[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DMH5G11AXK5DKAFPW01Z8H":3,"skill-same:01M6DMH5G11AXK5DKAFPW01Z8H":63},{"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":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":47,"updatedAt":48,"install":49,"resources":62},"01M6DMH5G11AXK5DKAFPW01Z8H","comprehensive-protein-analysis",null,"Comprehensive protein analysis combining InterProScan domain identification with BLAST similarity search to provide complete functional and evolutionary annotation.","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\u002Fcomprehensive-protein-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],"analysis","comprehensive protein analysis","search","Similarity Search","domain","complete","2026-11-10 10:12:30",false,"---\nname: comprehensive-protein-analysis\ndescription: Comprehensive protein analysis combining InterProScan domain identification\n  with BLAST similarity search to provide complete functional and evolutionary annotation.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 结合InterProScan与。\n---\n\n# Comprehensive Protein 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. Comprehensive Protein Analysis Workflow\n\nThis workflow combines InterProScan domain analysis with BLAST similarity search to provide a complete functional and evolutionary annotation of a protein sequence.\n\n**Workflow Steps:**\n\n1. **Validate Input** - Check protein sequence format\n2. **Run InterProScan** - Identify functional domains and GO terms\n3. **Run BLAST Search** - Find similar sequences and homologs\n4. **Integrate Results** - Combine domain and homology information for comprehensive annotation\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 = \"\"\"\nMALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\n\"\"\"\n\nsequence_id = \"INS_HUMAN\"\n\n## Step 1, 2 & 3: Run comprehensive analysis (InterProScan + BLAST)\nresult = await client.session.call_tool(\n    \"analyze_protein\",\n    arguments={\n        \"sequence\": protein_sequence.strip(),\n        \"sequence_id\": sequence_id,\n        \"databases\": [\"Pfam\"],     # InterProScan databases\n        \"evalue\": 1e-5,             # BLAST E-value threshold (more stringent)\n        \"max_hits\": 10              # BLAST max hits\n    },\n    read_timeout_seconds=timedelta(seconds=1200)  # Allow up to 20 minutes\n)\n\n## Step 4: Parse and display comprehensive results\nresult_data = client.parse_result(result)\n\nprint(f\"{'='*80}\")\nprint(f\"Comprehensive Protein Analysis: {sequence_id}\")\nprint(f\"{'='*80}\\n\")\n\n# InterProScan Results\nips_result = result_data.get(\"interproscan\", {})\nif ips_result.get(\"success\"):\n    ips_data = ips_result.get(\"results\", {})\n    domains = ips_data.get('domains', [])\n    go_terms = ips_data.get('go_terms', [])\n\n    print(\"=== DOMAIN ANALYSIS (InterProScan) ===\")\n    print(f\"Execution time: {ips_result.get('time_seconds', '?')} seconds\")\n    print(f\"Domains found: {len(domains)}\")\n    print(f\"GO annotations: {len(go_terms)}\\n\")\n\n    if domains:\n        print(\"Functional Domains:\")\n        for domain in domains:\n            print(f\"  • {domain.get('name', 'N\u002FA')} ({domain.get('database', 'N\u002FA')})\")\n            if domain.get('description'):\n                print(f\"    Description: {domain.get('description')}\")\n            locations = domain.get('locations', [])\n            if locations:\n                loc = locations[0]\n                print(f\"    Position: {loc.get('start')}-{loc.get('end')} aa\")\n        print()\n\n    if go_terms:\n        print(\"Gene Ontology Annotations:\")\n        for go in go_terms[:5]:  # Show top 5\n            print(f\"  • {go.get('id', 'N\u002FA')}: {go.get('name', 'N\u002FA')}\")\n            print(f\"    Category: {go.get('category', 'N\u002FA')}\")\n        if len(go_terms) > 5:\n            print(f\"  ... and {len(go_terms) - 5} more\")\n        print()\nelse:\n    print(f\"❌ InterProScan failed: {ips_result.get('error', 'Unknown')}\\n\")\n\n# BLAST Results\nblast_result = result_data.get(\"blast\", {})\nif blast_result.get(\"success\"):\n    hits = blast_result.get('hits', [])\n\n    print(\"=== HOMOLOGY SEARCH (BLAST) ===\")\n    print(f\"Execution time: {blast_result.get('time_seconds', '?')