[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DMJ187MSGDHKC8Z47NBE4T":3,"skill-same:01M6DMJ187MSGDHKC8Z47NBE4T":64},{"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":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6DMJ187MSGDHKC8Z47NBE4T","interproscan-domain-analysis",null,"Analyze protein sequences using InterProScan to identify functional domains, protein families, and Gene Ontology (GO) annotations.","InternScience\u002FDrClaw","InternScience","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F154449623?v=4","01M6DHY1ZF02QASNAVMNPT7NP6","Intern Science","01M6DHY2523FY56Q5572NKAAD0",172,"MIT","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Finterproscan-domain-analysis\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30],"analysis","domain","interproscan domain analysis","go","analyze","annotations","ontology","2026-11-10 10:12:30",false,"---\nname: interproscan-domain-analysis\ndescription: Analyze protein sequences using InterProScan to identify functional domains,\n  protein families, and Gene Ontology (GO) annotations.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 用InterProScan分析。\n---\n\n# InterProScan Protein Domain Analysis\n\n## Usage\n\n### 1. MCP Server Definition\n\nUse the same `BioInfoToolsClient` class as defined in the protein-blast-search skill.\n\n### 2. InterProScan Domain Analysis Workflow\n\nThis workflow analyzes protein sequences using InterProScan to identify functional domains, protein families, binding sites, and associated Gene Ontology annotations.\n\n**Workflow Steps:**\n\n1. **Validate Sequence** - Check protein sequence format and length\n2. **Run InterProScan** - Identify domains using multiple signature databases\n3. **Extract Annotations** - Parse domain locations, families, and GO terms\n\n**Implementation:**\n\n```python\nfrom datetime import timedelta\n\n## Initialize client\nclient = BioInfoToolsClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F17\u002FBioInfo-Tools\",\n    \"\u003Cyour-api-key>\"\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\n## Input: Protein sequence to analyze\nprotein_sequence = \"\"\"\nMVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH\n\"\"\"\n\n## Step 1 & 2: Run InterProScan analysis\nresult = await client.session.call_tool(\n    \"interproscan_analyze\",\n    arguments={\n        \"sequence\": protein_sequence.strip(),\n        \"sequence_id\": \"HBB_HUMAN\",        # Optional identifier\n        \"databases\": [\"Pfam\"],              # Signature databases to use\n        \"goterms\": True                     # Include GO term annotations\n    },\n    read_timeout_seconds=timedelta(seconds=900)  # Allow up to 15 minutes\n)\n\n## Step 3: Parse and display results\nresult_data = client.parse_result(result)\n\nif result_data.get(\"success\"):\n    results = result_data.get(\"results\", {})\n    domains = results.get(\"domains\", [])\n    go_terms = results.get(\"go_terms\", [])\n\n    print(f\"✅ InterProScan analysis completed successfully\")\n    print(f\"Execution time: {result_data.get('time_seconds', '?')} seconds\")\n    print(f\"Domains found: {len(domains)}\")\n    print(f\"GO annotations: {len(go_terms)}\\n\")\n\n    # Display domain information\n    if domains:\n        print(\"=== Functional Domains ===\\n\")\n        for i, domain in enumerate(domains, 1):\n            print(f\"{i}. {domain.get('name', 'N\u002FA')}\")\n            print(f\"   Accession: {domain.get('accession', 'N\u002FA')}\")\n            print(f\"   Database: {domain.get('database', 'N\u002FA')}\")\n            if domain.get('description'):\n                print(f\"   Description: {domain.get('description')}\")\n\n            # Display domain locations\n            locations = domain.get('locations', [])\n            if locations:\n                print(f\"   Locations:\")\n                for loc in locations:\n                    print(f\"     - Position {loc.get('start')}-{loc.get('end')} aa\")\n                    if loc.get('score'):\n                        print(f\"       Score: {loc.get('score')}\")\n            print()\n\n    # Display GO annotations\n    if go_terms:\n        print(\"=== Gene Ontology Annotations ===\\n\")\n\n        # Group by category\n        by_category = {}\n        for go in go_terms:\n            category = go.get('category', 'UNKNOWN')\n            if category not in by_category:\n                by_category[category] = []\n            by_category[category].append(go)\n\n        for category, terms in by_category.items():\n            print(f\"{category}:\")\n            for go in terms:\n                print(f\"  - {go.get('id', 'N\u002FA')}: {go.get('name', 'N\u002FA')}\")\n            