[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ER2D80EA2MRPJNNZP606FW":3,"skill-same:01M6ER2D80EA2MRPJNNZP606FW":90},{"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":31,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6ER2D80EA2MRPJNNZP606FW","ensembl-database",null,"Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fensembl-database\u002FSKILL.md",[18,23,26],{"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",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45],"database","ensembl database","Ensembl","research","REST","analysis","api","variant analysis","query","genomics","rest api","vep","predictions","retrieval","2026-11-10 10:12:30",false,"---\nname: ensembl-database\ndescription: \"Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.\"\n---\n\n# Ensembl Database\n\n## Overview\n\nAccess and query the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. The database provides gene annotations, sequences, variants, regulatory information, and comparative genomics data for over 250 species. Current release is 115 (September 2025).\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- Querying gene information by symbol or Ensembl ID\n- Retrieving DNA, transcript, or protein sequences\n- Analyzing genetic variants using the Variant Effect Predictor (VEP)\n- Finding orthologs and paralogs across species\n- Accessing regulatory features and genomic annotations\n- Converting coordinates between genome assemblies (e.g., GRCh37 to GRCh38)\n- Performing comparative genomics analyses\n- Integrating Ensembl data into genomic research pipelines\n\n## Core Capabilities\n\n### 1. Gene Information Retrieval\n\nQuery gene data by symbol, Ensembl ID, or external database identifiers.\n\n**Common operations:**\n- Look up gene information by symbol (e.g., \"BRCA2\", \"TP53\")\n- Retrieve transcript and protein information\n- Get gene coordinates and chromosomal locations\n- Access cross-references to external databases (UniProt, RefSeq, etc.)\n\n**Using the ensembl_rest package:**\n```python\nfrom ensembl_rest import EnsemblClient\n\nclient = EnsemblClient()\n\n# Look up gene by symbol\ngene_data = client.symbol_lookup(\n    species='human',\n    symbol='BRCA2'\n)\n\n# Get detailed gene information\ngene_info = client.lookup_id(\n    id='ENSG00000139618',  # BRCA2 Ensembl ID\n    expand=True\n)\n```\n\n**Direct REST API (no package):**\n```python\nimport requests\n\nserver = \"https:\u002F\u002Frest.ensembl.org\"\n\n# Symbol lookup\nresponse = requests.get(\n    f\"{server}\u002Flookup\u002Fsymbol\u002Fhomo_sapiens\u002FBRCA2\",\n    headers={\"Content-Type\": \"application\u002Fjson\"}\n)\ngene_data = response.json()\n```\n\n### 2. Sequence Retrieval\n\nFetch genomic, transcript, or protein sequences in various formats (JSON, FASTA, plain text).\n\n**Operations:**\n- Get DNA sequences for genes or genomic regions\n- Retrieve transcript sequences (cDNA)\n- Access protein sequences\n- Extract sequences with flanking regions or modifications\n\n**Example:**\n```python\n# Using ensembl_rest package\nsequence = client.sequence_id(\n    id='ENSG00000139618',  # Gene ID\n    content_type='application\u002Fjson'\n)\n\n# Get sequence for a genomic region\nregion_seq = client.sequence_region(\n    species='human',\n    region='7:140424943-140624564'  # chromosome:start-end\n)\n```\n\n### 3. Variant Analysis\n\nQuery genetic variation data and predict variant consequences using the Variant Effect Predictor (VEP).\n\n**Capabilities:**\n- Look up variants by rsID or genomic coordinates\n- Predict functional consequences of variants\n- Access population frequency data\n- Retrieve phenotype associations\n\n**VEP example:**\n```python\n# Predict variant consequences\nvep_result = client.vep_hgvs(\n    species='human',\n    hgvs_notation='ENST00000380152.7:c.803C>T'\n)\n\n# Query variant by rsID\nvariant = client.variation_id(\n    species='human',\n    id='rs699'\n)\n```\n\n### 4. Comparative Genomics\n\nPerform cross-species comparisons to identify orthologs, paralogs, and evolutionary relationships.