[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFP1C5GG00TWYTM8NGDSNE":3,"skill-same:01M6AFP1C5GG00TWYTM8NGDSNE":117},{"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":26,"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},"01M6AFP1C5GG00TWYTM8NGDSNE","scvi-tools",null,"This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33418,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscvi-tools\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"Tools","scvi tools","scvi","expression","when","single","for","seq","differential expression","transcriptomics","omics","annotation","spatial transcriptomics","data analysis","modeling","analysis","data","integration","multimodal","batch","tasks","2026-11-10 10:12:30",false,"---\nname: scvi-tools\ndescription: This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.\n---\n\n# scvi-tools\n\n## Overview\n\nscvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.\n\n## When to Use This Skill\n\nUse this skill when:\n- Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)\n- Working with single-cell ATAC-seq or chromatin accessibility data\n- Integrating multimodal data (CITE-seq, multiome, paired\u002Funpaired datasets)\n- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)\n- Performing differential expression analysis on single-cell data\n- Conducting cell type annotation or transfer learning tasks\n- Working with specialized single-cell modalities (methylation, cytometry, RNA velocity)\n- Building custom probabilistic models for single-cell analysis\n\n## Core Capabilities\n\nscvi-tools provides models organized by data modality:\n\n### 1. Single-Cell RNA-seq Analysis\nCore models for expression analysis, batch correction, and integration. See `references\u002Fmodels-scrna-seq.md` for:\n- **scVI**: Unsupervised dimensionality reduction and batch correction\n- **scANVI**: Semi-supervised cell type annotation and integration\n- **AUTOZI**: Zero-inflation detection and modeling\n- **VeloVI**: RNA velocity analysis\n- **contrastiveVI**: Perturbation effect isolation\n\n### 2. Chromatin Accessibility (ATAC-seq)\nModels for analyzing single-cell chromatin data. See `references\u002Fmodels-atac-seq.md` for:\n- **PeakVI**: Peak-based ATAC-seq analysis and integration\n- **PoissonVI**: Quantitative fragment count modeling\n- **scBasset**: Deep learning approach with motif analysis\n\n### 3. Multimodal & Multi-omics Integration\nJoint analysis of multiple data types. See `references\u002Fmodels-multimodal.md` for:\n- **totalVI**: CITE-seq protein and RNA joint modeling\n- **MultiVI**: Paired and unpaired multi-omic integration\n- **MrVI**: Multi-resolution cross-sample analysis\n\n### 4. Spatial Transcriptomics\nSpatially-resolved transcriptomics analysis. See `references\u002Fmodels-spatial.md` for:\n- **DestVI**: Multi-resolution spatial deconvolution\n- **Stereoscope**: Cell type deconvolution\n- **Tangram**: Spatial mapping and integration\n- **scVIVA**: Cell-environment relationship analysis\n\n### 5. Specialized Modalities\nAdditional specialized analysis tools. See `references\u002Fmodels-specialized.md` for:\n- **MethylVI\u002FMethylANVI**: Single-cell methylation analysis\n- **CytoVI**: Flow\u002Fmass cytometry batch correction\n- **Solo**: Doublet detection\n- **CellAssign**: Marker-based cell type annotation\n\n## Typical Workflow\n\nAll scvi-tools models follow a consistent API pattern:\n\n```python\n# 1. Load and preprocess data (AnnData format)\nimport scvi\nimport scanpy as sc\n\nadata = scvi.data.heart_cell_atlas_subsampled()\nsc.pp.filter_genes(adata, min_counts=3)\nsc.pp.highly_variable_genes(adata, n_top_genes=1200)\n\n# 2. Register data with model (specify layers, covariates)\nscvi.model.SCVI.setup_anndata(\n    adata,\n    layer=\"counts\",  # Use raw counts, not log-normalized\n    batch_key=\"batch\",\n    categorical_covariate_keys=[\"donor\"],\n    continuous_covariate_keys=[\"percent_mito\"]\n)\n\n# 3. Create and train model\nmodel = scvi.model.SCVI(adata)\nmodel.train()\n\n# 4. Extract latent representations and normalized values\nlatent = model.get_latent_representation()\nnormalized = model.get_normalized_expression(library_size=1e4)\n\n# 5. Store in AnnData for downstream analysis\nadata.obsm[\"X_scVI\"] = latent\nadata.layers[\"scvi_normalized\"] = normalized\n\n# 6. Downstream analysis with scanpy\nsc.pp.neighbors(adata, use_rep=\"X_scVI\")\nsc.tl.umap(adata)\nsc.tl.leiden(adata)\n```\n\n**Key Design Principles:**\n- **Raw counts required**: Models expect unnormalized count data for optimal performance\n- **Unified API**: Consistent interface across all models (setup → train → extract)\n- **AnnData-centric**: Seamless integration with the scanpy ecosystem\n- **GPU acceleration**: Automatic utilization of available GPUs\n- **Batch correction**: Handle technical variation through covariate registration\n\n## Common Analysis Tasks\n\n### Differential Expression\nProbabilistic DE analysis using the learned generative models:\n\n```python\nde_results = model.differential_expression(\n    groupby=\"cell_type\",\n    group1=\"TypeA\",\n    group2=\"TypeB\",\n    mode=\"change\",  # Use composite hypothesis testing\n    delta=0.25      # Minimum effect size threshold\n)\n```\n\nSee `references\u002Fdifferential-expression.md` for detailed methodology and interpretation.