\n\n**Parameters**:\n- `ens_id`: Ensembl gene ID or NCBI gene ID (for non-Ensembl species). Multiple IDs supported when `type=expression`\n- `-t\u002F--type`: 'orthologs' (default) or 'expression'\n\n**Returns**:\n- **Orthologs mode**: Matching genes across species with IDs, names, taxonomic info\n- **Expression mode**: Anatomical entities, confidence scores, expression status\n\n**Examples**:\n```bash\n# Get orthologs\ngget bgee ENSG00000169194\n\n# Get expression data\ngget bgee ENSG00000169194 -t expression\n\n# Multiple genes\ngget bgee ENSBTAG00000047356 ENSBTAG00000018317 -t expression\n```\n\n```python\n# Python\ngget.bgee(\"ENSG00000169194\", type=\"orthologs\")\n```\n\n#### gget opentargets - Disease & Drug Associations\n\nRetrieve disease and drug associations from OpenTargets.\n\n**Parameters**:\n- Ensembl gene ID (required)\n- `-r\u002F--resource`: diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions\n- `-l\u002F--limit`: Cap results count\n- Filter arguments (vary by resource):\n  - drugs: `--filter_disease`\n  - pharmacogenetics: `--filter_drug`\n  - expression\u002Fdepmap: `--filter_tissue`, `--filter_anat_sys`, `--filter_organ`\n  - interactions: `--filter_protein_a`, `--filter_protein_b`, `--filter_gene_b`\n\n**Examples**:\n```bash\n# Get associated diseases\ngget opentargets ENSG00000169194 -r diseases -l 5\n\n# Get associated drugs\ngget opentargets ENSG00000169194 -r drugs -l 10\n\n# Get tissue expression\ngget opentargets ENSG00000169194 -r expression --filter_tissue brain\n```\n\n```python\n# Python\ngget.opentargets(\"ENSG00000169194\", resource=\"diseases\", limit=5)\n```\n\n#### gget cbio - cBioPortal Cancer Genomics\n\nPlot cancer genomics heatmaps using cBioPortal data.\n\n**Two subcommands**:\n\n**search** - Find study IDs:\n```bash\ngget cbio search breast lung\n```\n\n**plot** - Generate heatmaps:\n\n**Parameters**:\n- `-s\u002F--study_ids`: Space-separated cBioPortal study IDs (required)\n- `-g\u002F--genes`: Space-separated gene names or Ensembl IDs (required)\n- `-st\u002F--stratification`: Column to organize data (tissue, cancer_type, cancer_type_detailed, study_id, sample)\n- `-vt\u002F--variation_type`: Data type (mutation_occurrences, cna_nonbinary, sv_occurrences, cna_occurrences, Consequence)\n- `-f\u002F--filter`: Filter by column value (e.g., 'study_id:msk_impact_2017')\n- `-dd\u002F--data_dir`: Cache directory (default: .\u002Fgget_cbio_cache)\n- `-fd\u002F--figure_dir`: Output directory (default: .\u002Fgget_cbio_figures)\n- `-dpi`: Resolution (default: 100)\n- `-sh\u002F--show`: Display plot in window\n- `-nc\u002F--no_confirm`: Skip download confirmations\n\n**Examples**:\n```bash\n# Search for studies\ngget cbio search esophag ovary\n\n# Create heatmap\ngget cbio plot -s msk_impact_2017 -g AKT1 ALK BRAF -st tissue -vt mutation_occurrences\n```\n\n```python\n# Python\ngget.cbio_search([\"esophag\", \"ovary\"])\ngget.cbio_plot([\"msk_impact_2017\"], [\"AKT1\", \"ALK\"], stratification=\"tissue\")\n```\n\n#### gget cosmic - COSMIC Database\n\nSearch COSMIC (Catalogue Of Somatic Mutations In Cancer) database.\n\n**Important**: License fees apply for commercial use. Requires COSMIC account credentials.\n\n**Parameters**:\n- `searchterm`: Gene name, Ensembl ID, mutation notation, or sample ID\n- `-ctp\u002F--cosmic_tsv_path`: Path to downloaded COSMIC TSV file (required for querying)\n- `-l\u002F--limit`: Maximum results (default: 100)\n\n**Database download flags**:\n- `-d\u002F--download_cosmic`: Activate download mode\n- `-gm\u002F--gget_mutate`: Create version for gget mutate\n- `-cp\u002F--cosmic_project`: Database type (cancer, census, cell_line, resistance, genome_screen, targeted_screen)\n- `-cv\u002F--cosmic_version`: COSMIC version\n- `-gv\u002F--grch_version`: Human reference genome (37 or 38)\n- `--email`, `--password`: COSMIC credentials\n\n**Examples**:\n```bash\n# First download database\ngget cosmic -d --email user@example.com --password xxx -cp cancer\n\n# Then query\ngget cosmic EGFR -ctp cosmic_data.tsv -l 10\n```\n\n```python\n# Python\ngget.cosmic(\"EGFR\", cosmic_tsv_path=\"cosmic_data.tsv\", limit=10)\n```\n\n### 5. Additional Tools\n\n#### gget mutate - Generate Mutated Sequences\n\nGenerate mutated nucleotide sequences from mutation annotations.