[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JKXFE33Z2339FR1MS0KXB5":3,"skill-same:01M6JKXFE33Z2339FR1MS0KXB5":76},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6JKXFE33Z2339FR1MS0KXB5","pdb-database",null,"Access RCSB PDB for 3D protein\u002Fnucleic acid structures. Search by text\u002Fsequence\u002Fstructure, download coordinates (PDB\u002FmmCIF), retrieve metadata, for structural biology and drug discovery.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpdb-database\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"pdb database","pdb","database","coordinates","sequence","for","biology","drug discovery","structural","structural biology","acid","structure","text","download","access","discovery","3D","metadata","search","2026-11-10 10:12:30",false,"---\nname: pdb-database\ndescription: \"Access RCSB PDB for 3D protein\u002Fnucleic acid structures. Search by text\u002Fsequence\u002Fstructure, download coordinates (PDB\u002FmmCIF), retrieve metadata, for structural biology and drug discovery.\"\n---\n\n# PDB Database\n\n## Overview\n\nRCSB PDB is the worldwide repository for 3D structural data of biological macromolecules. Search for structures, retrieve coordinates and metadata, perform sequence and structure similarity searches across 200,000+ experimentally determined structures and computed models.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Searching for protein or nucleic acid 3D structures by text, sequence, or structural similarity\n- Downloading coordinate files in PDB, mmCIF, or BinaryCIF formats\n- Retrieving structural metadata, experimental methods, or quality metrics\n- Performing batch operations across multiple structures\n- Integrating PDB data into computational workflows for drug discovery, protein engineering, or structural biology research\n\n## Core Capabilities\n\n### 1. Searching for Structures\n\nFind PDB entries using various search criteria:\n\n**Text Search:** Search by protein name, keywords, or descriptions\n```python\nfrom rcsbapi.search import TextQuery\nquery = TextQuery(\"hemoglobin\")\nresults = list(query())\nprint(f\"Found {len(results)} structures\")\n```\n\n**Attribute Search:** Query specific properties (organism, resolution, method, etc.)\n```python\nfrom rcsbapi.search import AttributeQuery\nfrom rcsbapi.search.attrs import rcsb_entity_source_organism\n\n# Find human protein structures\nquery = AttributeQuery(\n    attribute=rcsb_entity_source_organism.scientific_name,\n    operator=\"exact_match\",\n    value=\"Homo sapiens\"\n)\nresults = list(query())\n```\n\n**Sequence Similarity:** Find structures similar to a given sequence\n```python\nfrom rcsbapi.search import SequenceQuery\n\nquery = SequenceQuery(\n    value=\"MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM\",\n    evalue_cutoff=0.1,\n    identity_cutoff=0.9\n)\nresults = list(query())\n```\n\n**Structure Similarity:** Find structures with similar 3D geometry\n```python\nfrom rcsbapi.search import StructSimilarityQuery\n\nquery = StructSimilarityQuery(\n    structure_search_type=\"entry\",\n    entry_id=\"4HHB\"  # Hemoglobin\n)\nresults = list(query())\n```\n\n**Combining Queries:** Use logical operators to build complex searches\n```python\nfrom rcsbapi.search import TextQuery, AttributeQuery\nfrom rcsbapi.search.attrs import rcsb_entry_info\n\n# High-resolution human proteins\nquery1 = AttributeQuery(\n    attribute=rcsb_entity_source_organism.scientific_name,\n    operator=\"exact_match\",\n    value=\"Homo sapiens\"\n)\nquery2 = AttributeQuery(\n    attribute=rcsb_entry_info.resolution_combined,\n    operator=\"less\",\n    value=2.0\n)\ncombined_query = query1 & query2  # AND operation\nresults = list(combined_query())\n```\n\n### 2. Retrieving Structure Data\n\nAccess