\n\n**Operations:**\n- Find orthologs (same gene in different species)\n- Identify paralogs (related genes in same species)\n- Access gene trees showing evolutionary relationships\n- Retrieve gene family information\n\n**Example:**\n```python\n# Find orthologs for a human gene\northologs = client.homology_ensemblgene(\n    id='ENSG00000139618',  # Human BRCA2\n    target_species='mouse'\n)\n\n# Get gene tree\ngene_tree = client.genetree_member_symbol(\n    species='human',\n    symbol='BRCA2'\n)\n```\n\n### 5. Genomic Region Analysis\n\nFind all genomic features (genes, transcripts, regulatory elements) in a specific region.\n\n**Use cases:**\n- Identify all genes in a chromosomal region\n- Find regulatory features (promoters, enhancers)\n- Locate variants within a region\n- Retrieve structural features\n\n**Example:**\n```python\n# Find all features in a region\nfeatures = client.overlap_region(\n    species='human',\n    region='7:140424943-140624564',\n    feature='gene'\n)\n```\n\n### 6. Assembly Mapping\n\nConvert coordinates between different genome assemblies (e.g., GRCh37 to GRCh38).\n\n**Important:** Use `https:\u002F\u002Fgrch37.rest.ensembl.org` for GRCh37\u002Fhg19 queries and `https:\u002F\u002Frest.ensembl.org` for current assemblies.\n\n**Example:**\n```python\nfrom ensembl_rest import AssemblyMapper\n\n# Map coordinates from GRCh37 to GRCh38\nmapper = AssemblyMapper(\n    species='human',\n    asm_from='GRCh37',\n    asm_to='GRCh38'\n)\n\nmapped = mapper.map(chrom='7', start=140453136, end=140453136)\n```\n\n## API Best Practices\n\n### Rate Limiting\n\nThe Ensembl REST API has rate limits. Follow these practices:\n\n1. **Respect rate limits:** Maximum 15 requests per second for anonymous users\n2. **Handle 429 responses:** When rate-limited, check the `Retry-After` header and wait\n3. **Use batch endpoints:** When querying multiple items, use batch endpoints where available\n4. **Cache results:** Store frequently accessed data to reduce API calls\n\n### Error Handling\n\nAlways implement proper error handling:\n\n```python\nimport requests\nimport time\n\ndef query_ensembl(endpoint, params=None, max_retries=3):\n    server = \"https:\u002F\u002Frest.ensembl.org\"\n    headers = {\"Content-Type\": \"application\u002Fjson\"}\n\n    for attempt in range(max_retries):\n        response = requests.get(\n            f\"{server}{endpoint}\",\n            headers=headers,\n            params=params\n        )\n\n        if response.status_code == 200:\n            return response.json()\n        elif response.status_code == 429:\n            # Rate limited - wait and retry\n            retry_after = int(response.headers.get('Retry-After', 1))\n            time.sleep(retry_after)\n        else:\n            response.raise_for_status()\n\n    raise Exception(f\"Failed after {max_retries} attempts\")\n```\n\n## Installation\n\n### Python Package (Recommended)\n\n```bash\nuv pip install ensembl_rest\n```\n\nThe `ensembl_rest` package provides a Pythonic interface to all Ensembl REST API endpoints.\n\n### Direct REST API\n\nNo installation needed - use standard HTTP libraries like `requests`:\n\n```bash\nuv pip install requests\n```\n\n## Resources\n\n### references\u002F\n\n- `api_endpoints.md`: Comprehensive documentation of all 17 API endpoint categories with examples and parameters\n\n### scripts\u002F\n\n- `ensembl_query.py`: Reusable Python script for common Ensembl queries with built-in rate limiting and error handling\n\n## Common Workflows\n\n### Workflow 1: Gene Annotation Pipeline\n\n1. Look up gene by symbol to get Ensembl ID\n2. Retrieve transcript information\n3. Get protein sequences for all transcripts\n4. Find orthologs in other species\n5. Export results\n\n### Workflow 2: Variant Analysis\n\n1. Query variant by rsID or coordinates\n2. Use VEP to predict functional consequences\n3. Check population frequencies\n4. Retrieve phenotype associations\n5. Generate report\n\n### Workflow 3: Comparative Analysis\n\n1. Start with gene of interest in reference species\n2. Find orthologs