[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFDB9Z79WHS1P1R0SH0RRT":3,"skill-same:01M6AFDB9Z79WHS1P1R0SH0RRT":137},{"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":29,"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},"01M6AFDB9Z79WHS1P1R0SH0RRT","drugbank-database",null,"Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdrugbank-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},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"database","drugbank database","drugbank","when","research","discovery research","with","information","targets","predictions","identification","similarity","pharmacology","interaction","task","analyze","access","discovery","analysis","data","2026-11-10 10:12:30",false,"---\nname: drugbank-database\ndescription: Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.\n---\n\n# DrugBank Database\n\n## Overview\n\nDrugBank is a comprehensive bioinformatics and cheminformatics database containing detailed information on drugs and drug targets. This skill enables programmatic access to DrugBank data including ~9,591 drug entries (2,037 FDA-approved small molecules, 241 biotech drugs, 96 nutraceuticals, and 6,000+ experimental compounds) with 200+ data fields per entry.\n\n## Core Capabilities\n\n### 1. Data Access and Authentication\n\nDownload and access DrugBank data using Python with proper authentication. The skill provides guidance on:\n\n- Installing and configuring the `drugbank-downloader` package\n- Managing credentials securely via environment variables or config files\n- Downloading specific or latest database versions\n- Opening and parsing XML data efficiently\n- Working with cached data to optimize performance\n\n**When to use**: Setting up DrugBank access, downloading database updates, initial project configuration.\n\n**Reference**: See `references\u002Fdata-access.md` for detailed authentication, download procedures, API access, caching strategies, and troubleshooting.\n\n### 2. Drug Information Queries\n\nExtract comprehensive drug information from the database including identifiers, chemical properties, pharmacology, clinical data, and cross-references to external databases.\n\n**Query capabilities**:\n- Search by DrugBank ID, name, CAS number, or keywords\n- Extract basic drug information (name, type, description, indication)\n- Retrieve chemical properties (SMILES, InChI, molecular formula)\n- Get pharmacology data (mechanism of action, pharmacodynamics, ADME)\n- Access external identifiers (PubChem, ChEMBL, UniProt, KEGG)\n- Build searchable drug datasets and export to DataFrames\n- Filter drugs by type (small molecule, biotech, nutraceutical)\n\n**When to use**: Retrieving specific drug information, building drug databases, pharmacology research, literature review, drug profiling.\n\n**Reference**: See `references\u002Fdrug-queries.md` for XML navigation, query functions, data extraction methods, and performance optimization.\n\n### 3. Drug-Drug Interactions Analysis\n\nAnalyze drug-drug interactions (DDIs) including mechanism, clinical significance, and interaction networks for pharmacovigilance and clinical decision support.\n\n**Analysis capabilities**:\n- Extract all interactions for specific drugs\n- Build bidirectional interaction networks\n- Classify interactions by severity and mechanism\n- Check interactions between drug pairs\n- Identify drugs with most interactions\n- Analyze polypharmacy regimens for safety\n- Create interaction matrices and network graphs\n- Perform community detection in interaction networks\n- Calculate interaction risk scores\n\n**When to use**: Polypharmacy safety analysis, clinical decision support, drug interaction prediction, pharmacovigilance research, identifying contraindications.\n\n**Reference**: See `references\u002Finteractions.md` for interaction extraction, classification methods, network analysis, and clinical applications.\n\n### 4. Drug Targets and Pathways\n\nAccess detailed information about drug-protein interactions including targets, enzymes, transporters, carriers, and biological pathways.\n\n**Target analysis capabilities**:\n- Extract drug targets with actions (inhibitor, agonist, antagonist)\n- Identify metabolic enzymes (CYP450, Phase II enzymes)\n- Analyze transporters (uptake, efflux) for ADME studies\n- Map drugs to biological pathways (SMPDB)\n- Find drugs targeting specific proteins\n- Identify drugs with shared targets for repurposing\n- Analyze polypharmacology and off-target effects\n- Extract Gene Ontology (GO) terms for targets\n- Cross-reference with UniProt for protein data\n\n**When to use**: Mechanism of action studies, drug repurposing research, target identification, pathway analysis, predicting off-target effects, understanding drug metabolism.\n\n**Reference**: See `references\u002Ftargets-pathways.md` for target extraction, pathway analysis, repurposing strategies, CYP450 profiling, and transporter analysis.\n\n### 5. Chemical Properties and Similarity\n\nPerform structure-based analysis including molecular similarity searches, property calculations, substructure searches, and ADMET predictions.