detailed information about specific PDB entries:\n\n**Basic Entry Information:**\n```python\nfrom rcsbapi.data import Schema, fetch\n\n# Get entry-level data\nentry_data = fetch(\"4HHB\", schema=Schema.ENTRY)\nprint(entry_data[\"struct\"][\"title\"])\nprint(entry_data[\"exptl\"][0][\"method\"])\n```\n\n**Polymer Entity Information:**\n```python\n# Get protein\u002Fnucleic acid information\nentity_data = fetch(\"4HHB_1\", schema=Schema.POLYMER_ENTITY)\nprint(entity_data[\"entity_poly\"][\"pdbx_seq_one_letter_code\"])\n```\n\n**Using GraphQL for Flexible Queries:**\n```python\nfrom rcsbapi.data import fetch\n\n# Custom GraphQL query\nquery = \"\"\"\n{\n  entry(entry_id: \"4HHB\") {\n    struct {\n      title\n    }\n    exptl {\n      method\n    }\n    rcsb_entry_info {\n      resolution_combined\n      deposited_atom_count\n    }\n  }\n}\n\"\"\"\ndata = fetch(query_type=\"graphql\", query=query)\n```\n\n### 3. Downloading Structure Files\n\nRetrieve coordinate files in various formats:\n\n**Download Methods:**\n- **PDB format** (legacy text format): `https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{PDB_ID}.pdb`\n- **mmCIF format** (modern standard): `https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{PDB_ID}.cif`\n- **BinaryCIF** (compressed binary): Use ModelServer API for efficient access\n- **Biological assembly**: `https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{PDB_ID}.pdb1` (for assembly 1)\n\n**Example Download:**\n```python\nimport requests\n\npdb_id = \"4HHB\"\n\n# Download PDB format\npdb_url = f\"https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{pdb_id}.pdb\"\nresponse = requests.get(pdb_url)\nwith open(f\"{pdb_id}.pdb\", \"w\") as f:\n    f.write(response.text)\n\n# Download mmCIF format\ncif_url = f\"https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{pdb_id}.cif\"\nresponse = requests.get(cif_url)\nwith open(f\"{pdb_id}.cif\", \"w\") as f:\n    f.write(response.text)\n```\n\n### 4. Working with Structure Data\n\nCommon operations with retrieved structures:\n\n**Parse and Analyze Coordinates:**\nUse BioPython or other structural biology libraries to work with downloaded files:\n```python\nfrom Bio.PDB import PDBParser\n\nparser = PDBParser()\nstructure = parser.get_structure(\"protein\", \"4HHB.pdb\")\n\n# Iterate through atoms\nfor model in structure:\n    for chain in model:\n        for residue in chain:\n            for atom in residue:\n                print(atom.get_coord())\n```\n\n**Extract Metadata:**\n```python\nfrom rcsbapi.data import fetch, Schema\n\n# Get experimental details\ndata = fetch(\"4HHB\", schema=Schema.ENTRY)\n\nresolution = data.get(\"rcsb_entry_info\", {}).get(\"resolution_combined\")\nmethod = data.get(\"exptl\", [{}])[0].get(\"method\")\ndeposition_date = data.get(\"rcsb_accession_info\", {}).get(\"deposit_date\")\n\nprint(f\"Resolution: {resolution} Å\")\nprint(f\"Method: {method}\")\nprint(f\"Deposited: {deposition_date}\")\n```\n\n### 5. Batch Operations\n\nProcess multiple structures efficiently:\n\n```python\nfrom rcsbapi.data import fetch, Schema\n\npdb_ids = [\"4HHB\", \"1MBN\", \"1GZX\"]  # Hemoglobin, myoglobin, etc.\n\nresults = {}\nfor pdb_id in pdb_ids:\n    try:\n        data = fetch(pdb_id, schema=Schema.ENTRY)\n        results[pdb_id] = {\n            \"title\": data[\"struct\"][\"title\"],\n            \"resolution\": data.get(\"rcsb_entry_info\", {}).get(\"resolution_combined\"),\n            \"organism\": data.get(\"rcsb_entity_source_organism\", [{}])[0].get(\"scientific_name\")\n        }\n    except Exception as e:\n        print(f\"Error fetching {pdb_id}: {e}\")\n\n# Display results\nfor pdb_id, info in results.items():\n    print(f\"\\n{pdb_id}: {info['title']}\")\n    print(f\"  Resolution: {info['resolution']} Å\")\n    print(f\"  Organism: {info['organism']}\")\n```\n\n## Python Package Installation\n\nInstall the official RCSB PDB Python API client:\n\n```bash\n# Current recommended package\nuv pip install rcsb-api\n\n# For legacy code (deprecated, use rcsb-api instead)\nuv pip install rcsbsearchapi\n```\n\nThe `rcsb-api` package provides unified access to both Search and Data APIs through the `rcsbapi.search` and `rcsbapi.data` modules.