[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFE5K5YGF3ASF6EVFSQCME":3,"skill-same:01M6AFE5K5YGF3ASF6EVFSQCME":101},{"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":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":58,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6AFE5K5YGF3ASF6EVFSQCME","gene-database",null,"Query NCBI Gene via E-utilities\u002FDatasets API. Search by symbol\u002FID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33286,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"database","gene database","gene","NCBI","go","functional analysis","for","Utilities","functional","via","annotation","id","info","query","datasets","api","analysis","batch","search","2026-11-10 10:12:30",false,"---\nname: gene-database\ndescription: \"Query NCBI Gene via E-utilities\u002FDatasets API. Search by symbol\u002FID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.\"\n---\n\n# Gene Database\n\n## Overview\n\nNCBI Gene is a comprehensive database integrating gene information from diverse species. It provides nomenclature, reference sequences (RefSeqs), chromosomal maps, biological pathways, genetic variations, phenotypes, and cross-references to global genomic resources.\n\n## When to Use This Skill\n\nThis skill should be used when working with gene data including searching by gene symbol or ID, retrieving gene sequences and metadata, analyzing gene functions and pathways, or performing batch gene lookups.\n\n## Quick Start\n\nNCBI provides two main APIs for gene data access:\n\n1. **E-utilities** (Traditional): Full-featured API for all Entrez databases with flexible querying\n2. **NCBI Datasets API** (Newer): Optimized for gene data retrieval with simplified workflows\n\nChoose E-utilities for complex queries and cross-database searches. Choose Datasets API for straightforward gene data retrieval with metadata and sequences in a single request.\n\n## Common Workflows\n\n### Search Genes by Symbol or Name\n\nTo search for genes by symbol or name across organisms:\n\n1. Use the `scripts\u002Fquery_gene.py` script with E-utilities ESearch\n2. Specify the gene symbol and organism (e.g., \"BRCA1 in human\")\n3. The script returns matching Gene IDs\n\nExample query patterns:\n- Gene symbol: `insulin[gene name] AND human[organism]`\n- Gene with disease: `dystrophin[gene name] AND muscular dystrophy[disease]`\n- Chromosome location: `human[organism] AND 17q21[chromosome]`\n\n### Retrieve Gene Information by ID\n\nTo fetch detailed information for known Gene IDs:\n\n1. Use `scripts\u002Ffetch_gene_data.py` with the Datasets API for comprehensive data\n2. Alternatively, use `scripts\u002Fquery_gene.py` with E-utilities EFetch for specific formats\n3. Specify desired output format (JSON, XML, or text)\n\nThe Datasets API returns:\n- Gene nomenclature and aliases\n- Reference sequences (RefSeqs) for transcripts and proteins\n- Chromosomal location and mapping\n- Gene Ontology (GO) annotations\n- Associated publications\n\n### Batch Gene Lookups\n\nFor multiple genes simultaneously:\n\n1. Use `scripts\u002Fbatch_gene_lookup.py` for efficient batch processing\n2. Provide a list of gene symbols or IDs\n3. Specify the organism for symbol-based queries\n4. The script handles rate limiting automatically (10 requests\u002Fsecond with API key)\n\nThis workflow is useful for:\n- Validating gene lists\n- Retrieving metadata for gene panels\n- Cross-referencing gene identifiers\n- Building gene annotation tables\n\n### Search by Biological Context\n\nTo find genes associated with specific biological functions or phenotypes:\n\n1. Use E-utilities with Gene Ontology (GO) terms or phenotype keywords\n2. Query by pathway names or disease associations\n3. Filter by organism, chromosome, or other attributes\n\nExample searches:\n- By GO term: `GO:0006915[biological process]` (apoptosis)\n- By phenotype: `diabetes[phenotype] AND mouse[organism]`\n- By pathway: `insulin signaling pathway[pathway]`\n\n### API Access Patterns\n\n**Rate Limits:**\n- Without API key: 3 requests\u002Fsecond for E-utilities, 5 requests\u002Fsecond for Datasets API\n- With API key: 10 requests\u002Fsecond for both APIs\n\n**Authentication:**\nRegister for a free NCBI API key at https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Faccount\u002F to increase rate limits.