\n\n**Chemical analysis capabilities**:\n- Extract chemical structures (SMILES, InChI, molecular formula)\n- Calculate physicochemical properties (MW, logP, PSA, H-bonds)\n- Apply Lipinski's Rule of Five and Veber's rules\n- Calculate Tanimoto similarity between molecules\n- Generate molecular fingerprints (Morgan, MACCS, topological)\n- Perform substructure searches with SMARTS patterns\n- Find structurally similar drugs for repurposing\n- Create similarity matrices for drug clustering\n- Predict oral absorption and BBB permeability\n- Analyze chemical space with PCA and clustering\n- Export chemical property databases\n\n**When to use**: Structure-activity relationship (SAR) studies, drug similarity searches, QSAR modeling, drug-likeness assessment, ADMET prediction, chemical space exploration.\n\n**Reference**: See `references\u002Fchemical-analysis.md` for structure extraction, similarity calculations, fingerprint generation, ADMET predictions, and chemical space analysis.\n\n## Typical Workflows\n\n### Drug Discovery Workflow\n1. Use `data-access.md` to download and access latest DrugBank data\n2. Use `drug-queries.md` to build searchable drug database\n3. Use `chemical-analysis.md` to find similar compounds\n4. Use `targets-pathways.md` to identify shared targets\n5. Use `interactions.md` to check safety of candidate combinations\n\n### Polypharmacy Safety Analysis\n1. Use `drug-queries.md` to look up patient medications\n2. Use `interactions.md` to check all pairwise interactions\n3. Use `interactions.md` to classify interaction severity\n4. Use `interactions.md` to calculate overall risk score\n5. Use `targets-pathways.md` to understand interaction mechanisms\n\n### Drug Repurposing Research\n1. Use `targets-pathways.md` to find drugs with shared targets\n2. Use `chemical-analysis.md` to find structurally similar drugs\n3. Use `drug-queries.md` to extract indication and pharmacology data\n4. Use `interactions.md` to assess potential combination therapies\n\n### Pharmacology Study\n1. Use `drug-queries.md` to extract drug of interest\n2. Use `targets-pathways.md` to identify all protein interactions\n3. Use `targets-pathways.md` to map to biological pathways\n4. Use `chemical-analysis.md` to predict ADMET properties\n5. Use `interactions.md` to identify potential contraindications\n\n## Installation Requirements\n\n### Python Packages\n```bash\nuv pip install drugbank-downloader  # Core access\nuv pip install bioversions          # Latest version detection\nuv pip install lxml                 # XML parsing optimization\nuv pip install pandas               # Data manipulation\nuv pip install rdkit                # Chemical informatics (for similarity)\nuv pip install networkx             # Network analysis (for interactions)\nuv pip install scikit-learn         # ML\u002Fclustering (for chemical space)\n```\n\n### Account Setup\n1. Create free account at go.drugbank.com\n2. Accept license agreement (free for academic use)\n3. Obtain username and password credentials\n4. Configure credentials as documented in `references\u002Fdata-access.md`\n\n## Data Version and Reproducibility\n\nAlways specify the DrugBank version for reproducible research:\n\n```python\nfrom drugbank_downloader import download_drugbank\npath = download_drugbank(version='5.1.10')  # Specify exact version\n```\n\nDocument the version used in publications and analysis scripts.\n\n## Best Practices\n\n1. **Credentials**: Use environment variables or config files, never hardcode\n2. **Versioning**: Specify exact database version for reproducibility\n3. **Caching**: Cache parsed data to avoid re-downloading and re-parsing\n4. **Namespaces**: Handle XML namespaces properly when parsing\n5. **Validation**: Validate chemical structures with RDKit before use\n6. **Cross-referencing**: Use external identifiers (UniProt, PubChem) for integration\n7. **Clinical Context**: Always consider clinical context when interpreting interaction data\n8. **License Compliance**: Ensure proper licensing for your use case\n\n## Reference Documentation\n\nAll detailed implementation guidance is organized in modular reference files:\n\n- **references\u002Fdata-access.md**: Authentication, download, parsing, API access, caching\n- **references\u002Fdrug-queries.md**: XML navigation, query methods, data extraction, indexing\n- **references\u002Finteractions.md**: DDI extraction, classification, network analysis, safety scoring\n- **references\u002Ftargets-pathways.md**: Target\u002Fenzyme\u002Ftransporter extraction, pathway mapping, repurposing\n- **references\u002Fchemical-analysis.md**: Structure extraction, similarity, fingerprints, ADMET prediction\n\nLoad these references as needed based on your specific analysis requirements.\n","# DrugBank Database\n\n## Overview\n\nDrugBank is a comprehensive bioinformatics and cheminformatics database containing detailed information on drugs and drug targets. This skill enables programmatic access to DrugBank data including ~9,591 drug entries (2,037 FDA-approved small molecules, 241 biotech drugs, 96 nutraceuticals, and 6,000+ experimental compounds) with 200+ data fields per entry.\n\n## Core Capabilities\n\n### 1. Data Access and Authentication\n\nDownload and access DrugBank data using Python with proper authentication. The skill provides guidance on:\n\n- Installing and configuring the `drugbank-downloader` package\n- Managing credentials securely via environment variables or config files\n- Downloading specific or latest database versions\n- Opening and parsing XML data efficiently\n- Working with cached data to optimize performance\n\n**When to use**: Setting up DrugBank access, downloading database updates, initial project configuration.