[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFARVC2Y60YX5N84NZW464":3,"skill-same:01M6AFARVC2Y60YX5N84NZW464":125},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6AFARVC2Y60YX5N84NZW464","bioservices",null,"Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"use","for","Unified API","multi","mapping","pathway","Reactome","KEGG","chembl","id","queries","python","direct","tool","api","analysis","REST","services","database","workflows","2026-11-10 10:12:30",false,"---\nname: bioservices\ndescription: \"Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).\"\n---\n\n# BioServices\n\n## Overview\n\nBioServices is a Python package providing programmatic access to approximately 40 bioinformatics web services and databases. Retrieve biological data, perform cross-database queries, map identifiers, analyze sequences, and integrate multiple biological resources in Python workflows. The package handles both REST and SOAP\u002FWSDL protocols transparently.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam\n- Analyzing metabolic pathways and gene functions via KEGG or Reactome\n- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information\n- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)\n- Running sequence similarity searches (BLAST, MUSCLE alignment)\n- Querying gene ontology terms (QuickGO, GO annotations)\n- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)\n- Mining genomic data (BioMart, ArrayExpress, ENA)\n- Integrating data from multiple bioinformatics resources in a single workflow\n\n## Core Capabilities\n\n### 1. Protein Analysis\n\nRetrieve protein information, sequences, and functional annotations:\n\n```python\nfrom bioservices import UniProt\n\nu = UniProt(verbose=False)\n\n# Search for protein by name\nresults = u.search(\"ZAP70_HUMAN\", frmt=\"tab\", columns=\"id,genes,organism\")\n\n# Retrieve FASTA sequence\nsequence = u.retrieve(\"P43403\", \"fasta\")\n\n# Map identifiers between databases\nkegg_ids = u.mapping(fr=\"UniProtKB_AC-ID\", to=\"KEGG\", query=\"P43403\")\n```\n\n**Key methods:**\n- `search()`: Query UniProt with flexible search terms\n- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)\n- `mapping()`: Convert identifiers between databases\n\nReference: `references\u002Fservices_reference.md` for complete UniProt API details.\n\n### 2. Pathway Discovery and Analysis\n\nAccess KEGG pathway information for genes and organisms:\n\n```python\nfrom bioservices import KEGG\n\nk = KEGG()\nk.organism = \"hsa\"  # Set to human\n\n# Search for organisms\nk.lookfor_organism(\"droso\")  # Find Drosophila species\n\n# Find pathways by name\nk.lookfor_pathway(\"B cell\")  # Returns matching pathway IDs\n\n# Get pathways containing specific genes\npathways = k.get_pathway_by_gene(\"7535\", \"hsa\")  # ZAP70 gene\n\n# Retrieve and parse pathway data\ndata = k.get(\"hsa04660\")\nparsed = k.parse(data)\n\n# Extract pathway interactions\ninteractions = k.parse_kgml_pathway(\"hsa04660\")\nrelations = interactions['relations']  # Protein-protein interactions\n\n# Convert to Simple Interaction Format\nsif_data = k.pathway2sif(\"hsa04660\")\n```\n\n**Key methods:**\n- `lookfor_organism()`, `lookfor_pathway()`: Search by name\n- `get_pathway_by_gene()`: Find pathways containing genes\n- `parse_kgml_pathway()`: Extract structured pathway data\n- `pathway2sif()`: Get protein interaction networks\n\nReference: `references\u002Fworkflow_patterns.md` for complete pathway analysis workflows.\n\n### 3. Compound Database Searches\n\nSearch and cross-reference compounds across multiple databases:\n\n```python\nfrom bioservices import KEGG, UniChem\n\nk = KEGG()\n\n# Search compounds by name\nresults = k.find(\"compound\", \"Geldanamycin\")  # Returns cpd:C11222\n\n# Get compound information with database links\ncompound_info = k.get(\"cpd:C11222\")  # Includes ChEBI links\n\n# Cross-reference KEGG → ChEMBL using UniChem\nu = UniChem()\nchembl_id = u.get_compound_id_from_kegg(\"C11222\")  # Returns CHEMBL278315\n```\n\n**Common workflow:**\n1. Search compound by name in KEGG\n2. Extract KEGG compound ID\n3. Use UniChem for KEGG → ChEMBL mapping\n4. ChEBI IDs are often provided in KEGG entries\n\nReference: `references\u002Fidentifier_mapping.md` for complete cross-database mapping guide.