[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFDG771EEB9DH519PGJ1SN":3,"skill-same:01M6AFDG771EEB9DH519PGJ1SN":91},{"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":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6AFDG771EEB9DH519PGJ1SN","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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\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",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"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","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-database",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add davila7\u002Fclaude-code-templates --skill ensembl-database",{"tool":71,"command":72},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill ensembl-database",{"tool":74,"command":75},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill ensembl-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-database\u002FSKILL.md",true,[79,85],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fapi_endpoints.md","REFERENCE","参考文档",10046,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-database\u002Freferences\u002Fapi_endpoints.md",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fensembl_query.py","SCRIPT","脚本",13595,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fensembl-database\u002Fscripts\u002Fensembl_query.py",[92,119,146,173,208,232],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":99,"publishedAt":50},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[98],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[100,101,102,103,104,39,105,106,107,108,109,110,111,112,113,114,115,116,117,118],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":120,"name":121,"nameZh":6,"description":122,"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":126,"publishedAt":50},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[125],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[127,128,129,130,131,132,133,134,135,136,137,108,138,139,140,141,142,143,144,145],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":157,"publishedAt":50},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[152,153,154],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":155,"categoryName":156,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师",[158,159,160,161,162,163,164,39,165,166,167,168,169,170,171,107,172,32,108,45],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":177,"licenseFileUrl":178,"sourceHtmlUrl":179,"categories":180,"keywords":184,"publishedAt":50},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[181],{"categoryNo":182,"categoryName":183,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,39,207],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":177,"licenseFileUrl":212,"sourceHtmlUrl":213,"categories":214,"keywords":219,"publishedAt":50},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[215,218],{"categoryNo":216,"categoryName":217,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":182,"categoryName":183,"taxonomyType":21,"taxonomyTypeDesc":22},[220,221,222,223,224,186,225,195,196,226,227,228,229,39,230,110,231],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":177,"licenseFileUrl":236,"sourceHtmlUrl":237,"categories":238,"keywords":246,"publishedAt":50},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[239,242,243],{"categoryNo":240,"categoryName":241,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":244,"categoryName":245,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",[247,248,249,250,251,252,253,254,188,255,198,138,256,257,258,259,260,170,261,108,262,48,263,264,265,116,266],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]