} seconds\")\n    print(f\"Similar sequences found: {blast_result.get('total_hits', 0)}\")\n    print(f\"E-value threshold: {1e-5}\\n\")\n\n    if hits:\n        print(\"Top Homologous Proteins:\")\n        for i, hit in enumerate(hits[:5], 1):\n            print(f\"  {i}. {hit['uniprot_id']} - {hit.get('organism', 'N\u002FA')}\")\n            print(f\"     Description: {hit['description']}\")\n            print(f\"     Identity: {hit['identity_percent']:.1f}%, E-value: {hit['evalue']:.2e}\")\n        if len(hits) > 5:\n            print(f\"  ... and {len(hits) - 5} more matches\")\n        print()\n    else:\n        print(\"No significant homologs found (E-value threshold may be too stringent)\\n\")\nelse:\n    print(f\"❌ BLAST failed: {blast_result.get('error', 'Unknown')}\\n\")\n\n# Summary\nprint(\"=== FUNCTIONAL SUMMARY ===\")\nif domains:\n    print(f\"Protein Family: {domains[0].get('name', 'Unknown')}\")\nif hits:\n    most_similar = hits[0]\n    print(f\"Most Similar Protein: {most_similar['uniprot_id']} ({most_similar['identity_percent']:.1f}% identity)\")\n    print(f\"Organism: {most_similar.get('organism', 'Unknown')}\")\nprint(f\"{'='*80}\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**BioInfo-Tools Server:**\n- `analyze_protein`: Comprehensive protein analysis combining InterProScan and BLAST\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): InterProScan databases (default: [\"Pfam\"])\n    - `evalue` (float, optional): BLAST E-value threshold (default: 0.01)\n    - `max_hits` (int, optional): Maximum BLAST hits (default: 10)\n  - Returns:\n    - `interproscan` (dict): InterProScan analysis results\n      - `success` (bool): Whether InterProScan completed\n      - `results` (dict): Domains and GO terms\n      - `time_seconds` (float): Execution time\n    - `blast` (dict): BLAST search results\n      - `success` (bool): Whether BLAST completed\n      - `hits` (list): Similar proteins\n      - `total_hits` (int): Number of matches\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 InterProScan databases to query\n- `evalue`: BLAST E-value threshold (lower = more stringent)\n- `max_hits`: Maximum number of BLAST hits to return\n\n**Output:**\n- **InterProScan Results**:\n  - Functional domains with positions\n  - Protein family classifications\n  - Gene Ontology annotations\n- **BLAST Results**:\n  - Homologous proteins across species\n  - Sequence identity and alignment statistics\n  - Evolutionary relationships\n\n### Analysis Strategy\n\nThis comprehensive approach provides:\n\n1. **Structural Information** (InterProScan):\n   - Domain architecture and organization\n   - Functional motifs and active sites\n   - Protein family membership\n\n2. **Evolutionary Context** (BLAST):\n   - Homologs in other species\n   - Sequence conservation patterns\n   - Potential orthologs and paralogs\n\n3. **Functional Prediction**:\n   - Combining domain and homology information\n   - GO term annotations for molecular function\n   - Biological process involvement\n\n### Performance Notes\n\n- **Total execution time**: 2-20 minutes depending on sequence length\n  - InterProScan: 30 seconds to 15 minutes\n  - BLAST: 10-90 seconds\n  - Both run sequentially in this workflow\n- **Timeout recommendation**: Set to at least 1200 seconds (20 minutes)\n- **E-value tuning**: Use lower E-values (e.g., 1e-10) for highly conserved proteins, higher (e.g., 0.01) for divergent families\n\n### Use Cases\n\n- Complete functional annotation of unknown proteins\n- Validate predicted protein functions\n- Study protein evolution and conservation\n- Identify potential drug targets\n- Annotate proteomes and genome sequences\n- Compare protein function across species\n\n### Interpretation Tips\n\n- **High domain coverage + high homology**: Well-characterized protein with known function\n- **Domains but no homologs**: Novel protein with conserved domains, function can be inferred from domains\n- **Homologs but no domains**: May need more sensitive domain detection or represents a novel fold\n- **Neither domains nor homologs**: Potentially novel protein, may require experimental characterization\n","# Comprehensive Protein 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. Comprehensive Protein Analysis Workflow\n\nThis workflow combines InterProScan domain analysis with BLAST similarity search to provide a complete functional and evolutionary annotation of a protein sequence.