\n\n### 4. Sequence Analysis\n\nRun BLAST searches and sequence alignments:\n\n```python\nfrom bioservices import NCBIblast\n\ns = NCBIblast(verbose=False)\n\n# Run BLASTP against UniProtKB\njobid = s.run(\n    program=\"blastp\",\n    sequence=protein_sequence,\n    stype=\"protein\",\n    database=\"uniprotkb\",\n    email=\"your.email@example.com\"  # Required by NCBI\n)\n\n# Check job status and retrieve results\ns.getStatus(jobid)\nresults = s.getResult(jobid, \"out\")\n```\n\n**Note:** BLAST jobs are asynchronous. Check status before retrieving results.\n\n### 5. Identifier Mapping\n\nConvert identifiers between different biological databases:\n\n```python\nfrom bioservices import UniProt, KEGG\n\n# UniProt mapping (many database pairs supported)\nu = UniProt()\nresults = u.mapping(\n    fr=\"UniProtKB_AC-ID\",  # Source database\n    to=\"KEGG\",              # Target database\n    query=\"P43403\"          # Identifier(s) to convert\n)\n\n# KEGG gene ID → UniProt\nkegg_to_uniprot = u.mapping(fr=\"KEGG\", to=\"UniProtKB_AC-ID\", query=\"hsa:7535\")\n\n# For compounds, use UniChem\nfrom bioservices import UniChem\nu = UniChem()\nchembl_from_kegg = u.get_compound_id_from_kegg(\"C11222\")\n```\n\n**Supported mappings (UniProt):**\n- UniProtKB ↔ KEGG\n- UniProtKB ↔ Ensembl\n- UniProtKB ↔ PDB\n- UniProtKB ↔ RefSeq\n- And many more (see `references\u002Fidentifier_mapping.md`)\n\n### 6. Gene Ontology Queries\n\nAccess GO terms and annotations:\n\n```python\nfrom bioservices import QuickGO\n\ng = QuickGO(verbose=False)\n\n# Retrieve GO term information\nterm_info = g.Term(\"GO:0003824\", frmt=\"obo\")\n\n# Search annotations\nannotations = g.Annotation(protein=\"P43403\", format=\"tsv\")\n```\n\n### 7. Protein-Protein Interactions\n\nQuery interaction databases via PSICQUIC:\n\n```python\nfrom bioservices import PSICQUIC\n\ns = PSICQUIC(verbose=False)\n\n# Query specific database (e.g., MINT)\ninteractions = s.query(\"mint\", \"ZAP70 AND species:9606\")\n\n# List available interaction databases\ndatabases = s.activeDBs\n```\n\n**Available databases:** MINT, IntAct, BioGRID, DIP, and 30+ others.\n\n## Multi-Service Integration Workflows\n\nBioServices excels at combining multiple services for comprehensive analysis. Common integration patterns:\n\n### Complete Protein Analysis Pipeline\n\nExecute a full protein characterization workflow:\n\n```bash\npython scripts\u002Fprotein_analysis_workflow.py ZAP70_HUMAN your.email@example.com\n```\n\nThis script demonstrates:\n1. UniProt search for protein entry\n2. FASTA sequence retrieval\n3. BLAST similarity search\n4. KEGG pathway discovery\n5. PSICQUIC interaction mapping\n\n### Pathway Network Analysis\n\nAnalyze all pathways for an organism:\n\n```bash\npython scripts\u002Fpathway_analysis.py hsa output_directory\u002F\n```\n\nExtracts and analyzes:\n- All pathway IDs for organism\n- Protein-protein interactions per pathway\n- Interaction type distributions\n- Exports to CSV\u002FSIF formats\n\n### Cross-Database Compound Search\n\nMap compound identifiers across databases:\n\n```bash\npython scripts\u002Fcompound_cross_reference.py Geldanamycin\n```\n\nRetrieves:\n- KEGG compound ID\n- ChEBI identifier\n- ChEMBL identifier\n- Basic compound properties\n\n### Batch Identifier Conversion\n\nConvert multiple identifiers at once:\n\n```bash\npython scripts\u002Fbatch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG\n```\n\n## Best Practices\n\n### Output Format Handling\n\nDifferent services return data in various formats:\n- **XML**: Parse using BeautifulSoup (most SOAP services)\n- **Tab-separated (TSV)**: Pandas DataFrames for tabular data\n- **Dictionary\u002FJSON**: Direct Python manipulation\n- **FASTA**: BioPython integration for sequence analysis\n\n### Rate Limiting and Verbosity\n\nControl API request behavior:\n\n```python\nfrom bioservices import KEGG\n\nk = KEGG(verbose=False)  # Suppress HTTP request details\nk.TIMEOUT = 30  # Adjust timeout for slow connections\n```\n\n### Error Handling\n\nWrap service calls in try-except blocks:\n\n```python\ntry:\n    results = u.search(\"ambiguous_query\")\n    if results:\n        # Process results\n        pass\nexcept Exception as e:\n    print(f\"Search failed: {e}\")\n```\n\n### Organism Codes\n\nUse standard organism abbreviations:\n- `hsa`: Homo sapiens (human)\n- `mmu`: Mus musculus (mouse)\n- `dme`: Drosophila melanogaster\n- `sce`: