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Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.","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\u002Fensembl-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],"database","ensembl database","Ensembl","sequence","research","variant","gene","for","retrieval","predictions","vep","rest api","genomics","query","variant analysis","api","analysis","REST","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","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-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":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill ensembl-database",{"tool":61,"command":62},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill ensembl-database",{"tool":64,"command":65},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill ensembl-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\u002Fensembl-database\u002FSKILL.md",true,[69,75],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fapi_endpoints.md","REFERENCE","参考文档",10046,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-database\u002Freferences\u002Fapi_endpoints.md",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"scripts\u002Fensembl_query.py","SCRIPT","脚本",13595,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-database\u002Fscripts\u002Fensembl_query.py",[82,111,132,156,176,202],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":91,"publishedAt":42},"01M6JMRJR3BE0HGWFWDZSB853S","zapier-make-patterns","No-code automation democratizes workflow building. 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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",[138],{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},[97,98,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,31,110],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"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":162,"publishedAt":42},"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",[],[163,164,165,166,167,143,168,144,145,169,102,170,171,172,173,174,175,155,31,110],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":185,"publishedAt":42},"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",[182],{"categoryNo":183,"categoryName":184,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[186,187,188,155,189,97,190,191,192,193,194,195,102,196,197,198,199,200,201,31],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":210,"publishedAt":42},"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",[208,209],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},[97,211,150,212,93,213,214,143,215,191,187,24,39,216,217,218,219,220,221,222],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]