\n\n## Common Use Cases\n\n### Drug Discovery\n- Search for structures of drug targets\n- Analyze ligand binding sites\n- Compare protein-ligand complexes\n- Identify similar binding pockets\n\n### Protein Engineering\n- Find homologous structures for modeling\n- Analyze sequence-structure relationships\n- Compare mutant structures\n- Study protein stability and dynamics\n\n### Structural Biology Research\n- Download structures for computational analysis\n- Build structure-based alignments\n- Analyze structural features (secondary structure, domains)\n- Compare experimental methods and quality metrics\n\n### Education and Visualization\n- Retrieve structures for teaching\n- Generate molecular visualizations\n- Explore structure-function relationships\n- Study evolutionary conservation\n\n## Key Concepts\n\n**PDB ID:** Unique 4-character identifier (e.g., \"4HHB\") for each structure entry. AlphaFold and ModelArchive entries start with \"AF_\" or \"MA_\" prefixes.\n\n**mmCIF\u002FPDBx:** Modern file format that uses key-value structure, replacing legacy PDB format for large structures.\n\n**Biological Assembly:** The functional form of a macromolecule, which may contain multiple copies of chains from the asymmetric unit.\n\n**Resolution:** Measure of detail in crystallographic structures (lower values = higher detail). Typical range: 1.5-3.5 Å for high-quality structures.\n\n**Entity:** A unique molecular component in a structure (protein chain, DNA, ligand, etc.).\n\n## Resources\n\nThis skill includes reference documentation in the `references\u002F` directory:\n\n### references\u002Fapi_reference.md\nComprehensive API documentation covering:\n- Detailed API endpoint specifications\n- Advanced query patterns and examples\n- Data schema reference\n- Rate limiting and best practices\n- Troubleshooting common issues\n\nUse this reference when you need in-depth information about API capabilities, complex query construction, or detailed data schema information.\n\n## Additional Resources\n\n- **RCSB PDB Website:** https:\u002F\u002Fwww.rcsb.org\n- **PDB-101 Educational Portal:** https:\u002F\u002Fpdb101.rcsb.org\n- **API Documentation:** https:\u002F\u002Fwww.rcsb.org\u002Fdocs\u002Fprogrammatic-access\u002Fweb-apis-overview\n- **Python Package Docs:** https:\u002F\u002Frcsbapi.readthedocs.io\u002F\n- **Data API Documentation:** https:\u002F\u002Fdata.rcsb.org\u002F\n- **GitHub Repository:** https:\u002F\u002Fgithub.com\u002Frcsb\u002Fpy-rcsb-api\n","# PDB Database\n\n## Overview\n\nRCSB PDB is the worldwide repository for 3D structural data of biological macromolecules. Search for structures, retrieve coordinates and metadata, perform sequence and structure similarity searches across 200,000+ experimentally determined structures and computed models.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Searching for protein or nucleic acid 3D structures by text, sequence, or structural similarity\n- Downloading coordinate files in PDB, mmCIF, or BinaryCIF formats\n- Retrieving structural metadata, experimental methods, or quality metrics\n- Performing batch operations across multiple structures\n- Integrating PDB data into computational workflows for drug discovery, protein engineering, or structural biology research\n\n## Core Capabilities\n\n### 1. Searching for Structures\n\nFind PDB entries using various search criteria:\n\n**Text Search:** Search by protein name, keywords, or descriptions\n```python\nfrom rcsbapi.search import TextQuery\nquery = TextQuery(\"hemoglobin\")\nresults = list(query())\nprint(f\"Found {len(results)} structures\")\n```\n\n**Attribute Search:** Query specific properties (organism, resolution, method, etc.)