in target species\n3. Retrieve sequences for all orthologs\n4. Compare gene structures and features\n5. Analyze evolutionary conservation\n\n## Species and Assembly Information\n\nTo query available species and assemblies:\n\n```python\n# List all available species\nspecies_list = client.info_species()\n\n# Get assembly information for a species\nassembly_info = client.info_assembly(species='human')\n```\n\nCommon species identifiers:\n- Human: `homo_sapiens` or `human`\n- Mouse: `mus_musculus` or `mouse`\n- Zebrafish: `danio_rerio` or `zebrafish`\n- Fruit fly: `drosophila_melanogaster`\n\n## Additional Resources\n\n- **Official Documentation:** https:\u002F\u002Frest.ensembl.org\u002Fdocumentation\n- **Python Package Docs:** https:\u002F\u002Fensemblrest.readthedocs.io\n- **EBI Training:** https:\u002F\u002Fwww.ebi.ac.uk\u002Ftraining\u002Fonline\u002Fcourses\u002Fensembl-rest-api\u002F\n- **Ensembl Browser:** https:\u002F\u002Fuseast.ensembl.org\n- **GitHub Examples:** https:\u002F\u002Fgithub.com\u002FEnsembl\u002Fensembl-rest\u002Fwiki\n","# Ensembl Database\n\n## Overview\n\nAccess and query the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. The database provides gene annotations, sequences, variants, regulatory information, and comparative genomics data for over 250 species. Current release is 115 (September 2025).\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- Querying gene information by symbol or Ensembl ID\n- Retrieving DNA, transcript, or protein sequences\n- Analyzing genetic variants using the Variant Effect Predictor (VEP)\n- Finding orthologs and paralogs across species\n- Accessing regulatory features and genomic annotations\n- Converting coordinates between genome assemblies (e.g., GRCh37 to GRCh38)\n- Performing comparative genomics analyses\n- Integrating Ensembl data into genomic research pipelines\n\n## Core Capabilities\n\n### 1. Gene Information Retrieval\n\nQuery gene data by symbol, Ensembl ID, or external database identifiers.\n\n**Common operations:**\n- Look up gene information by symbol (e.g., \"BRCA2\", \"TP53\")\n- Retrieve transcript and protein information\n- Get gene coordinates and chromosomal locations\n- Access cross-references to external databases (UniProt, RefSeq, etc.)\n\n**Using the ensembl_rest package:**\n```python\nfrom ensembl_rest import EnsemblClient\n\nclient = EnsemblClient()\n\n# Look up gene by symbol\ngene_data = client.symbol_lookup(\n    species='human',\n    symbol='BRCA2'\n)\n\n# Get detailed gene information\ngene_info = client.lookup_id(\n    id='ENSG00000139618',  # BRCA2 Ensembl ID\n    expand=True\n)\n```\n\n**Direct REST API (no package):**\n```python\nimport requests\n\nserver = \"https:\u002F\u002Frest.ensembl.org\"\n\n# Symbol lookup\nresponse = requests.get(\n    f\"{server}\u002Flookup\u002Fsymbol\u002Fhomo_sapiens\u002FBRCA2\",\n    headers={\"Content-Type\": \"application\u002Fjson\"}\n)\ngene_data = response.json()\n```\n\n### 2. Sequence Retrieval\n\nFetch genomic, transcript, or protein sequences in various formats (JSON, FASTA, plain text).\n\n**Operations:**\n- Get DNA sequences for genes or genomic regions\n- Retrieve transcript sequences (cDNA)\n- Access protein sequences\n- Extract sequences with flanking regions or modifications\n\n**Example:**\n```python\n# Using ensembl_rest package\nsequence = client.sequence_id(\n    id='ENSG00000139618',  # Gene ID\n    content_type='application\u002Fjson'\n)\n\n# Get sequence for a genomic region\nregion_seq = client.sequence_region(\n    species='human',\n    region='7:140424943-140624564'  # chromosome:start-end\n)\n```\n\n### 3. Variant Analysis\n\nQuery genetic variation data and predict variant consequences using the Variant Effect Predictor (VEP).\n\n**Capabilities:**\n- Look up variants by rsID or genomic coordinates\n- Predict functional consequences of variants\n- Access population frequency data\n- Retrieve phenotype associations\n\n**VEP example:**\n```python\n# Predict variant consequences\nvep_result = client.vep_hgvs(\n    species='human',\n    hgvs_notation='ENST00000380152.7:c.803C>T'\n)\n\n# Query variant by rsID\nvariant = client.variation_id(\n    species='human',\n    id='rs699'\n)\n```\n\n### 4. Comparative Genomics\n\nPerform cross-species comparisons to identify orthologs, paralogs, and evolutionary relationships.