[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFQK90M51N4BNR08BQNAVE":3,"skill-same:01M6AFQK90M51N4BNR08BQNAVE":99},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6AFQK90M51N4BNR08BQNAVE","uniprot-database",null,"Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot\u002FTrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP\u002FREST work or UniProt-specific control.","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\u002Funiprot-database\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"database","uniprot database","work","Protein","FASTA","swiss","for","interface","with","mapping","id","databases","use","python","direct","access","api","bioservices","REST","services","http","workflows","2026-11-10 10:12:30",false,"---\nname: uniprot-database\ndescription: \"Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot\u002FTrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP\u002FREST work or UniProt-specific control.\"\n---\n\n# UniProt Database\n\n## Overview\n\nUniProt is the world's leading comprehensive protein sequence and functional information resource. Search proteins by name, gene, or accession, retrieve sequences in FASTA format, perform ID mapping across databases, access Swiss-Prot\u002FTrEMBL annotations via REST API for protein analysis.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Searching for protein entries by name, gene symbol, accession, or organism\n- Retrieving protein sequences in FASTA or other formats\n- Mapping identifiers between UniProt and external databases (Ensembl, RefSeq, PDB, etc.)\n- Accessing protein annotations including GO terms, domains, and functional descriptions\n- Batch retrieving multiple protein entries efficiently\n- Querying reviewed (Swiss-Prot) vs. unreviewed (TrEMBL) protein data\n- Streaming large protein datasets\n- Building custom queries with field-specific search syntax\n\n## Core Capabilities\n\n### 1. Searching for Proteins\n\nSearch UniProt using natural language queries or structured search syntax.\n\n**Common search patterns:**\n```python\n# Search by protein name\nquery = \"insulin AND organism_name:\\\"Homo sapiens\\\"\"\n\n# Search by gene name\nquery = \"gene:BRCA1 AND reviewed:true\"\n\n# Search by accession\nquery = \"accession:P12345\"\n\n# Search by sequence length\nquery = \"length:[100 TO 500]\"\n\n# Search by taxonomy\nquery = \"taxonomy_id:9606\"  # Human proteins\n\n# Search by GO term\nquery = \"go:0005515\"  # Protein binding\n```\n\nUse the API search endpoint: `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002Fsearch?query={query}&format={format}`\n\n**Supported formats:** JSON, TSV, Excel, XML, FASTA, RDF, TXT\n\n### 2. Retrieving Individual Protein Entries\n\nRetrieve specific protein entries by accession number.\n\n**Accession number formats:**\n- Classic: P12345, Q1AAA9, O15530 (6 characters: letter + 5 alphanumeric)\n- Extended: A0A022YWF9 (10 characters for newer entries)\n\n**Retrieve endpoint:** `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002F{accession}.{format}`\n\nExample: `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002FP12345.fasta`\n\n### 3. Batch Retrieval and ID Mapping\n\nMap protein identifiers between different database systems and retrieve multiple entries efficiently.\n\n**ID Mapping workflow:**\n1. Submit mapping job to: `https:\u002F\u002Frest.uniprot.org\u002Fidmapping\u002Frun`\n2. Check job status: `https:\u002F\u002Frest.uniprot.org\u002Fidmapping\u002Fstatus\u002F{jobId}`\n3. Retrieve results: `https:\u002F\u002Frest.uniprot.org\u002Fidmapping\u002Fresults\u002F{jobId}`\n\n**Supported databases for mapping:**\n- UniProtKB AC\u002FID\n- Gene names\n- Ensembl, RefSeq, EMBL\n- PDB, AlphaFoldDB\n- KEGG, GO terms\n- And many more (see `\u002Freferences\u002Fid_mapping_databases.md`)\n\n**Limitations:**\n- Maximum 100,000 IDs per job\n- Results stored for 7 days\n\n### 4. Streaming Large Result Sets\n\nFor large queries that exceed pagination limits, use the stream endpoint:\n\n`https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002Fstream?query={query}&format={format}`\n\nThe stream endpoint returns all results without pagination, suitable for downloading complete datasets.\n\n### 5. Customizing Retrieved Fields\n\nSpecify exactly which fields to retrieve for efficient data transfer.\n\n**Common fields:**\n- `accession` - UniProt accession number\n- `id` - Entry name\n- `gene_names` - Gene name(s)\n- `organism_name` - Organism\n- `protein_name` - Protein names\n- `sequence` - Amino acid sequence\n- `length` - Sequence length\n- `go_*` - Gene Ontology annotations\n- `cc_*` - Comment fields (function, interaction, etc.)\n- `ft_*` - Feature annotations (domains, sites, etc.)