\n\n**Error Handling:**\nBoth APIs return standard HTTP status codes. Common errors include:\n- 400: Malformed query or invalid parameters\n- 429: Rate limit exceeded\n- 404: Gene ID not found\n\nRetry failed requests with exponential backoff.\n\n## Script Usage\n\n### query_gene.py\n\nQuery NCBI Gene using E-utilities (ESearch, ESummary, EFetch).\n\n```bash\npython scripts\u002Fquery_gene.py --search \"BRCA1\" --organism \"human\"\npython scripts\u002Fquery_gene.py --id 672 --format json\npython scripts\u002Fquery_gene.py --search \"insulin[gene] AND diabetes[disease]\"\n```\n\n### fetch_gene_data.py\n\nFetch comprehensive gene data using NCBI Datasets API.\n\n```bash\npython scripts\u002Ffetch_gene_data.py --gene-id 672\npython scripts\u002Ffetch_gene_data.py --symbol BRCA1 --taxon human\npython scripts\u002Ffetch_gene_data.py --symbol TP53 --taxon \"Homo sapiens\" --output json\n```\n\n### batch_gene_lookup.py\n\nProcess multiple gene queries efficiently.\n\n```bash\npython scripts\u002Fbatch_gene_lookup.py --file gene_list.txt --organism human\npython scripts\u002Fbatch_gene_lookup.py --ids 672,7157,5594 --output results.json\n```\n\n## API References\n\nFor detailed API documentation including endpoints, parameters, response formats, and examples, refer to:\n\n- `references\u002Fapi_reference.md` - Comprehensive API documentation for E-utilities and Datasets API\n- `references\u002Fcommon_workflows.md` - Additional examples and use case patterns\n\nSearch these references when needing specific API endpoint details, parameter options, or response structure information.\n\n## Data Formats\n\nNCBI Gene data can be retrieved in multiple formats:\n\n- **JSON**: Structured data ideal for programmatic processing\n- **XML**: Detailed hierarchical format with full metadata\n- **GenBank**: Sequence data with annotations\n- **FASTA**: Sequence data only\n- **Text**: Human-readable summaries\n\nChoose JSON for modern applications, XML for legacy systems requiring detailed metadata, and FASTA for sequence analysis workflows.\n\n## Best Practices\n\n1. **Always specify organism** when searching by gene symbol to avoid ambiguity\n2. **Use Gene IDs** for precise lookups when available\n3. **Batch requests** when working with multiple genes to minimize API calls\n4. **Cache results** locally to reduce redundant queries\n5. **Include API key** in scripts for higher rate limits\n6. **Handle errors gracefully** with retry logic for transient failures\n7. **Validate gene symbols** before batch processing to catch typos\n\n## Resources\n\nThis skill includes:\n\n### scripts\u002F\n- `query_gene.py` - Query genes using E-utilities (ESearch, ESummary, EFetch)\n- `fetch_gene_data.py` - Fetch gene data using NCBI Datasets API\n- `batch_gene_lookup.py` - Handle multiple gene queries efficiently\n\n### references\u002F\n- `api_reference.md` - Detailed API documentation for both E-utilities and Datasets API\n- `common_workflows.md` - Examples of common gene queries and use cases\n","# Gene Database\n\n## Overview\n\nNCBI Gene is a comprehensive database integrating gene information from diverse species. It provides nomenclature, reference sequences (RefSeqs), chromosomal maps, biological pathways, genetic variations, phenotypes, and cross-references to global genomic resources.\n\n## When to Use This Skill\n\nThis skill should be used when working with gene data including searching by gene symbol or ID, retrieving gene sequences and metadata, analyzing gene functions and pathways, or performing batch gene lookups.\n\n## Quick Start\n\nNCBI provides two main APIs for gene data access:\n\n1. **E-utilities** (Traditional): Full-featured API for all Entrez databases with flexible querying\n2. **NCBI Datasets API** (Newer): Optimized for gene data retrieval with simplified workflows\n\nChoose E-utilities for complex queries and cross-database searches. Choose Datasets API for straightforward gene data retrieval with metadata and sequences in a single request.