\n\n**Operations:**\n- Find orthologs (same gene in different species)\n- Identify paralogs (related genes in same species)\n- Access gene trees showing evolutionary relationships\n- Retrieve gene family information\n\n**Example:**\n```python\n# Find orthologs for a human gene\northologs = client.homology_ensemblgene(\n    id='ENSG00000139618',  # Human BRCA2\n    target_species='mouse'\n)\n\n# Get gene tree\ngene_tree = client.genetree_member_symbol(\n    species='human',\n    symbol='BRCA2'\n)\n```\n\n### 5. Genomic Region Analysis\n\nFind all genomic features (genes, transcripts, regulatory elements) in a specific region.\n\n**Use cases:**\n- Identify all genes in a chromosomal region\n- Find regulatory features (promoters, enhancers)\n- Locate variants within a region\n- Retrieve structural features\n\n**Example:**\n```python\n# Find all features in a region\nfeatures = client.overlap_region(\n    species='human',\n    region='7:140424943-140624564',\n    feature='gene'\n)\n```\n\n### 6. Assembly Mapping\n\nConvert coordinates between different genome assemblies (e.g., GRCh37 to GRCh38).\n\n**Important:** Use `https:\u002F\u002Fgrch37.rest.ensembl.org` for GRCh37\u002Fhg19 queries and `https:\u002F\u002Frest.ensembl.org` for current assemblies.\n\n**Example:**\n```python\nfrom ensembl_rest import AssemblyMapper\n\n# Map coordinates from GRCh37 to GRCh38\nmapper = AssemblyMapper(\n    species='human',\n    asm_from='GRCh37',\n    asm_to='GRCh38'\n)\n\nmapped = mapper.map(chrom='7', start=140453136, end=140453136)\n```\n\n## API Best Practices\n\n### Rate Limiting\n\nThe Ensembl REST API has rate limits. Follow these practices:\n\n1. **Respect rate limits:** Maximum 15 requests per second for anonymous users\n2. **Handle 429 responses:** When rate-limited, check the `Retry-After` header and wait\n3. **Use batch endpoints:** When querying multiple items, use batch endpoints where available\n4. **Cache results:** Store frequently accessed data to reduce API calls\n\n### Error Handling\n\nAlways implement proper error handling:\n\n```python\nimport requests\nimport time\n\ndef query_ensembl(endpoint, params=None, max_retries=3):\n    server = \"https:\u002F\u002Frest.ensembl.org\"\n    headers = {\"Content-Type\": \"application\u002Fjson\"}\n\n    for attempt in range(max_retries):\n        response = requests.get(\n            f\"{server}{endpoint}\",\n            headers=headers,\n            params=params\n        )\n\n        if response.status_code == 200:\n            return response.json()\n        elif response.status_code == 429:\n            # Rate limited - wait and retry\n            retry_after = int(response.headers.get('Retry-After', 1))\n            time.sleep(retry_after)\n        else:\n            response.raise_for_status()\n\n    raise Exception(f\"Failed after {max_retries} attempts\")\n```\n\n## Installation\n\n### Python Package (Recommended)\n\n```bash\nuv pip install ensembl_rest\n```\n\nThe `ensembl_rest` package provides a Pythonic interface to all Ensembl REST API endpoints.\n\n### Direct REST API\n\nNo installation needed - use standard HTTP libraries like `requests`:\n\n```bash\nuv pip install requests\n```\n\n## Resources\n\n### references\u002F\n\n- `api_endpoints.md`: Comprehensive documentation of all 17 API endpoint categories with examples and parameters\n\n### scripts\u002F\n\n- `ensembl_query.py`: Reusable Python script for common Ensembl queries with built-in rate limiting and error handling\n\n## Common Workflows\n\n### Workflow 1: Gene Annotation Pipeline\n\n1. Look up gene by symbol to get Ensembl ID\n2. Retrieve transcript information\n3. Get protein sequences for all transcripts\n4. Find orthologs in other species\n5. Export results\n\n### Workflow 2: Variant Analysis\n\n1. Query variant