\n\n**Example:** `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002Fsearch?query=insulin&fields=accession,gene_names,organism_name,length,sequence&format=tsv`\n\nSee `\u002Freferences\u002Fapi_fields.md` for complete field list.\n\n## Python Implementation\n\nFor programmatic access, use the provided helper script `scripts\u002Funiprot_client.py` which implements:\n\n- `search_proteins(query, format)` - Search UniProt with any query\n- `get_protein(accession, format)` - Retrieve single protein entry\n- `map_ids(ids, from_db, to_db)` - Map between identifier types\n- `batch_retrieve(accessions, format)` - Retrieve multiple entries\n- `stream_results(query, format)` - Stream large result sets\n\n**Alternative Python packages:**\n- **Unipressed**: Modern, typed Python client for UniProt REST API\n- **bioservices**: Comprehensive bioinformatics web services client\n\n## Query Syntax Examples\n\n**Boolean operators:**\n```\nkinase AND organism_name:human\n(diabetes OR insulin) AND reviewed:true\ncancer NOT lung\n```\n\n**Field-specific searches:**\n```\ngene:BRCA1\naccession:P12345\norganism_id:9606\ntaxonomy_name:\"Homo sapiens\"\nannotation:(type:signal)\n```\n\n**Range queries:**\n```\nlength:[100 TO 500]\nmass:[50000 TO 100000]\n```\n\n**Wildcards:**\n```\ngene:BRCA*\nprotein_name:kinase*\n```\n\nSee `\u002Freferences\u002Fquery_syntax.md` for comprehensive syntax documentation.\n\n## Best Practices\n\n1. **Use reviewed entries when possible**: Filter with `reviewed:true` for Swiss-Prot (manually curated) entries\n2. **Specify format explicitly**: Choose the most appropriate format (FASTA for sequences, TSV for tabular data, JSON for programmatic parsing)\n3. **Use field selection**: Only request fields you need to reduce bandwidth and processing time\n4. **Handle pagination**: For large result sets, implement proper pagination or use the stream endpoint\n5. **Cache results**: Store frequently accessed data locally to minimize API calls\n6. **Rate limiting**: Be respectful of API resources; implement delays for large batch operations\n7. **Check data quality**: TrEMBL entries are computational predictions; Swiss-Prot entries are manually reviewed\n\n## Resources\n\n### scripts\u002F\n`uniprot_client.py` - Python client with helper functions for common UniProt operations including search, retrieval, ID mapping, and streaming.\n\n### references\u002F\n- `api_fields.md` - Complete list of available fields for customizing queries\n- `id_mapping_databases.md` - Supported databases for ID mapping operations\n- `query_syntax.md` - Comprehensive query syntax with advanced examples\n- `api_examples.md` - Code examples in multiple languages (Python, curl, R)\n\n## Additional Resources\n\n- **API Documentation**: https:\u002F\u002Fwww.uniprot.org\u002Fhelp\u002Fapi\n- **Interactive API Explorer**: https:\u002F\u002Fwww.uniprot.org\u002Fapi-documentation\n- **REST Tutorial**: https:\u002F\u002Fwww.uniprot.org\u002Fhelp\u002Funiprot_rest_tutorial\n- **Query Syntax Help**: https:\u002F\u002Fwww.uniprot.org\u002Fhelp\u002Fquery-fields\n- **SPARQL Endpoint**: https:\u002F\u002Fsparql.uniprot.org\u002F (for advanced graph queries)\n","# UniProt Database\n\n## Overview\n\nUniProt is the world's leading comprehensive protein sequence and functional information resource. Search proteins by name, gene, or accession, retrieve sequences in FASTA format, perform ID mapping across databases, access Swiss-Prot\u002FTrEMBL annotations via REST API for protein analysis.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Searching for protein entries by name, gene symbol, accession, or organism\n- Retrieving protein sequences in FASTA or other formats\n- Mapping identifiers between UniProt and external databases (Ensembl, RefSeq, PDB, etc.)\n- Accessing protein annotations including GO terms, domains, and functional descriptions\n- Batch retrieving multiple protein entries efficiently\n- Querying reviewed (Swiss-Prot) vs. unreviewed (TrEMBL) protein data\n- Streaming large protein datasets\n- Building custom queries with field-specific search syntax\n\n## Core Capabilities\n\n### 1. Searching for Proteins\n\nSearch UniProt using natural language queries or structured search syntax.\n\n**Common search patterns:**\n```python\n# Search by protein name\nquery = \"insulin AND organism_name:\\\"Homo sapiens\\\"\"\n\n# Search by gene name\nquery = \"gene:BRCA1 AND reviewed:true\"\n\n# Search by accession\nquery = \"accession:P12345\"\n\n# Search by sequence length\nquery = \"length:[100 TO 500]\"\n\n# Search by taxonomy\nquery = \"taxonomy_id:9606\"  # Human proteins\n\n# Search by GO term\nquery = \"go:0005515\"  # Protein binding\n```\n\nUse the API search endpoint: `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002Fsearch?query={query}&format={format}`\n\n**Supported formats:** JSON, TSV, Excel, XML, FASTA, RDF, TXT\n\n### 2. Retrieving Individual Protein Entries\n\nRetrieve specific protein entries by accession number.