Saccharomyces cerevisiae (yeast)\n\nList all organisms: `k.list(\"organism\")` or `k.organismIds`\n\n### Integration with Other Tools\n\nBioServices works well with:\n- **BioPython**: Sequence analysis on retrieved FASTA data\n- **Pandas**: Tabular data manipulation\n- **PyMOL**: 3D structure visualization (retrieve PDB IDs)\n- **NetworkX**: Network analysis of pathway interactions\n- **Galaxy**: Custom tool wrappers for workflow platforms\n\n## Resources\n\n### scripts\u002F\n\nExecutable Python scripts demonstrating complete workflows:\n\n- `protein_analysis_workflow.py`: End-to-end protein characterization\n- `pathway_analysis.py`: KEGG pathway discovery and network extraction\n- `compound_cross_reference.py`: Multi-database compound searching\n- `batch_id_converter.py`: Bulk identifier mapping utility\n\nScripts can be executed directly or adapted for specific use cases.\n\n### references\u002F\n\nDetailed documentation loaded as needed:\n\n- `services_reference.md`: Comprehensive list of all 40+ services with methods\n- `workflow_patterns.md`: Detailed multi-step analysis workflows\n- `identifier_mapping.md`: Complete guide to cross-database ID conversion\n\nLoad references when working with specific services or complex integration tasks.\n\n## Installation\n\n```bash\nuv pip install bioservices\n```\n\nDependencies are automatically managed. Package is tested on Python 3.9-3.12.\n\n## Additional Information\n\nFor detailed API documentation and advanced features, refer to:\n- Official documentation: https:\u002F\u002Fbioservices.readthedocs.io\u002F\n- Source code: https:\u002F\u002Fgithub.com\u002Fcokelaer\u002Fbioservices\n- Service-specific references in `references\u002Fservices_reference.md`\n","# BioServices\n\n## Overview\n\nBioServices is a Python package providing programmatic access to approximately 40 bioinformatics web services and databases. Retrieve biological data, perform cross-database queries, map identifiers, analyze sequences, and integrate multiple biological resources in Python workflows. The package handles both REST and SOAP\u002FWSDL protocols transparently.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam\n- Analyzing metabolic pathways and gene functions via KEGG or Reactome\n- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information\n- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)\n- Running sequence similarity searches (BLAST, MUSCLE alignment)\n- Querying gene ontology terms (QuickGO, GO annotations)\n- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)\n- Mining genomic data (BioMart, ArrayExpress, ENA)\n- Integrating data from multiple bioinformatics resources in a single workflow\n\n## Core Capabilities\n\n### 1. Protein Analysis\n\nRetrieve protein information, sequences, and functional annotations:\n\n```python\nfrom bioservices import UniProt\n\nu = UniProt(verbose=False)\n\n# Search for protein by name\nresults = u.search(\"ZAP70_HUMAN\", frmt=\"tab\", columns=\"id,genes,organism\")\n\n# Retrieve FASTA sequence\nsequence = u.retrieve(\"P43403\", \"fasta\")\n\n# Map identifiers between databases\nkegg_ids = u.mapping(fr=\"UniProtKB_AC-ID\", to=\"KEGG\", query=\"P43403\")\n```\n\n**Key methods:**\n- `search()`: Query UniProt with flexible search terms\n- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)\n- `mapping()`: Convert identifiers between databases\n\nReference: `references\u002Fservices_reference.md` for complete UniProt API details.\n\n### 2. Pathway Discovery and Analysis\n\nAccess KEGG pathway information for genes and organisms:\n\n```python\nfrom bioservices import KEGG\n\nk = KEGG()\nk.organism = \"hsa\"  # Set to human\n\n# Search for organisms\nk.lookfor_organism(\"droso\")  # Find Drosophila species\n\n# Find pathways by name\nk.lookfor_pathway(\"B cell\")  # Returns matching pathway IDs\n\n# Get pathways containing specific genes\npathways = k.get_pathway_by_gene(\"7535\", \"hsa\")  # ZAP70 gene\n\n# Retrieve and parse pathway data\ndata = k.get(\"hsa04660\")\nparsed = k.parse(data)\n\n# Extract pathway interactions\ninteractions = k.parse_kgml_pathway(\"hsa04660\")\nrelations = interactions['relations']  # Protein-protein interactions\n\n# Convert to Simple Interaction Format\nsif_data = k.pathway2sif(\"hsa04660\")\n```\n\n**Key methods:**\n- `lookfor_organism()`, `lookfor_pathway()`: Search by name\n- `get_pathway_by_gene()`: Find pathways containing genes\n- `parse_kgml_pathway()`: Extract structured pathway data\n- `pathway2sif()`: Get protein interaction networks\n\nReference: `references\u002Fworkflow_patterns.md` for complete pathway analysis workflows.