print()\nelse:\n    print(f\"❌ InterProScan analysis failed: {result_data.get('error', 'Unknown error')}\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**BioInfo-Tools Server:**\n- `interproscan_analyze`: Analyze protein sequence using InterProScan\n  - Args:\n    - `sequence` (str): Protein sequence in amino acid single-letter code\n    - `sequence_id` (str, optional): Identifier for the query sequence\n    - `databases` (list, optional): Signature databases to query (default: [\"Pfam\"])\n    - `goterms` (bool, optional): Include GO term annotations (default: True)\n  - Returns:\n    - `success` (bool): Whether analysis completed successfully\n    - `results` (dict): Analysis results containing domains and GO terms\n    - `time_seconds` (float): Execution time\n\n### Input\u002FOutput\n\n**Input:**\n- `sequence`: Protein sequence (amino acid single-letter code)\n- `sequence_id`: Optional identifier for the query\n- `databases`: List of signature databases (e.g., [\"Pfam\", \"SMART\", \"PRINTS\"])\n- `goterms`: Whether to include Gene Ontology annotations\n\n**Output:**\n- `domains`: List of identified protein domains, each containing:\n  - `name`: Domain or family name\n  - `accession`: Database accession number\n  - `database`: Source database (e.g., \"PFAM\", \"SMART\")\n  - `description`: Functional description\n  - `locations`: List of domain positions in the sequence\n    - `start`: Start position (amino acid number)\n    - `end`: End position (amino acid number)\n    - `score`: Match score (if available)\n- `go_terms`: List of GO annotations, each containing:\n  - `id`: GO identifier (e.g., \"GO:0020037\")\n  - `name`: GO term name\n  - `category`: GO category (MOLECULAR_FUNCTION, BIOLOGICAL_PROCESS, or CELLULAR_COMPONENT)\n\n### Available Signature Databases\n\nInterProScan integrates multiple signature databases:\n- **Pfam**: Protein families based on HMMs\n- **SMART**: Simple Modular Architecture Research Tool\n- **PRINTS**: Protein fingerprints\n- **ProSite**: Protein domains, families, and functional sites\n- **SUPERFAMILY**: Structural and functional annotation\n- And more...\n\nDefault: `[\"Pfam\"]` for fastest results\n\n### Performance Notes\n\n- **Typical execution time**:\n  - Short sequences (~150 aa): 30-60 seconds\n  - Medium sequences (~400 aa): 2-4 minutes\n  - Long sequences (~800+ aa): 5-15 minutes\n- **Timeout recommendation**: Set to at least 900 seconds (15 minutes)\n- **Multiple databases**: Using more databases increases execution time but provides comprehensive annotation\n\n### Use Cases\n\n- Identify functional domains in novel protein sequences\n- Predict protein function from domain composition\n- Locate active sites and binding regions\n- Annotate protein families and superfamilies\n- Obtain GO term annotations for functional analysis\n- Compare domain architecture across homologous proteins\n\n### GO Term Categories\n\n- **MOLECULAR_FUNCTION**: Molecular-level activities (e.g., \"heme binding\", \"catalytic activity\")\n- **BIOLOGICAL_PROCESS**: Biological pathways and processes (e.g., \"oxygen transport\", \"signal transduction\")\n- **CELLULAR_COMPONENT**: Cellular locations (e.g., \"cytoplasm\", \"membrane\")\n","# InterProScan Protein Domain Analysis\n\n## Usage\n\n### 1. MCP Server Definition\n\nUse the same `BioInfoToolsClient` class as defined in the protein-blast-search skill.\n\n### 2. InterProScan Domain Analysis Workflow\n\nThis workflow analyzes protein sequences using InterProScan to identify functional domains, protein families, binding sites, and associated Gene Ontology annotations.\n\n**Workflow Steps:**\n\n1. **Validate Sequence** - Check protein sequence format and length\n2. **Run InterProScan** - Identify domains using multiple signature databases\n3. **Extract Annotations** - Parse domain locations, families, and GO terms\n\n**Implementation:**\n\n```python\nfrom datetime import timedelta\n\n## Initialize client\nclient = BioInfoToolsClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F17\u002FBioInfo-Tools\",\n    \"\u003Cyour-api-key>\"\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\n## Input: Protein sequence to analyze\nprotein_sequence = \"\"\"\nMVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH\n\"\"\"\n\n## Step 1 & 2: Run InterProScan analysis\nresult = await client.session.call_tool(\n    \"interproscan_analyze\",\n    arguments={\n        \"sequence\": protein_sequence.strip(),\n        \"sequence_id\": \"HBB_HUMAN\",        # Optional identifier\n        \"databases\": [\"Pfam\"],              # Signature databases to use\n        \"goterms\": True                     # Include GO term annotations\n    },\n    read_timeout_seconds=timedelta(seconds=900)  # Allow up to 15 minutes\n)\n\n## Step 3: Parse and display results\nresult_data = client.parse_result(result)\n\nif result_data.get(\"success\"):\n    results = result_data.get(\"results\", {})\n    domains = results.get(\"domains\", [])\n    go_terms = results.get(\"go_terms\", [])\n\n    print(f\"✅ InterProScan analysis completed successfully\")\n    print(f\"Execution time: {result_data.get('time_seconds', '?')