\n\n**Accession number formats:**\n- Classic: P12345, Q1AAA9, O15530 (6 characters: letter + 5 alphanumeric)\n- Extended: A0A022YWF9 (10 characters for newer entries)\n\n**Retrieve endpoint:** `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002F{accession}.{format}`\n\nExample: `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002FP12345.fasta`\n\n### 3. Batch Retrieval and ID Mapping\n\nMap protein identifiers between different database systems and retrieve multiple entries efficiently.\n\n**ID Mapping workflow:**\n1. Submit mapping job to: `https:\u002F\u002Frest.uniprot.org\u002Fidmapping\u002Frun`\n2. Check job status: `https:\u002F\u002Frest.uniprot.org\u002Fidmapping\u002Fstatus\u002F{jobId}`\n3. Retrieve results: `https:\u002F\u002Frest.uniprot.org\u002Fidmapping\u002Fresults\u002F{jobId}`\n\n**Supported databases for mapping:**\n- UniProtKB AC\u002FID\n- Gene names\n- Ensembl, RefSeq, EMBL\n- PDB, AlphaFoldDB\n- KEGG, GO terms\n- And many more (see `\u002Freferences\u002Fid_mapping_databases.md`)\n\n**Limitations:**\n- Maximum 100,000 IDs per job\n- Results stored for 7 days\n\n### 4. Streaming Large Result Sets\n\nFor large queries that exceed pagination limits, use the stream endpoint:\n\n`https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002Fstream?query={query}&format={format}`\n\nThe stream endpoint returns all results without pagination, suitable for downloading complete datasets.\n\n### 5. Customizing Retrieved Fields\n\nSpecify exactly which fields to retrieve for efficient data transfer.\n\n**Common fields:**\n- `accession` - UniProt accession number\n- `id` - Entry name\n- `gene_names` - Gene name(s)\n- `organism_name` - Organism\n- `protein_name` - Protein names\n- `sequence` - Amino acid sequence\n- `length` - Sequence length\n- `go_*` - Gene Ontology annotations\n- `cc_*` - Comment fields (function, interaction, etc.)\n- `ft_*` - Feature annotations (domains, sites, etc.)\n\n**Example:** `https:\u002F\u002Frest.uniprot.org\u002Funiprotkb\u002Fsearch?query=insulin&fields=accession,gene_names,organism_name,length,sequence&format=tsv`\n\nSee `\u002Freferences\u002Fapi_fields.md` for complete field list.\n\n## Python Implementation\n\nFor programmatic access, use the provided helper script `scripts\u002Funiprot_client.py` which implements:\n\n- `search_proteins(query, format)` - Search UniProt with any query\n- `get_protein(accession, format)` - Retrieve single protein entry\n- `map_ids(ids, from_db, to_db)` - Map between identifier types\n- `batch_retrieve(accessions, format)` - Retrieve multiple entries\n- `stream_results(query, format)` - Stream large result sets\n\n**Alternative Python packages:**\n- **Unipressed**: Modern, typed Python client for UniProt REST API\n- **bioservices**: Comprehensive bioinformatics web services client\n\n## Query Syntax Examples\n\n**Boolean operators:**\n```\nkinase AND organism_name:human\n(diabetes OR insulin) AND reviewed:true\ncancer NOT lung\n```\n\n**Field-specific searches:**\n```\ngene:BRCA1\naccession:P12345\norganism_id:9606\ntaxonomy_name:\"Homo sapiens\"\nannotation:(type:signal)\n```\n\n**Range queries:**\n```\nlength:[100 TO 500]\nmass:[50000 TO 100000]\n```\n\n**Wildcards:**\n```\ngene:BRCA*\nprotein_name:kinase*\n```\n\nSee `\u002Freferences\u002Fquery_syntax.md` for comprehensive syntax documentation.\n\n## Best Practices\n\n1. **Use reviewed entries when possible**: Filter with `reviewed:true` for Swiss-Prot (manually curated) entries\n2. **Specify format explicitly**: Choose the most appropriate format (FASTA for sequences, TSV for tabular data, JSON for programmatic parsing)\n3. **Use field selection**: Only request fields you need to reduce bandwidth and processing time\n4. **Handle pagination**: For large result sets, implement proper pagination or use the stream endpoint\n5. **Cache results**: Store frequently accessed data locally to minimize API calls\n6. **Rate limiting**: Be respectful of API resources; implement delays for large batch operations\n7. **Check data quality**: TrEMBL entries are computational predictions; Swiss-Prot entries are manually reviewed\n\n## Resources\n\n### scripts\u002F\n`uniprot_client.py` - Python client with helper functions for common UniProt operations including search, retrieval, ID mapping, and streaming.