\n```python\nfrom rcsbapi.search import AttributeQuery\nfrom rcsbapi.search.attrs import rcsb_entity_source_organism\n\n# Find human protein structures\nquery = AttributeQuery(\n    attribute=rcsb_entity_source_organism.scientific_name,\n    operator=\"exact_match\",\n    value=\"Homo sapiens\"\n)\nresults = list(query())\n```\n\n**Sequence Similarity:** Find structures similar to a given sequence\n```python\nfrom rcsbapi.search import SequenceQuery\n\nquery = SequenceQuery(\n    value=\"MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM\",\n    evalue_cutoff=0.1,\n    identity_cutoff=0.9\n)\nresults = list(query())\n```\n\n**Structure Similarity:** Find structures with similar 3D geometry\n```python\nfrom rcsbapi.search import StructSimilarityQuery\n\nquery = StructSimilarityQuery(\n    structure_search_type=\"entry\",\n    entry_id=\"4HHB\"  # Hemoglobin\n)\nresults = list(query())\n```\n\n**Combining Queries:** Use logical operators to build complex searches\n```python\nfrom rcsbapi.search import TextQuery, AttributeQuery\nfrom rcsbapi.search.attrs import rcsb_entry_info\n\n# High-resolution human proteins\nquery1 = AttributeQuery(\n    attribute=rcsb_entity_source_organism.scientific_name,\n    operator=\"exact_match\",\n    value=\"Homo sapiens\"\n)\nquery2 = AttributeQuery(\n    attribute=rcsb_entry_info.resolution_combined,\n    operator=\"less\",\n    value=2.0\n)\ncombined_query = query1 & query2  # AND operation\nresults = list(combined_query())\n```\n\n### 2. Retrieving Structure Data\n\nAccess detailed information about specific PDB entries:\n\n**Basic Entry Information:**\n```python\nfrom rcsbapi.data import Schema, fetch\n\n# Get entry-level data\nentry_data = fetch(\"4HHB\", schema=Schema.ENTRY)\nprint(entry_data[\"struct\"][\"title\"])\nprint(entry_data[\"exptl\"][0][\"method\"])\n```\n\n**Polymer Entity Information:**\n```python\n# Get protein\u002Fnucleic acid information\nentity_data = fetch(\"4HHB_1\", schema=Schema.POLYMER_ENTITY)\nprint(entity_data[\"entity_poly\"][\"pdbx_seq_one_letter_code\"])\n```\n\n**Using GraphQL for Flexible Queries:**\n```python\nfrom rcsbapi.data import fetch\n\n# Custom GraphQL query\nquery = \"\"\"\n{\n  entry(entry_id: \"4HHB\") {\n    struct {\n      title\n    }\n    exptl {\n      method\n    }\n    rcsb_entry_info {\n      resolution_combined\n      deposited_atom_count\n    }\n  }\n}\n\"\"\"\ndata = fetch(query_type=\"graphql\", query=query)\n```\n\n### 3. Downloading Structure Files\n\nRetrieve coordinate files in various formats:\n\n**Download Methods:**\n- **PDB format** (legacy text format): `https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{PDB_ID}.pdb`\n- **mmCIF format** (modern standard): `https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{PDB_ID}.cif`\n- **BinaryCIF** (compressed binary): Use ModelServer API for efficient access\n- **Biological assembly**: `https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{PDB_ID}.pdb1` (for assembly 1)\n\n**Example Download:**\n```python\nimport requests\n\npdb_id = \"4HHB\"\n\n# Download PDB format\npdb_url = f\"https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{pdb_id}.pdb\"\nresponse = requests.get(pdb_url)\nwith open(f\"{pdb_id}.pdb\", \"w\") as f:\n    f.write(response.text)\n\n# Download mmCIF format\ncif_url = f\"https:\u002F\u002Ffiles.rcsb.org\u002Fdownload\u002F{pdb_id}.cif\"\nresponse = requests.get(cif_url)\nwith open(f\"{pdb_id}.cif\", \"w\") as f:\n    f.write(response.text)\n```\n\n### 4. Working with Structure Data\n\nCommon operations with retrieved structures:\n\n**Parse and Analyze Coordinates:**\nUse BioPython or other structural biology libraries to work with downloaded files:\n```python\nfrom Bio.PDB import PDBParser\n\nparser = PDBParser()\nstructure = parser.get_structure(\"protein\", \"4HHB.pdb\")\n\n# Iterate through atoms\nfor model in structure:\n    for chain in model:\n        for residue in chain:\n            for atom in residue:\n                print(atom.get_coord())\n```\n\n**Extract Metadata:**\n```python\nfrom rcsbapi.data import fetch, Schema\n\n# Get experimental details\ndata = fetch(\"4HHB\", schema=Schema.ENTRY)\n\nresolution = data.get(\"rcsb_entry_info\", {}).get(\"resolution_combined\")\nmethod = data.get(\"exptl\", [{}])[0].get(\"method\")\ndeposition_date = data.get(\"rcsb_accession_info\", {}).get(\"deposit_date\")\n\nprint(f\"Resolution: {resolution} Å\")\nprint(f\"Method: {method}\")\nprint(f\"Deposited: {deposition_date}\")\n```\n\n### 5. Batch Operations\n\nProcess multiple structures efficiently:\n\n```python\nfrom rcsbapi.data import fetch, Schema\n\npdb_ids = [\"4HHB\", \"1MBN\", \"1GZX\"]  # Hemoglobin, myoglobin, etc.\n\nresults = {}\nfor pdb_id in pdb_ids:\n    try:\n        data = fetch(pdb_id, schema=Schema.ENTRY)\n        results[pdb_id] = {\n            \"title\": data[\"struct\"][\"title\"],\n            \"resolution\": data.get(\"rcsb_entry_info\", {}).get(\"resolution_combined\"),\n            \"organism\": data.get(\"rcsb_entity_source_organism\", [{}])[0].get(\"scientific_name\")\n        }\n    except Exception as e:\n        print(f\"Error fetching {pdb_id}: {e}\")\n\n# Display results\nfor pdb_id, info in results.items():\n    print(f\"\\n{pdb_id}: {info['title']}\")\n    print(f\"  Resolution: {info['resolution']} Å\")\n    print(f\"  Organism: {info['organism']}\")\n```\n\n## Python Package Installation\n\nInstall the official RCSB PDB Python API client:\n\n```bash\n# Current recommended package\nuv pip install rcsb-api\n\n# For legacy code (deprecated, use rcsb-api instead)\nuv pip install rcsbsearchapi\n```\n\nThe `rcsb-api` package provides unified access to both Search and Data APIs through the `rcsbapi.search` and `rcsbapi.data` modules.\n\n## Common Use Cases\n\n### Drug Discovery\n- Search for structures of drug targets\n- Analyze ligand binding sites\n- Compare protein-ligand complexes\n- Identify similar binding pockets\n\n### Protein Engineering\n- Find homologous structures for modeling\n- Analyze sequence-structure relationships\n- Compare mutant structures\n- Study protein stability and dynamics\n\n### Structural Biology Research\n- Download structures for computational analysis\n- Build structure-based alignments\n- Analyze structural features (secondary structure, domains)\n- Compare experimental methods and quality metrics\n\n### Education and Visualization\n- Retrieve structures for teaching\n- Generate molecular visualizations\n- Explore structure-function relationships\n- Study evolutionary conservation\n\n## Key Concepts\n\n**PDB ID:** Unique 4-character identifier (e.g., \"4HHB\") for each structure entry. AlphaFold and ModelArchive entries start with \"AF_\" or \"MA_\" prefixes.\n\n**mmCIF\u002FPDBx:** Modern file format that uses key-value structure, replacing legacy PDB format for large structures.\n\n**Biological Assembly:** The functional form of a macromolecule, which may contain multiple copies of chains from the asymmetric unit.\n\n**Resolution:** Measure of detail in crystallographic structures (lower values = higher detail). Typical range: 1.5-3.5 Å for high-quality structures.