} seconds\")\n    print(f\"Domains found: {len(domains)}\")\n    print(f\"GO annotations: {len(go_terms)}\\n\")\n\n    # Display domain information\n    if domains:\n        print(\"=== Functional Domains ===\\n\")\n        for i, domain in enumerate(domains, 1):\n            print(f\"{i}. {domain.get('name', 'N\u002FA')}\")\n            print(f\"   Accession: {domain.get('accession', 'N\u002FA')}\")\n            print(f\"   Database: {domain.get('database', 'N\u002FA')}\")\n            if domain.get('description'):\n                print(f\"   Description: {domain.get('description')}\")\n\n            # Display domain locations\n            locations = domain.get('locations', [])\n            if locations:\n                print(f\"   Locations:\")\n                for loc in locations:\n                    print(f\"     - Position {loc.get('start')}-{loc.get('end')} aa\")\n                    if loc.get('score'):\n                        print(f\"       Score: {loc.get('score')}\")\n            print()\n\n    # Display GO annotations\n    if go_terms:\n        print(\"=== Gene Ontology Annotations ===\\n\")\n\n        # Group by category\n        by_category = {}\n        for go in go_terms:\n            category = go.get('category', 'UNKNOWN')\n            if category not in by_category:\n                by_category[category] = []\n            by_category[category].append(go)\n\n        for category, terms in by_category.items():\n            print(f\"{category}:\")\n            for go in terms:\n                print(f\"  - {go.get('id', 'N\u002FA')}: {go.get('name', 'N\u002FA')}\")\n            print()\nelse:\n    print(f\"❌ InterProScan analysis failed: {result_data.get('error', 'Unknown error')}\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**BioInfo-Tools Server:**\n- `interproscan_analyze`: Analyze protein sequence using InterProScan\n  - Args:\n    - `sequence` (str): Protein sequence in amino acid single-letter code\n    - `sequence_id` (str, optional): Identifier for the query sequence\n    - `databases` (list, optional): Signature databases to query (default: [\"Pfam\"])\n    - `goterms` (bool, optional): Include GO term annotations (default: True)\n  - Returns:\n    - `success` (bool): Whether analysis completed successfully\n    - `results` (dict): Analysis results containing domains and GO terms\n    - `time_seconds` (float): Execution time\n\n### Input\u002FOutput\n\n**Input:**\n- `sequence`: Protein sequence (amino acid single-letter code)\n- `sequence_id`: Optional identifier for the query\n- `databases`: List of signature databases (e.g., [\"Pfam\", \"SMART\", \"PRINTS\"])\n- `goterms`: Whether to include Gene Ontology annotations\n\n**Output:**\n- `domains`: List of identified protein domains, each containing:\n  - `name`: Domain or family name\n  - `accession`: Database accession number\n  - `database`: Source database (e.g., \"PFAM\", \"SMART\")\n  - `description`: Functional description\n  - `locations`: List of domain positions in the sequence\n    - `start`: Start position (amino acid number)\n    - `end`: End position (amino acid number)\n    - `score`: Match score (if available)\n- `go_terms`: List of GO annotations, each containing:\n  - `id`: GO identifier (e.g., \"GO:0020037\")\n  - `name`: GO term name\n  - `category`: GO category (MOLECULAR_FUNCTION, BIOLOGICAL_PROCESS, or CELLULAR_COMPONENT)\n\n### Available Signature Databases\n\nInterProScan integrates multiple signature databases:\n- **Pfam**: Protein families based on HMMs\n- **SMART**: Simple Modular Architecture Research Tool\n- **PRINTS**: Protein fingerprints\n- **ProSite**: Protein domains, families, and functional sites\n- **SUPERFAMILY**: Structural and functional annotation\n- And more...