\n\n### references\u002F\n- `api_fields.md` - Complete list of available fields for customizing queries\n- `id_mapping_databases.md` - Supported databases for ID mapping operations\n- `query_syntax.md` - Comprehensive query syntax with advanced examples\n- `api_examples.md` - Code examples in multiple languages (Python, curl, R)\n\n## Additional Resources\n\n- **API Documentation**: https:\u002F\u002Fwww.uniprot.org\u002Fhelp\u002Fapi\n- **Interactive API Explorer**: https:\u002F\u002Fwww.uniprot.org\u002Fapi-documentation\n- **REST Tutorial**: https:\u002F\u002Fwww.uniprot.org\u002Fhelp\u002Funiprot_rest_tutorial\n- **Query Syntax Help**: https:\u002F\u002Fwww.uniprot.org\u002Fhelp\u002Fquery-fields\n- **SPARQL Endpoint**: https:\u002F\u002Fsparql.uniprot.org\u002F (for advanced graph queries)\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-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":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add davila7\u002Fclaude-code-templates --skill uniprot-database",{"tool":67,"command":68},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill uniprot-database",{"tool":70,"command":71},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill uniprot-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002FSKILL.md",true,[75,81,85,89,93],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fapi_examples.md","REFERENCE","参考文档",10348,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002Freferences\u002Fapi_examples.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fapi_fields.md",7729,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002Freferences\u002Fapi_fields.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fid_mapping_databases.md",6682,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002Freferences\u002Fid_mapping_databases.md",{"path":90,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fquery_syntax.md",6833,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002Freferences\u002Fquery_syntax.md",{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"scripts\u002Funiprot_client.py","SCRIPT","脚本",9982,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002Fscripts\u002Funiprot_client.py",[100,127,153,182,217,241],{"skillNo":101,"name":102,"nameZh":6,"description":103,"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":109,"publishedAt":46},"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",[106],{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[110,111,112,113,114,30,115,116,36,117,118,32,119,120,121,122,123,124,125,126],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","when","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":134,"publishedAt":46},"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",[133],{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},[135,136,137,138,139,140,141,142,143,144,145,117,146,147,26,148,149,150,151,152],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","plan","focus","signals","audit","dependency",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":166,"publishedAt":46},"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",[159,160,161],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":162,"categoryName":163,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[167,168,169,170,171,172,173,30,174,175,176,177,178,179,180,36,39,24,117,181],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","query",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":186,"licenseFileUrl":187,"sourceHtmlUrl":188,"categories":189,"keywords":193,"publishedAt":46},"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",[190],{"categoryNo":191,"categoryName":192,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,30,216],"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":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":186,"licenseFileUrl":221,"sourceHtmlUrl":222,"categories":223,"keywords":228,"publishedAt":46},"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",[224,227],{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":191,"categoryName":192,"taxonomyType":21,"taxonomyTypeDesc":22},[229,230,231,232,233,195,234,204,205,235,236,237,238,30,239,32,240],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":242,"name":243,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":186,"licenseFileUrl":245,"sourceHtmlUrl":246,"categories":247,"keywords":255,"publishedAt":46},"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",[248,251,252],{"categoryNo":249,"categoryName":250,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":107,"categoryName":108,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":253,"categoryName":254,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000036","内容创作者",[256,257,258,259,260,261,262,263,197,264,207,146,265,266,267,268,269,179,270,117,271,272,273,274,275,124,276],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]