\n\n### 3. Compound Database Searches\n\nSearch and cross-reference compounds across multiple databases:\n\n```python\nfrom bioservices import KEGG, UniChem\n\nk = KEGG()\n\n# Search compounds by name\nresults = k.find(\"compound\", \"Geldanamycin\")  # Returns cpd:C11222\n\n# Get compound information with database links\ncompound_info = k.get(\"cpd:C11222\")  # Includes ChEBI links\n\n# Cross-reference KEGG → ChEMBL using UniChem\nu = UniChem()\nchembl_id = u.get_compound_id_from_kegg(\"C11222\")  # Returns CHEMBL278315\n```\n\n**Common workflow:**\n1. Search compound by name in KEGG\n2. Extract KEGG compound ID\n3. Use UniChem for KEGG → ChEMBL mapping\n4. ChEBI IDs are often provided in KEGG entries\n\nReference: `references\u002Fidentifier_mapping.md` for complete cross-database mapping guide.\n\n### 4. Sequence Analysis\n\nRun BLAST searches and sequence alignments:\n\n```python\nfrom bioservices import NCBIblast\n\ns = NCBIblast(verbose=False)\n\n# Run BLASTP against UniProtKB\njobid = s.run(\n    program=\"blastp\",\n    sequence=protein_sequence,\n    stype=\"protein\",\n    database=\"uniprotkb\",\n    email=\"your.email@example.com\"  # Required by NCBI\n)\n\n# Check job status and retrieve results\ns.getStatus(jobid)\nresults = s.getResult(jobid, \"out\")\n```\n\n**Note:** BLAST jobs are asynchronous. Check status before retrieving results.\n\n### 5. Identifier Mapping\n\nConvert identifiers between different biological databases:\n\n```python\nfrom bioservices import UniProt, KEGG\n\n# UniProt mapping (many database pairs supported)\nu = UniProt()\nresults = u.mapping(\n    fr=\"UniProtKB_AC-ID\",  # Source database\n    to=\"KEGG\",              # Target database\n    query=\"P43403\"          # Identifier(s) to convert\n)\n\n# KEGG gene ID → UniProt\nkegg_to_uniprot = u.mapping(fr=\"KEGG\", to=\"UniProtKB_AC-ID\", query=\"hsa:7535\")\n\n# For compounds, use UniChem\nfrom bioservices import UniChem\nu = UniChem()\nchembl_from_kegg = u.get_compound_id_from_kegg(\"C11222\")\n```\n\n**Supported mappings (UniProt):**\n- UniProtKB ↔ KEGG\n- UniProtKB ↔ Ensembl\n- UniProtKB ↔ PDB\n- UniProtKB ↔ RefSeq\n- And many more (see `references\u002Fidentifier_mapping.md`)\n\n### 6. Gene Ontology Queries\n\nAccess GO terms and annotations:\n\n```python\nfrom bioservices import QuickGO\n\ng = QuickGO(verbose=False)\n\n# Retrieve GO term information\nterm_info = g.Term(\"GO:0003824\", frmt=\"obo\")\n\n# Search annotations\nannotations = g.Annotation(protein=\"P43403\", format=\"tsv\")\n```\n\n### 7. Protein-Protein Interactions\n\nQuery interaction databases via PSICQUIC:\n\n```python\nfrom bioservices import PSICQUIC\n\ns = PSICQUIC(verbose=False)\n\n# Query specific database (e.g., MINT)\ninteractions = s.query(\"mint\", \"ZAP70 AND species:9606\")\n\n# List available interaction databases\ndatabases = s.activeDBs\n```\n\n**Available databases:** MINT, IntAct, BioGRID, DIP, and 30+ others.\n\n## Multi-Service Integration Workflows\n\nBioServices excels at combining multiple services for comprehensive analysis. Common integration patterns:\n\n### Complete Protein Analysis Pipeline\n\nExecute a full protein characterization workflow:\n\n```bash\npython scripts\u002Fprotein_analysis_workflow.py ZAP70_HUMAN your.email@example.com\n```\n\nThis script demonstrates:\n1. UniProt search for protein entry\n2. FASTA sequence retrieval\n3. BLAST similarity search\n4. KEGG pathway discovery\n5. PSICQUIC interaction mapping\n\n### Pathway Network Analysis\n\nAnalyze all pathways for an organism:\n\n```bash\npython scripts\u002Fpathway_analysis.py hsa output_directory\u002F\n```\n\nExtracts and analyzes:\n- All pathway IDs for organism\n- Protein-protein interactions per pathway\n- Interaction type distributions\n- Exports to CSV\u002FSIF formats\n\n### Cross-Database Compound Search\n\nMap compound identifiers across databases:\n\n```bash\npython scripts\u002Fcompound_cross_reference.py Geldanamycin\n```\n\nRetrieves:\n- KEGG compound ID\n- ChEBI identifier\n- ChEMBL identifier\n- Basic compound properties\n\n### Batch