\n\nDefault: `[\"Pfam\"]` for fastest results\n\n### Performance Notes\n\n- **Typical execution time**:\n  - Short sequences (~150 aa): 30-60 seconds\n  - Medium sequences (~400 aa): 2-4 minutes\n  - Long sequences (~800+ aa): 5-15 minutes\n- **Timeout recommendation**: Set to at least 900 seconds (15 minutes)\n- **Multiple databases**: Using more databases increases execution time but provides comprehensive annotation\n\n### Use Cases\n\n- Identify functional domains in novel protein sequences\n- Predict protein function from domain composition\n- Locate active sites and binding regions\n- Annotate protein families and superfamilies\n- Obtain GO term annotations for functional analysis\n- Compare domain architecture across homologous proteins\n\n### GO Term Categories\n\n- **MOLECULAR_FUNCTION**: Molecular-level activities (e.g., \"heme binding\", \"catalytic activity\")\n- **BIOLOGICAL_PROCESS**: Biological pathways and processes (e.g., \"oxygen transport\", \"signal transduction\")\n- **CELLULAR_COMPONENT**: Cellular locations (e.g., \"cytoplasm\", \"membrane\")\n",{"i18n":36,"name":5,"license":39,"metadata":40,"description":7},{"zh":37},{"description":38},"用InterProScan分析。","MIT license",{"skill-author":41},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Finterproscan-domain-analysis",10,"https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw","https:\u002F\u002Fgithub.com\u002FInternScience","Shanghai Artificial Intelligence Laboratory","2026-03-20 17:06:10",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add InternScience\u002FDrClaw --skill interproscan-domain-analysis",{"tool":56,"command":57},"bunx","bunx skills add InternScience\u002FDrClaw --skill interproscan-domain-analysis",{"tool":59,"command":60},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill interproscan-domain-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Finterproscan-domain-analysis\u002FSKILL.md",true,[],[65,99,122,138,156,171],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":79,"publishedAt":31},"01M6DMQESTSBW8VJW03QWHMT85","scientific-critical-thinking","Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fthinking\u002Fscientific-critical-thinking\u002FSKILL.md",[71,74],{"categoryNo":72,"categoryName":73,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[80,81,82,83,84,85,86,24,87,88,89,90,91,92,93,94,95,96,97,98],"critical","scientific critical thinking","thinking","design","writing","review","risk","peer review","Bash","quality","read","write","edit","use","evidence","peer","frameworks","teaching","bias",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":31},"01M6DMQ77Z121463E049G2DG8K","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fthinking\u002Fscientific-brainstorming\u002FSKILL.md",[],[106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,93],"brainstorming","scientific brainstorming","research ideation","stage","connections","generation","exploration","do","planning","creative","ideation","research","sessions","data","hypothesis","open",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":132,"publishedAt":31},"01M6DMPV4AMCCTWARGPSFHDWJB","scientific-literature-search","Search scientific literature and research papers using FlowSearch to find relevant academic articles and publications.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fscientific-literature-search\u002FSKILL.md",[128,131],{"categoryNo":129,"categoryName":130,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[133,134,117,135,136,137],"search","scientific literature search","find","Academic","搜索",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":150,"publishedAt":31},"01M6DMPQJP8HVFV54V05D6K9HA","pubmed-article-search","Search PubMed database for scientific articles and publications to retrieve biomedical literature.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fpubmed-article-search\u002FSKILL.md",[144,147],{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":148,"categoryName":149,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000036","内容创作者",[151,152,133,137,153,154,155],"pubmed article search","article","database","pubmed database","biomedical",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":163,"publishedAt":31},"01M6DMPK4H3JQ185M97BRB9KQJ","biomedical-web-search","Search biomedical literature and web content using Tavily search engine for research and clinical information.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fbiomedical-web-search\u002FSKILL.md",[162],{"categoryNo":129,"categoryName":130,"taxonomyType":21,"taxonomyTypeDesc":22},[164,155,165,133,166,167,117,168,169,137,170],"biomedical web search","web search","web","tavily","content","engine","clinical",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":178,"publishedAt":31},"01M6DMNNJH9V1S5PQHZRX0N934","uniprot-protein-retrieval","Retrieve protein sequences and functional information from UniProt database by protein name, enabling protein analysis and bioinformatics workflows.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Funiprot-protein-retrieval\u002FSKILL.md",[177],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[179,153,24,180,181,182,183],"uniprot protein retrieval","uniprot database","bioinformatics","name","workflows"]