\n\n## Common Workflows\n\n### Search Genes by Symbol or Name\n\nTo search for genes by symbol or name across organisms:\n\n1. Use the `scripts\u002Fquery_gene.py` script with E-utilities ESearch\n2. Specify the gene symbol and organism (e.g., \"BRCA1 in human\")\n3. The script returns matching Gene IDs\n\nExample query patterns:\n- Gene symbol: `insulin[gene name] AND human[organism]`\n- Gene with disease: `dystrophin[gene name] AND muscular dystrophy[disease]`\n- Chromosome location: `human[organism] AND 17q21[chromosome]`\n\n### Retrieve Gene Information by ID\n\nTo fetch detailed information for known Gene IDs:\n\n1. Use `scripts\u002Ffetch_gene_data.py` with the Datasets API for comprehensive data\n2. Alternatively, use `scripts\u002Fquery_gene.py` with E-utilities EFetch for specific formats\n3. Specify desired output format (JSON, XML, or text)\n\nThe Datasets API returns:\n- Gene nomenclature and aliases\n- Reference sequences (RefSeqs) for transcripts and proteins\n- Chromosomal location and mapping\n- Gene Ontology (GO) annotations\n- Associated publications\n\n### Batch Gene Lookups\n\nFor multiple genes simultaneously:\n\n1. Use `scripts\u002Fbatch_gene_lookup.py` for efficient batch processing\n2. Provide a list of gene symbols or IDs\n3. Specify the organism for symbol-based queries\n4. The script handles rate limiting automatically (10 requests\u002Fsecond with API key)\n\nThis workflow is useful for:\n- Validating gene lists\n- Retrieving metadata for gene panels\n- Cross-referencing gene identifiers\n- Building gene annotation tables\n\n### Search by Biological Context\n\nTo find genes associated with specific biological functions or phenotypes:\n\n1. Use E-utilities with Gene Ontology (GO) terms or phenotype keywords\n2. Query by pathway names or disease associations\n3. Filter by organism, chromosome, or other attributes\n\nExample searches:\n- By GO term: `GO:0006915[biological process]` (apoptosis)\n- By phenotype: `diabetes[phenotype] AND mouse[organism]`\n- By pathway: `insulin signaling pathway[pathway]`\n\n### API Access Patterns\n\n**Rate Limits:**\n- Without API key: 3 requests\u002Fsecond for E-utilities, 5 requests\u002Fsecond for Datasets API\n- With API key: 10 requests\u002Fsecond for both APIs\n\n**Authentication:**\nRegister for a free NCBI API key at https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Faccount\u002F to increase rate limits.\n\n**Error Handling:**\nBoth APIs return standard HTTP status codes. Common errors include:\n- 400: Malformed query or invalid parameters\n- 429: Rate limit exceeded\n- 404: Gene ID not found\n\nRetry failed requests with exponential backoff.\n\n## Script Usage\n\n### query_gene.py\n\nQuery NCBI Gene using E-utilities (ESearch, ESummary, EFetch).\n\n```bash\npython scripts\u002Fquery_gene.py --search \"BRCA1\" --organism \"human\"\npython scripts\u002Fquery_gene.py --id 672 --format json\npython scripts\u002Fquery_gene.py --search \"insulin[gene] AND diabetes[disease]\"\n```\n\n### fetch_gene_data.py\n\nFetch comprehensive gene data using NCBI Datasets API.\n\n```bash\npython scripts\u002Ffetch_gene_data.py --gene-id 672\npython scripts\u002Ffetch_gene_data.py --symbol BRCA1 --taxon human\npython scripts\u002Ffetch_gene_data.py --symbol TP53 --taxon \"Homo sapiens\" --output json\n```\n\n### batch_gene_lookup.py\n\nProcess multiple gene queries efficiently.\n\n```bash\npython scripts\u002Fbatch_gene_lookup.py --file gene_list.txt --organism human\npython scripts\u002Fbatch_gene_lookup.py --ids 672,7157,5594 --output results.json\n```\n\n## API References\n\nFor detailed API documentation including endpoints, parameters, response formats, and examples, refer to:\n\n- `references\u002Fapi_reference.md` - Comprehensive API documentation for E-utilities and Datasets API\n- `references\u002Fcommon_workflows.md` - Additional examples and use case patterns\n\nSearch these references when needing specific API endpoint details, parameter options, or response structure information.\n\n## Data Formats\n\nNCBI Gene data can be retrieved in multiple formats:\n\n- **JSON**: Structured data ideal for programmatic processing\n- **XML**: Detailed hierarchical format with full metadata\n- **GenBank**: Sequence data with annotations\n- **FASTA**: Sequence data only\n- **Text**: Human-readable summaries\n\nChoose JSON for modern applications, XML for legacy systems requiring detailed metadata, and FASTA for sequence analysis workflows.