Identifier Conversion\n\nConvert multiple identifiers at once:\n\n```bash\npython scripts\u002Fbatch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG\n```\n\n## Best Practices\n\n### Output Format Handling\n\nDifferent services return data in various formats:\n- **XML**: Parse using BeautifulSoup (most SOAP services)\n- **Tab-separated (TSV)**: Pandas DataFrames for tabular data\n- **Dictionary\u002FJSON**: Direct Python manipulation\n- **FASTA**: BioPython integration for sequence analysis\n\n### Rate Limiting and Verbosity\n\nControl API request behavior:\n\n```python\nfrom bioservices import KEGG\n\nk = KEGG(verbose=False)  # Suppress HTTP request details\nk.TIMEOUT = 30  # Adjust timeout for slow connections\n```\n\n### Error Handling\n\nWrap service calls in try-except blocks:\n\n```python\ntry:\n    results = u.search(\"ambiguous_query\")\n    if results:\n        # Process results\n        pass\nexcept Exception as e:\n    print(f\"Search failed: {e}\")\n```\n\n### Organism Codes\n\nUse standard organism abbreviations:\n- `hsa`: Homo sapiens (human)\n- `mmu`: Mus musculus (mouse)\n- `dme`: Drosophila melanogaster\n- `sce`: Saccharomyces cerevisiae (yeast)\n\nList all organisms: `k.list(\"organism\")` or `k.organismIds`\n\n### Integration with Other Tools\n\nBioServices works well with:\n- **BioPython**: Sequence analysis on retrieved FASTA data\n- **Pandas**: Tabular data manipulation\n- **PyMOL**: 3D structure visualization (retrieve PDB IDs)\n- **NetworkX**: Network analysis of pathway interactions\n- **Galaxy**: Custom tool wrappers for workflow platforms\n\n## Resources\n\n### scripts\u002F\n\nExecutable Python scripts demonstrating complete workflows:\n\n- `protein_analysis_workflow.py`: End-to-end protein characterization\n- `pathway_analysis.py`: KEGG pathway discovery and network extraction\n- `compound_cross_reference.py`: Multi-database compound searching\n- `batch_id_converter.py`: Bulk identifier mapping utility\n\nScripts can be executed directly or adapted for specific use cases.\n\n### references\u002F\n\nDetailed documentation loaded as needed:\n\n- `services_reference.md`: Comprehensive list of all 40+ services with methods\n- `workflow_patterns.md`: Detailed multi-step analysis workflows\n- `identifier_mapping.md`: Complete guide to cross-database ID conversion\n\nLoad references when working with specific services or complex integration tasks.\n\n## Installation\n\n```bash\nuv pip install bioservices\n```\n\nDependencies are automatically managed. Package is tested on Python 3.9-3.12.\n\n## Additional Information\n\nFor detailed API documentation and advanced features, refer to:\n- Official documentation: https:\u002F\u002Fbioservices.readthedocs.io\u002F\n- Source code: https:\u002F\u002Fgithub.com\u002Fcokelaer\u002Fbioservices\n- Service-specific references in `references\u002Fservices_reference.md`\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices",3931,"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","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add davila7\u002Fclaude-code-templates --skill bioservices",{"tool":68,"command":69},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill bioservices",{"tool":71,"command":72},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill bioservices","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002FSKILL.md",true,[76,82,88,91,95,99,103,109,113,117,121],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"assets\u002Fexample_asset.txt","ASSET","资产文件",865,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Falirezarezvani-claude-skills\u002Fra-qm-team\u002Fregulatory-affairs-head\u002Fassets\u002Fexample_asset.txt",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Feu-mdr-submission-guide.md","REFERENCE","参考文档",3272,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Falirezarezvani-claude-skills\u002Fra-qm-team\u002Fregulatory-affairs-head\u002Freferences\u002Feu-mdr-submission-guide.