\n\n**Workflow Steps:**\n\n1. **Validate Input** - Check protein sequence format\n2. **Run InterProScan** - Identify functional domains and GO terms\n3. **Run BLAST Search** - Find similar sequences and homologs\n4. **Integrate Results** - Combine domain and homology information for comprehensive annotation\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 = \"\"\"\nMALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\n\"\"\"\n\nsequence_id = \"INS_HUMAN\"\n\n## Step 1, 2 & 3: Run comprehensive analysis (InterProScan + BLAST)\nresult = await client.session.call_tool(\n    \"analyze_protein\",\n    arguments={\n        \"sequence\": protein_sequence.strip(),\n        \"sequence_id\": sequence_id,\n        \"databases\": [\"Pfam\"],     # InterProScan databases\n        \"evalue\": 1e-5,             # BLAST E-value threshold (more stringent)\n        \"max_hits\": 10              # BLAST max hits\n    },\n    read_timeout_seconds=timedelta(seconds=1200)  # Allow up to 20 minutes\n)\n\n## Step 4: Parse and display comprehensive results\nresult_data = client.parse_result(result)\n\nprint(f\"{'='*80}\")\nprint(f\"Comprehensive Protein Analysis: {sequence_id}\")\nprint(f\"{'='*80}\\n\")\n\n# InterProScan Results\nips_result = result_data.get(\"interproscan\", {})\nif ips_result.get(\"success\"):\n    ips_data = ips_result.get(\"results\", {})\n    domains = ips_data.get('domains', [])\n    go_terms = ips_data.get('go_terms', [])\n\n    print(\"=== DOMAIN ANALYSIS (InterProScan) ===\")\n    print(f\"Execution time: {ips_result.get('time_seconds', '?')} seconds\")\n    print(f\"Domains found: {len(domains)}\")\n    print(f\"GO annotations: {len(go_terms)}\\n\")\n\n    if domains:\n        print(\"Functional Domains:\")\n        for domain in domains:\n            print(f\"  • {domain.get('name', 'N\u002FA')} ({domain.get('database', 'N\u002FA')})\")\n            if domain.get('description'):\n                print(f\"    Description: {domain.get('description')}\")\n            locations = domain.get('locations', [])\n            if locations:\n                loc = locations[0]\n                print(f\"    Position: {loc.get('start')}-{loc.get('end')} aa\")\n        print()\n\n    if go_terms:\n        print(\"Gene Ontology Annotations:\")\n        for go in go_terms[:5]:  # Show top 5\n            print(f\"  • {go.get('id', 'N\u002FA')}: {go.get('name', 'N\u002FA')}\")\n            print(f\"    Category: {go.get('category', 'N\u002FA')}\")\n        if len(go_terms) > 5:\n            print(f\"  ... and {len(go_terms) - 5} more\")\n        print()\nelse:\n    print(f\"❌ InterProScan failed: {ips_result.get('error', 'Unknown')}\\n\")\n\n# BLAST Results\nblast_result = result_data.get(\"blast\", {})\nif blast_result.get(\"success\"):\n    hits = blast_result.get('hits', [])\n\n    print(\"=== HOMOLOGY SEARCH (BLAST) ===\")\n    print(f\"Execution time: {blast_result.get('time_seconds', '?')} seconds\")\n    print(f\"Similar sequences found: {blast_result.get('total_hits', 0)}\")\n    print(f\"E-value threshold: {1e-5}\\n\")\n\n    if hits:\n        print(\"Top Homologous Proteins:\")\n        for i, hit in enumerate(hits[:5], 1):\n            print(f\"  {i}. {hit['uniprot_id']} - {hit.get('organism', 'N\u002FA')}\")\n            print(f\"     Description: {hit['description']}\")\n            print(f\"     Identity: {hit['identity_percent']:.1f}%, E-value: {hit['evalue']:.2e}\")\n        if len(hits) > 5:\n            print(f\"  ... and {len(hits) - 5} more matches\")\n        print()\n    else:\n        print(\"No significant homologs found (E-value threshold may be too stringent)\\n\")\nelse:\n    print(f\"❌ BLAST failed: {blast_result.get('error', 'Unknown')}\\n\")\n\n# Summary\nprint(\"=== FUNCTIONAL SUMMARY ===\")\nif domains:\n    print(f\"Protein Family: {domains[0].get('name', 'Unknown')}\")\nif hits:\n    most_similar = hits[0]\n    print(f\"Most Similar Protein: {most_similar['uniprot_id']} ({most_similar['identity_percent']:.1f}% identity)\")\n    print(f\"Organism: {most_similar.get('organism', 'Unknown')}\")\nprint(f\"{'='*80}\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**BioInfo-Tools Server:**\n- `analyze_protein`: Comprehensive protein analysis combining InterProScan and BLAST\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): InterProScan databases (default: [\"Pfam\"])\n    - `evalue` (float, optional): BLAST E-value threshold (default: 0.01)\n    - `max_hits` (int, optional): Maximum BLAST hits (default: 10)\n  - Returns:\n    - `interproscan` (dict): InterProScan analysis results\n      - `success` (bool): Whether InterProScan completed\n      - `results` (dict): Domains and GO terms\n      - `time_seconds` (float): Execution time\n    - `blast` (dict): BLAST search results\n      - `success` (bool): Whether BLAST completed\n      - `hits` (list): Similar proteins\n      - `total_hits` (int): Number of matches\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 InterProScan databases to query\n- `evalue`: BLAST E-value threshold (lower = more stringent)\n- `max_hits`: Maximum number of BLAST hits to return\n\n**Output:**\n- **InterProScan Results**:\n  - Functional domains with positions\n  - Protein family classifications\n  - Gene Ontology annotations\n- **BLAST Results**:\n  - Homologous proteins across species\n  - Sequence identity and alignment