\n\n**Reference**: See `references\u002Fdata-access.md` for detailed authentication, download procedures, API access, caching strategies, and troubleshooting.\n\n### 2. Drug Information Queries\n\nExtract comprehensive drug information from the database including identifiers, chemical properties, pharmacology, clinical data, and cross-references to external databases.\n\n**Query capabilities**:\n- Search by DrugBank ID, name, CAS number, or keywords\n- Extract basic drug information (name, type, description, indication)\n- Retrieve chemical properties (SMILES, InChI, molecular formula)\n- Get pharmacology data (mechanism of action, pharmacodynamics, ADME)\n- Access external identifiers (PubChem, ChEMBL, UniProt, KEGG)\n- Build searchable drug datasets and export to DataFrames\n- Filter drugs by type (small molecule, biotech, nutraceutical)\n\n**When to use**: Retrieving specific drug information, building drug databases, pharmacology research, literature review, drug profiling.\n\n**Reference**: See `references\u002Fdrug-queries.md` for XML navigation, query functions, data extraction methods, and performance optimization.\n\n### 3. Drug-Drug Interactions Analysis\n\nAnalyze drug-drug interactions (DDIs) including mechanism, clinical significance, and interaction networks for pharmacovigilance and clinical decision support.\n\n**Analysis capabilities**:\n- Extract all interactions for specific drugs\n- Build bidirectional interaction networks\n- Classify interactions by severity and mechanism\n- Check interactions between drug pairs\n- Identify drugs with most interactions\n- Analyze polypharmacy regimens for safety\n- Create interaction matrices and network graphs\n- Perform community detection in interaction networks\n- Calculate interaction risk scores\n\n**When to use**: Polypharmacy safety analysis, clinical decision support, drug interaction prediction, pharmacovigilance research, identifying contraindications.\n\n**Reference**: See `references\u002Finteractions.md` for interaction extraction, classification methods, network analysis, and clinical applications.\n\n### 4. Drug Targets and Pathways\n\nAccess detailed information about drug-protein interactions including targets, enzymes, transporters, carriers, and biological pathways.\n\n**Target analysis capabilities**:\n- Extract drug targets with actions (inhibitor, agonist, antagonist)\n- Identify metabolic enzymes (CYP450, Phase II enzymes)\n- Analyze transporters (uptake, efflux) for ADME studies\n- Map drugs to biological pathways (SMPDB)\n- Find drugs targeting specific proteins\n- Identify drugs with shared targets for repurposing\n- Analyze polypharmacology and off-target effects\n- Extract Gene Ontology (GO) terms for targets\n- Cross-reference with UniProt for protein data\n\n**When to use**: Mechanism of action studies, drug repurposing research, target identification, pathway analysis, predicting off-target effects, understanding drug metabolism.\n\n**Reference**: See `references\u002Ftargets-pathways.md` for target extraction, pathway analysis, repurposing strategies, CYP450 profiling, and transporter analysis.\n\n### 5. Chemical Properties and Similarity\n\nPerform structure-based analysis including molecular similarity searches, property calculations, substructure searches, and ADMET predictions.\n\n**Chemical analysis capabilities**:\n- Extract chemical structures (SMILES, InChI, molecular formula)\n- Calculate physicochemical properties (MW, logP, PSA, H-bonds)\n- Apply Lipinski's Rule of Five and Veber's rules\n- Calculate Tanimoto similarity between molecules\n- Generate molecular fingerprints (Morgan, MACCS, topological)\n- Perform substructure searches with SMARTS patterns\n- Find structurally similar drugs for repurposing\n- Create similarity matrices for drug clustering\n- Predict oral absorption and BBB permeability\n- Analyze chemical space with PCA and clustering\n- Export chemical property databases\n\n**When to use**: Structure-activity relationship (SAR) studies, drug similarity searches, QSAR modeling, drug-likeness assessment, ADMET prediction, chemical space exploration.\n\n**Reference**: See `references\u002Fchemical-analysis.md` for structure extraction, similarity calculations, fingerprint generation, ADMET predictions, and chemical space analysis.