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":55,"sourceHtmlUrl":90},"references\u002Ffda-submission-guide.md","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Falirezarezvani-claude-skills\u002Fra-qm-team\u002Fregulatory-affairs-head\u002Freferences\u002Ffda-submission-guide.md",{"path":92,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fidentifier_mapping.md",17551,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Freferences\u002Fidentifier_mapping.md",{"path":96,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fservices_reference.md",12566,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Freferences\u002Fservices_reference.md",{"path":100,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fworkflow_patterns.md",20126,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Freferences\u002Fworkflow_patterns.md",{"path":104,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":107,"sourceHtmlUrl":108},"scripts\u002Fbatch_id_converter.py","SCRIPT","脚本",10892,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Fscripts\u002Fbatch_id_converter.py",{"path":110,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":111,"sourceHtmlUrl":112},"scripts\u002Fcompound_cross_reference.py",11289,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Fscripts\u002Fcompound_cross_reference.py",{"path":114,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":115,"sourceHtmlUrl":116},"scripts\u002Fpathway_analysis.py",9547,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Fscripts\u002Fpathway_analysis.py",{"path":118,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":119,"sourceHtmlUrl":120},"scripts\u002Fprotein_analysis_workflow.py",12449,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fbioservices\u002Fscripts\u002Fprotein_analysis_workflow.py",{"path":122,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":123,"sourceHtmlUrl":124},"scripts\u002Fregulatory_tracker.py",8223,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Frsmdt-the-startup\u002Falirezarezvani-claude-skills\u002Fra-qm-team\u002Fregulatory-affairs-head\u002Fscripts\u002Fregulatory_tracker.py",[126,152,179,208,243,267],{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":133,"publishedAt":47},"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",[132],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[134,135,136,137,138,28,139,140,27,141,142,143,144,145,146,147,148,149,150,151],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":159,"publishedAt":47},"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",[158],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[160,161,162,163,164,165,166,167,168,169,170,141,171,172,173,174,175,176,177,178],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":192,"publishedAt":47},"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",[185,186,187],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":188,"categoryName":189,"taxonomyType":190,"taxonomyTypeDesc":191},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[193,194,195,196,197,198,199,28,200,201,202,40,203,204,205,27,206,45,141,207],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","calls","only","MCP","access","query",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":212,"licenseFileUrl":213,"sourceHtmlUrl":214,"categories":215,"keywords":219,"publishedAt":47},"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",[216],{"categoryNo":217,"categoryName":218,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,28,242],"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":244,"name":245,"nameZh":6,"description":246,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":212,"licenseFileUrl":247,"sourceHtmlUrl":248,"categories":249,"keywords":254,"publishedAt":47},"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",[250,253],{"categoryNo":251,"categoryName":252,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":217,"categoryName":218,"taxonomyType":21,"taxonomyTypeDesc":22},[255,256,257,258,259,221,260,230,231,261,262,263,264,28,265,143,266],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":268,"name":269,"nameZh":6,"description":270,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":212,"licenseFileUrl":271,"sourceHtmlUrl":272,"categories":273,"keywords":281,"publishedAt":47},"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",[274,277,278],{"categoryNo":275,"categoryName":276,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":279,"categoryName":280,"taxonomyType":190,"taxonomyTypeDesc":191},"01JBSEEDCAT000000000000036","内容创作者",[282,283,284,285,286,287,288,289,223,290,233,171,291,292,293,294,295,204,296,141,297,42,298,299,300,149,301],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]