\n\n## Best Practices\n\n1. **Always specify organism** when searching by gene symbol to avoid ambiguity\n2. **Use Gene IDs** for precise lookups when available\n3. **Batch requests** when working with multiple genes to minimize API calls\n4. **Cache results** locally to reduce redundant queries\n5. **Include API key** in scripts for higher rate limits\n6. **Handle errors gracefully** with retry logic for transient failures\n7. **Validate gene symbols** before batch processing to catch typos\n\n## Resources\n\nThis skill includes:\n\n### scripts\u002F\n- `query_gene.py` - Query genes using E-utilities (ESearch, ESummary, EFetch)\n- `fetch_gene_data.py` - Fetch gene data using NCBI Datasets API\n- `batch_gene_lookup.py` - Handle multiple gene queries efficiently\n\n### references\u002F\n- `api_reference.md` - Detailed API documentation for both E-utilities and Datasets API\n- `common_workflows.md` - Examples of common gene queries and use cases\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database",3909,"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","2026-11-11 11:53:30",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add davila7\u002Fclaude-code-templates --skill gene-database",{"tool":69,"command":70},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill gene-database",{"tool":72,"command":73},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill gene-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002FSKILL.md",true,[77,83,87,93,97],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fapi_reference.md","REFERENCE","参考文档",9121,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002Freferences\u002Fapi_reference.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fcommon_workflows.md",10395,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002Freferences\u002Fcommon_workflows.md",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Fbatch_gene_lookup.py","SCRIPT","脚本",9109,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002Fscripts\u002Fbatch_gene_lookup.py",{"path":94,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":95,"sourceHtmlUrl":96},"scripts\u002Ffetch_gene_data.py",8602,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002Fscripts\u002Ffetch_gene_data.py",{"path":98,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fquery_gene.py",7409,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgene-database\u002Fscripts\u002Fquery_gene.py",[102,131,158,185,220,244],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":111,"publishedAt":48},"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",[108],{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[112,113,114,115,116,35,117,118,119,120,121,122,123,124,125,126,127,128,129,130],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":138,"publishedAt":48},"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",[137],{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},[139,140,141,142,143,144,145,146,147,148,149,120,150,151,152,153,154,155,156,157],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":169,"publishedAt":48},"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",[164,165,166],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":167,"categoryName":168,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[170,171,172,173,174,175,176,35,177,178,179,180,181,182,183,119,184,29,120,42],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":190,"sourceHtmlUrl":191,"categories":192,"keywords":196,"publishedAt":48},"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",[193],{"categoryNo":194,"categoryName":195,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,35,219],"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":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":224,"sourceHtmlUrl":225,"categories":226,"keywords":231,"publishedAt":48},"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",[227,230],{"categoryNo":228,"categoryName":229,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":194,"categoryName":195,"taxonomyType":21,"taxonomyTypeDesc":22},[232,233,234,235,236,198,237,207,208,238,239,240,241,35,242,122,243],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":245,"name":246,"nameZh":6,"description":247,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":248,"sourceHtmlUrl":249,"categories":250,"keywords":258,"publishedAt":48},"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. 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