\n\n## Typical Workflows\n\n### Drug Discovery Workflow\n1. Use `data-access.md` to download and access latest DrugBank data\n2. Use `drug-queries.md` to build searchable drug database\n3. Use `chemical-analysis.md` to find similar compounds\n4. Use `targets-pathways.md` to identify shared targets\n5. Use `interactions.md` to check safety of candidate combinations\n\n### Polypharmacy Safety Analysis\n1. Use `drug-queries.md` to look up patient medications\n2. Use `interactions.md` to check all pairwise interactions\n3. Use `interactions.md` to classify interaction severity\n4. Use `interactions.md` to calculate overall risk score\n5. Use `targets-pathways.md` to understand interaction mechanisms\n\n### Drug Repurposing Research\n1. Use `targets-pathways.md` to find drugs with shared targets\n2. Use `chemical-analysis.md` to find structurally similar drugs\n3. Use `drug-queries.md` to extract indication and pharmacology data\n4. Use `interactions.md` to assess potential combination therapies\n\n### Pharmacology Study\n1. Use `drug-queries.md` to extract drug of interest\n2. Use `targets-pathways.md` to identify all protein interactions\n3. Use `targets-pathways.md` to map to biological pathways\n4. Use `chemical-analysis.md` to predict ADMET properties\n5. Use `interactions.md` to identify potential contraindications\n\n## Installation Requirements\n\n### Python Packages\n```bash\nuv pip install drugbank-downloader  # Core access\nuv pip install bioversions          # Latest version detection\nuv pip install lxml                 # XML parsing optimization\nuv pip install pandas               # Data manipulation\nuv pip install rdkit                # Chemical informatics (for similarity)\nuv pip install networkx             # Network analysis (for interactions)\nuv pip install scikit-learn         # ML\u002Fclustering (for chemical space)\n```\n\n### Account Setup\n1. Create free account at go.drugbank.com\n2. Accept license agreement (free for academic use)\n3. Obtain username and password credentials\n4. Configure credentials as documented in `references\u002Fdata-access.md`\n\n## Data Version and Reproducibility\n\nAlways specify the DrugBank version for reproducible research:\n\n```python\nfrom drugbank_downloader import download_drugbank\npath = download_drugbank(version='5.1.10')  # Specify exact version\n```\n\nDocument the version used in publications and analysis scripts.\n\n## Best Practices\n\n1. **Credentials**: Use environment variables or config files, never hardcode\n2. **Versioning**: Specify exact database version for reproducibility\n3. **Caching**: Cache parsed data to avoid re-downloading and re-parsing\n4. **Namespaces**: Handle XML namespaces properly when parsing\n5. **Validation**: Validate chemical structures with RDKit before use\n6. **Cross-referencing**: Use external identifiers (UniProt, PubChem) for integration\n7. **Clinical Context**: Always consider clinical context when interpreting interaction data\n8. **License Compliance**: Ensure proper licensing for your use case\n\n## Reference Documentation\n\nAll detailed implementation guidance is organized in modular reference files:\n\n- **references\u002Fdata-access.md**: Authentication, download, parsing, API access, caching\n- **references\u002Fdrug-queries.md**: XML navigation, query methods, data extraction, indexing\n- **references\u002Finteractions.md**: DDI extraction, classification, network analysis, safety scoring\n- **references\u002Ftargets-pathways.md**: Target\u002Fenzyme\u002Ftransporter extraction, pathway mapping, repurposing\n- **references\u002Fchemical-analysis.md**: Structure extraction, similarity, fingerprints, ADMET prediction\n\nLoad these references as needed based on your specific analysis 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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",[144],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[146,147,148,149,150,151,152,153,154,33,155,36,156,157,158,159,160,161,162,163],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":171,"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",[170],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[172,173,174,175,176,177,178,179,180,181,182,33,183,184,185,186,187,188,189,190],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":204,"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",[197,198,199],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":200,"categoryName":201,"taxonomyType":202,"taxonomyTypeDesc":203},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[205,206,207,208,209,210,211,151,212,213,214,215,216,217,218,154,46,30,33,219],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","query",{"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":224,"licenseFileUrl":225,"sourceHtmlUrl":226,"categories":227,"keywords":231,"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",[228],{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,151,254],"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":256,"name":257,"nameZh":6,"description":258,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":224,"licenseFileUrl":259,"sourceHtmlUrl":260,"categories":261,"keywords":266,"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",[262,265],{"categoryNo":263,"categoryName":264,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},[267,268,269,270,271,233,272,242,243,273,274,275,276,151,277,36,278],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":280,"name":281,"nameZh":6,"description":282,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":224,"licenseFileUrl":283,"sourceHtmlUrl":284,"categories":285,"keywords":293,"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",[286,289,290],{"categoryNo":287,"categoryName":288,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":291,"categoryName":292,"taxonomyType":202,"taxonomyTypeDesc":203},"01JBSEEDCAT000000000000036","内容创作者",[294,295,296,297,298,299,49,300,235,301,245,183,302,303,304,305,306,217,307,33,308,48,309,310,311,161,312],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]