\n\n**Parameters**:\n- `sequences`: FASTA file path or direct sequence input (string\u002Flist)\n- `-m\u002F--mutations`: CSV\u002FTSV file or DataFrame with mutation data (required)\n- `-mc\u002F--mut_column`: Mutation column name (default: 'mutation')\n- `-sic\u002F--seq_id_column`: Sequence ID column (default: 'seq_ID')\n- `-mic\u002F--mut_id_column`: Mutation ID column\n- `-k\u002F--k`: Length of flanking sequences (default: 30 nucleotides)\n\n**Returns**: Mutated sequences in FASTA format\n\n**Examples**:\n```bash\n# Single mutation\ngget mutate ATCGCTAAGCT -m \"c.4G>T\"\n\n# Multiple sequences with mutations from file\ngget mutate sequences.fasta -m mutations.csv -o mutated.fasta\n```\n\n```python\n# Python\nimport pandas as pd\nmutations_df = pd.DataFrame({\"seq_ID\": [\"seq1\"], \"mutation\": [\"c.4G>T\"]})\ngget.mutate([\"ATCGCTAAGCT\"], mutations=mutations_df)\n```\n\n#### gget gpt - OpenAI Text Generation\n\nGenerate natural language text using OpenAI's API.\n\n**Setup Required**:\n```bash\ngget setup gpt\n```\n\n**Important**: Free tier limited to 3 months after account creation. Set monthly billing limits.\n\n**Parameters**:\n- `prompt`: Text input for generation (required)\n- `api_key`: OpenAI authentication (required)\n- Model configuration: temperature, top_p, max_tokens, frequency_penalty, presence_penalty\n- Default model: gpt-3.5-turbo (configurable)\n\n**Examples**:\n```bash\ngget gpt \"Explain CRISPR\" --api_key your_key_here\n```\n\n```python\n# Python\ngget.gpt(\"Explain CRISPR\", api_key=\"your_key_here\")\n```\n\n#### gget setup - Install Dependencies\n\nInstall\u002Fdownload third-party dependencies for specific modules.\n\n**Parameters**:\n- `module`: Module name requiring dependency installation\n- `-o\u002F--out`: Output folder path (elm module only)\n\n**Modules requiring setup**:\n- `alphafold` - Downloads ~4GB of model parameters\n- `cellxgene` - Installs cellxgene-census (may not support latest Python)\n- `elm` - Downloads local ELM database\n- `gpt` - Configures OpenAI integration\n\n**Examples**:\n```bash\n# Setup AlphaFold\ngget setup alphafold\n\n# Setup ELM with custom directory\ngget setup elm -o \u002Fpath\u002Fto\u002Felm_data\n```\n\n```python\n# Python\ngget.setup(\"alphafold\")\n```\n\n## Common Workflows\n\n### Workflow 1: Gene Discovery to Sequence Analysis\n\nFind and analyze genes of interest:\n\n```python\n# 1. Search for genes\nresults = gget.search([\"GABA\", \"receptor\"], species=\"homo_sapiens\")\n\n# 2. Get detailed information\ngene_ids = results[\"ensembl_id\"].tolist()\ninfo = gget.info(gene_ids[:5])\n\n# 3. Retrieve sequences\nsequences = gget.seq(gene_ids[:5], translate=True)\n```\n\n### Workflow 2: Sequence Alignment and Structure\n\nAlign sequences and predict structures:\n\n```python\n# 1. Align multiple sequences\nalignment = gget.muscle(\"sequences.fasta\")\n\n# 2. Find similar sequences\nblast_results = gget.blast(my_sequence, database=\"swissprot\", limit=10)\n\n# 3. Predict structure\nstructure = gget.alphafold(my_sequence, plot=True)\n\n# 4. Find linear motifs\northolog_df, regex_df = gget.elm(my_sequence)\n```\n\n### Workflow 3: Gene Expression and Enrichment\n\nAnalyze expression patterns and functional enrichment:\n\n```python\n# 1. Get tissue expression\ntissue_expr = gget.archs4(\"ACE2\", which=\"tissue\")\n\n# 2. Find correlated genes\ncorrelated = gget.archs4(\"ACE2\", which=\"correlation\")\n\n# 3. Get single-cell data\nadata = gget.cellxgene(gene=[\"ACE2\"], tissue=\"lung\", cell_type=\"epithelial cell\")\n\n# 4. Perform enrichment analysis\ngene_list = correlated[\"gene_symbol\"].tolist()[:50]\nenrichment = gget.enrichr(gene_list, database=\"ontology\", plot=True)\n```\n\n### Workflow 4: Disease and Drug Analysis\n\nInvestigate disease associations and therapeutic targets:\n\n```python\n# 1. Search for genes\ngenes = gget.search([\"breast cancer\"], species=\"homo_sapiens\")\n\n# 2. Get disease associations\ndiseases = gget.opentargets(\"ENSG00000169194\", resource=\"diseases\")\n\n# 3. Get drug associations\ndrugs = gget.opentargets(\"ENSG00000169194\", resource=\"drugs\")\n\n# 4. Query cancer genomics data\nstudy_ids = gget.cbio_search([\"breast\"])\ngget.cbio_plot(study_ids[:2], [\"BRCA1\", \"BRCA2\"], stratification=\"cancer_type\")\n\n# 5. Search COSMIC for mutations\ncosmic_results = gget.cosmic(\"BRCA1\", cosmic_tsv_path=\"cosmic.tsv\")\n```\n\n### Workflow 5: Comparative Genomics\n\nCompare proteins across species:\n\n```python\n# 1. Get orthologs\northologs = gget.bgee(\"ENSG00000169194\", type=\"orthologs\")\n\n# 2. Get sequences for comparison\nhuman_seq = gget.seq(\"ENSG00000169194\", translate=True)\nmouse_seq = gget.seq(\"ENSMUSG00000026091\", translate=True)\n\n# 3. Align sequences\nalignment = gget.muscle([human_seq, mouse_seq])\n\n# 4. Compare structures\nhuman_structure = gget.pdb(\"7S7U\")\nmouse_structure = gget.alphafold(mouse_seq)\n```\n\n### Workflow 6: Building Reference Indices\n\nPrepare reference data for downstream analysis (e.g., kallisto|bustools):\n\n```bash\n# 1. List available species\ngget ref --list_species\n\n# 2. Download reference files\ngget ref -w gtf -w cdna -d homo_sapiens\n\n# 3. Build kallisto index\nkallisto index -i transcriptome.idx transcriptome.fasta\n\n# 4. Download genome for alignment\ngget ref -w dna -d homo_sapiens\n```\n\n## Best Practices\n\n### Data Retrieval\n- Use `--limit` to control result sizes for large queries\n- Save results with `-o\u002F--out` for reproducibility\n- Check database versions\u002Freleases for consistency across analyses\n- Use `--quiet` in production scripts to reduce output\n\n### Sequence Analysis\n- For BLAST\u002FBLAT, start with default parameters, then adjust sensitivity\n- Use `gget diamond` with `--threads` for faster local alignment\n- Save DIAMOND databases with `--diamond_db` for repeated queries\n- For multiple sequence alignment, use `-s5\u002F--super5` for large datasets\n\n### Expression and Disease Data\n- Gene symbols are case-sensitive in cellxgene (e.g., 'PAX7' vs 'Pax7')\n- Run `gget setup` before first use of alphafold, cellxgene, elm, gpt\n- For enrichment analysis, use database shortcuts for convenience\n- Cache cBioPortal data with `-dd` to avoid repeated downloads\n\n### Structure Prediction\n- AlphaFold multimer predictions: use `-mr 20` for higher accuracy\n- Use `-r` flag for AMBER relaxation of final structures\n- Visualize results in Python with `plot=True`\n- Check PDB database first before running AlphaFold predictions\n\n### Error Handling\n- Database structures change; update gget regularly: `uv pip install --upgrade gget`\n- Process max ~1000 Ensembl IDs at once with gget info\n- For large-scale analyses, implement rate limiting for API queries\n- Use virtual environments to avoid dependency conflicts\n\n## Output Formats\n\n### Command-line\n- Default: JSON\n- CSV: Add `-csv` flag\n- FASTA: gget seq, gget mutate\n- PDB: gget pdb, gget alphafold\n- PNG: gget cbio plot\n\n### Python\n- Default: DataFrame or dictionary\n- JSON: Add `json=True` parameter\n- Save to file: Add `save=True` or specify `out=\"filename\"`\n- AnnData: gget cellxgene\n\n## Resources\n\nThis skill includes reference documentation for detailed module information:\n\n### references\u002F\n- `module_reference.md` - Comprehensive parameter reference for all modules\n- `database_info.md` - Information about queried databases and their update frequencies\n- `workflows.md` - Extended workflow examples and use cases\n\nFor additional help:\n- Official documentation: https:\u002F\u002Fpachterlab.github.io\u002Fgget\u002F\n- GitHub issues: https:\u002F\u002Fgithub.com\u002Fpachterlab\u002Fgget\u002Fissues\n- Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtac836\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill gget",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill gget",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill gget","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002FSKILL.md",true,[73,79,83,87,91,97,101,105,109],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"examples\u002Fgood-prd.md","REFERENCE","参考文档",3090,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Fplugins\u002Fstart\u002Fskills\u002Frequirements-analysis\u002Fexamples\u002Fgood-prd.