statistics\n  - Evolutionary relationships\n\n### Analysis Strategy\n\nThis comprehensive approach provides:\n\n1. **Structural Information** (InterProScan):\n   - Domain architecture and organization\n   - Functional motifs and active sites\n   - Protein family membership\n\n2. **Evolutionary Context** (BLAST):\n   - Homologs in other species\n   - Sequence conservation patterns\n   - Potential orthologs and paralogs\n\n3. **Functional Prediction**:\n   - Combining domain and homology information\n   - GO term annotations for molecular function\n   - Biological process involvement\n\n### Performance Notes\n\n- **Total execution time**: 2-20 minutes depending on sequence length\n  - InterProScan: 30 seconds to 15 minutes\n  - BLAST: 10-90 seconds\n  - Both run sequentially in this workflow\n- **Timeout recommendation**: Set to at least 1200 seconds (20 minutes)\n- **E-value tuning**: Use lower E-values (e.g., 1e-10) for highly conserved proteins, higher (e.g., 0.01) for divergent families\n\n### Use Cases\n\n- Complete functional annotation of unknown proteins\n- Validate predicted protein functions\n- Study protein evolution and conservation\n- Identify potential drug targets\n- Annotate proteomes and genome sequences\n- Compare protein function across species\n\n### Interpretation Tips\n\n- **High domain coverage + high homology**: Well-characterized protein with known function\n- **Domains but no homologs**: Novel protein with conserved domains, function can be inferred from domains\n- **Homologs but no domains**: May need more sensitive domain detection or represents a novel fold\n- **Neither domains nor homologs**: Potentially novel protein, may require experimental characterization\n",{"i18n":35,"name":5,"license":38,"metadata":39,"description":7},{"zh":36},{"description":37},"结合InterProScan与。","MIT license",{"skill-author":40},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Fcomprehensive-protein-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":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add InternScience\u002FDrClaw --skill comprehensive-protein-analysis",{"tool":55,"command":56},"bunx","bunx skills add InternScience\u002FDrClaw --skill comprehensive-protein-analysis",{"tool":58,"command":59},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill comprehensive-protein-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Fcomprehensive-protein-analysis\u002FSKILL.md",true,[],[64,98,121,136,154,169],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":78,"publishedAt":30},"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",[70,73],{"categoryNo":71,"categoryName":72,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":74,"categoryName":75,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[79,80,81,82,83,84,85,24,86,87,88,89,90,91,92,93,94,95,96,97],"critical","scientific critical thinking","thinking","design","writing","review","risk","peer review","Bash","quality","read","write","edit","use","evidence","peer","frameworks","teaching","bias",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":30},"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",[],[105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,92],"brainstorming","scientific brainstorming","research ideation","stage","connections","generation","exploration","do","planning","creative","ideation","research","sessions","data","hypothesis","open",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":131,"publishedAt":30},"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",[127,130],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[26,132,116,133,134,135],"scientific literature search","find","Academic","搜索",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":148,"publishedAt":30},"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",[142,145],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":146,"categoryName":147,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000036","内容创作者",[149,150,26,135,151,152,153],"pubmed article search","article","database","pubmed database","biomedical",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":161,"publishedAt":30},"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",[160],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},[162,153,163,26,164,165,116,166,167,135,168],"biomedical web search","web search","web","tavily","content","engine","clinical",{"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":176,"publishedAt":30},"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",[175],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[177,151,24,178,179,180,181],"uniprot protein retrieval","uniprot database","bioinformatics","name","workflows"]