\n\n**Entity:** A unique molecular component in a structure (protein chain, DNA, ligand, etc.).\n\n## Resources\n\nThis skill includes reference documentation in the `references\u002F` directory:\n\n### references\u002Fapi_reference.md\nComprehensive API documentation covering:\n- Detailed API endpoint specifications\n- Advanced query patterns and examples\n- Data schema reference\n- Rate limiting and best practices\n- Troubleshooting common issues\n\nUse this reference when you need in-depth information about API capabilities, complex query construction, or detailed data schema information.\n\n## Additional Resources\n\n- **RCSB PDB Website:** https:\u002F\u002Fwww.rcsb.org\n- **PDB-101 Educational Portal:** https:\u002F\u002Fpdb101.rcsb.org\n- **API Documentation:** https:\u002F\u002Fwww.rcsb.org\u002Fdocs\u002Fprogrammatic-access\u002Fweb-apis-overview\n- **Python Package Docs:** https:\u002F\u002Frcsbapi.readthedocs.io\u002F\n- **Data API Documentation:** https:\u002F\u002Fdata.rcsb.org\u002F\n- **GitHub Repository:** https:\u002F\u002Fgithub.com\u002Frcsb\u002Fpy-rcsb-api\n",{"name":5,"description":7},"main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpdb-database",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 10:12:59",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill pdb-database",{"tool":62,"command":63},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill pdb-database",{"tool":65,"command":66},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill pdb-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpdb-database\u002FSKILL.md",true,[70],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fapi_reference.md","REFERENCE","参考文档",15244,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpdb-database\u002Freferences\u002Fapi_reference.md",[77,106,127,151,171,197],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":86,"publishedAt":43},"01M6JMRJR3BE0HGWFWDZSB853S","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[83],{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,29,105],"zapier make patterns","patterns","make","zapier","writing","workflow","automation","code","integrations","business","use","power","complexity","platform","apps","how to","No-Code","which","spawner",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":43},"01M6JMR8YK6SWPCTG1MCQTMN9S","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[108,113,114,115,116,117,118,119,120,121,122,123,97,124,125,126],"cli","github","commit","pull request","openai","pull","push","flow","open","gh","user","only","stage","github cli",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":134,"publishedAt":43},"01M6JMQNFE64ZASQK90HARRXW8","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[133],{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},[92,93,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,29,105],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":43},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[158,159,160,161,162,138,163,139,140,164,97,165,166,167,168,169,170,150,29,105],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"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":180,"publishedAt":43},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[177],{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[181,182,183,150,184,92,185,186,187,188,189,190,97,191,192,193,194,195,196,29],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":205,"publishedAt":43},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[203,204],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},[92,206,145,207,88,208,209,138,210,186,182,26,211,212,35,213,214,215,216,217],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","api","planning workflow","database operations","webhook","processing","http api","scheduled"]