by rsID or coordinates\n2. Use VEP to predict functional consequences\n3. Check population frequencies\n4. Retrieve phenotype associations\n5. Generate report\n\n### Workflow 3: Comparative Analysis\n\n1. Start with gene of interest in reference species\n2. Find orthologs in target species\n3. Retrieve sequences for all orthologs\n4. Compare gene structures and features\n5. Analyze evolutionary conservation\n\n## Species and Assembly Information\n\nTo query available species and assemblies:\n\n```python\n# List all available species\nspecies_list = client.info_species()\n\n# Get assembly information for a species\nassembly_info = client.info_assembly(species='human')\n```\n\nCommon species identifiers:\n- Human: `homo_sapiens` or `human`\n- Mouse: `mus_musculus` or `mouse`\n- Zebrafish: `danio_rerio` or `zebrafish`\n- Fruit fly: `drosophila_melanogaster`\n\n## Additional Resources\n\n- **Official Documentation:** https:\u002F\u002Frest.ensembl.org\u002Fdocumentation\n- **Python Package Docs:** https:\u002F\u002Fensemblrest.readthedocs.io\n- **EBI Training:** https:\u002F\u002Fwww.ebi.ac.uk\u002Ftraining\u002Fonline\u002Fcourses\u002Fensembl-rest-api\u002F\n- **Ensembl Browser:** https:\u002F\u002Fuseast.ensembl.org\n- **GitHub Examples:** https:\u002F\u002Fgithub.com\u002FEnsembl\u002Fensembl-rest\u002Fwiki\n",{"name":5,"description":7},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fensembl-database",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill ensembl-database",{"tool":66,"command":67},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill ensembl-database",{"tool":69,"command":70},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill ensembl-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fensembl-database\u002FSKILL.md",true,[74,80,86],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fapi_endpoints.md","REFERENCE","参考文档",10046,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fensembl-database\u002Freferences\u002Fapi_endpoints.md",{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"scripts\u002Fensembl_query.py","SCRIPT","脚本",13595,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fensembl-database\u002Fscripts\u002Fensembl_query.py",{"path":87,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":88,"sourceHtmlUrl":89},"skill-report.json",10357,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fensembl-database\u002Fskill-report.json",[91,101,111,120,126,133],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":97,"publishedAt":46},"01M6FKJN13GMG4KY57J3QD8GY5","model-hierarchy-skill","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzscole\u002Fmodel-hierarchy-skill\u002Fmodel-hierarchy-skill\u002FSKILL.md",[96],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[98,99,100],"model","model hierarchy","model hierarchy skill",{"skillNo":102,"name":103,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":107,"publishedAt":46},"01M6FKJJJ6GCAMR4PPPRD12M5X","zoom-mcp","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-mcp\u002FSKILL.md",[106],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[108,109,110],"MCP","zoom","zoom mcp",{"skillNo":112,"name":113,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":46},"01M6FKJE3KYT3KPEZMEEHYKA6K","zoom-apps-sdk","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[117,118,119,109],"sdk","zoom apps sdk","apps",{"skillNo":121,"name":122,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":46},"01M6FKJ9EDH9Q5VWDF8HAJRSP0","windows","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwindows\u002FSKILL.md",[],[122],{"skillNo":127,"name":128,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":46},"01M6FKJ666BEEFC3AE9GJ72Q93","whiteboard","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwhiteboard\u002FSKILL.md",[131],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[128],{"skillNo":134,"name":135,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":46},"01M6FKJ21WFCDGTJQ2Q91T9DVW","websockets","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwebsockets\u002FSKILL.md",[],[135]]