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fdatabase_info.md",10210,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002Freferences\u002Fdatabase_info.md",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fmodule_reference.md",17562,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002Freferences\u002Fmodule_reference.md",{"path":88,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fworkflows.md",25756,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002Freferences\u002Fworkflows.md",{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"scripts\u002Fbatch_sequence_analysis.py","SCRIPT","脚本",5978,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002Fscripts\u002Fbatch_sequence_analysis.py",{"path":98,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fenrichment_pipeline.py",7201,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002Fscripts\u002Fenrichment_pipeline.py",{"path":102,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":103,"sourceHtmlUrl":104},"scripts\u002Fgene_analysis.py",5778,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgget\u002Fscripts\u002Fgene_analysis.py",{"path":106,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":107,"sourceHtmlUrl":108},"template.md",5576,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Fplugins\u002Fstart\u002Fskills\u002Frequirements-analysis\u002Ftemplate.md",{"path":110,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":111,"sourceHtmlUrl":112},"validation.md",3093,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Fplugins\u002Fstart\u002Fskills\u002Frequirements-analysis\u002Fvalidation.md",[114,142,169,198,233,256],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":123,"publishedAt":44},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[120],{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[124,125,126,127,128,26,129,130,34,131,132,133,134,135,136,137,138,139,140,141],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"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":149,"publishedAt":44},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[148],{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},[150,151,152,153,154,155,156,157,158,159,160,131,161,162,163,164,165,166,167,168],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":182,"publishedAt":44},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[175,176,177],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":178,"categoryName":179,"taxonomyType":180,"taxonomyTypeDesc":181},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[183,184,185,186,187,188,189,26,190,191,192,193,194,195,196,34,24,41,131,197],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","query",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":209,"publishedAt":44},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[206],{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,26,232],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":237,"sourceHtmlUrl":238,"categories":239,"keywords":244,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[240,243],{"categoryNo":241,"categoryName":242,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},[245,246,247,248,249,211,250,220,221,251,42,252,253,26,254,133,255],"webapp testing","testing","webapp","browser","playwright","applications","debugging","logs","screenshots","local","behavior",{"skillNo":257,"name":258,"nameZh":6,"description":259,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":260,"sourceHtmlUrl":261,"categories":262,"keywords":270,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[263,266,267],{"categoryNo":264,"categoryName":265,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":268,"categoryName":269,"taxonomyType":180,"taxonomyTypeDesc":181},"01JBSEEDCAT000000000000036","内容创作者",[271,272,273,274,275,276,277,278,213,279,223,161,280,281,282,283,284,195,285,131,286,287,288,289,290,139,291],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]