\n\n### Model Persistence\nSave and load trained models:\n\n```python\n# Save model\nmodel.save(\".\u002Fmodel_directory\", overwrite=True)\n\n# Load model\nmodel = scvi.model.SCVI.load(\".\u002Fmodel_directory\", adata=adata)\n```\n\n### Batch Correction and Integration\nIntegrate datasets across batches or studies:\n\n```python\n# Register batch information\nscvi.model.SCVI.setup_anndata(adata, batch_key=\"study\")\n\n# Model automatically learns batch-corrected representations\nmodel = scvi.model.SCVI(adata)\nmodel.train()\nlatent = model.get_latent_representation()  # Batch-corrected\n```\n\n## Theoretical Foundations\n\nscvi-tools is built on:\n- **Variational inference**: Approximate posterior distributions for scalable Bayesian inference\n- **Deep generative models**: VAE architectures that learn complex data distributions\n- **Amortized inference**: Shared neural networks for efficient learning across cells\n- **Probabilistic modeling**: Principled uncertainty quantification and statistical testing\n\nSee `references\u002Ftheoretical-foundations.md` for detailed background on the mathematical framework.\n\n## Additional Resources\n\n- **Workflows**: `references\u002Fworkflows.md` contains common workflows, best practices, hyperparameter tuning, and GPU optimization\n- **Model References**: Detailed documentation for each model category in the `references\u002F` directory\n- **Official Documentation**: https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002F\n- **Tutorials**: https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002Ftutorials\u002Findex.html\n- **API Reference**: https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002Fapi\u002Findex.html\n\n## Installation\n\n```bash\nuv pip install scvi-tools\n# For GPU support\nuv pip install scvi-tools[cuda]\n```\n\n## Best Practices\n\n1. **Use raw counts**: Always provide unnormalized count data to models\n2. **Filter genes**: Remove low-count genes before analysis (e.g., `min_counts=3`)\n3. **Register covariates**: Include known technical factors (batch, donor, etc.) in `setup_anndata`\n4. **Feature selection**: Use highly variable genes for improved performance\n5. **Model saving**: Always save trained models to avoid retraining\n6. **GPU usage**: Enable GPU acceleration for large datasets (`accelerator=\"gpu\"`)\n7. **Scanpy integration**: Store outputs in AnnData objects for downstream analysis\n","# scvi-tools\n\n## Overview\n\nscvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.\n\n## When to Use This Skill\n\nUse this skill when:\n- Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)\n- Working with single-cell ATAC-seq or chromatin accessibility data\n- Integrating multimodal data (CITE-seq, multiome, paired\u002Funpaired datasets)\n- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)\n- Performing differential expression analysis on single-cell data\n- Conducting cell type annotation or transfer learning tasks\n- Working with specialized single-cell modalities (methylation, cytometry, RNA velocity)\n- Building custom probabilistic models for single-cell analysis\n\n## Core Capabilities\n\nscvi-tools provides models organized by data modality:\n\n### 1. Single-Cell RNA-seq Analysis\nCore models for expression analysis, batch correction, and integration. See `references\u002Fmodels-scrna-seq.md` for:\n- **scVI**: Unsupervised dimensionality reduction and batch correction\n- **scANVI**: Semi-supervised cell type annotation and integration\n- **AUTOZI**: Zero-inflation detection and modeling\n- **VeloVI**: RNA velocity analysis\n- **contrastiveVI**: Perturbation effect isolation\n\n### 2. Chromatin Accessibility (ATAC-seq)\nModels for analyzing single-cell chromatin data. See `references\u002Fmodels-atac-seq.md` for:\n- **PeakVI**: Peak-based ATAC-seq analysis and integration\n- **PoissonVI**: Quantitative fragment count modeling\n- **scBasset**: Deep learning approach with motif analysis\n\n### 3. Multimodal & Multi-omics Integration\nJoint analysis of multiple data types. See `references\u002Fmodels-multimodal.md` for:\n- **totalVI**: CITE-seq protein and RNA joint modeling\n- **MultiVI**: Paired and unpaired multi-omic integration\n- **MrVI**: Multi-resolution cross-sample analysis\n\n### 4. Spatial Transcriptomics\nSpatially-resolved transcriptomics analysis. See `references\u002Fmodels-spatial.md` for:\n- **DestVI**: Multi-resolution spatial deconvolution\n- **Stereoscope**: Cell type deconvolution\n- **Tangram**: Spatial mapping and integration\n- **scVIVA**: Cell-environment relationship analysis\n\n### 5. Specialized Modalities\nAdditional specialized analysis tools. See `references\u002Fmodels-specialized.md` for:\n- **MethylVI\u002FMethylANVI**: Single-cell methylation analysis\n- **CytoVI**: Flow\u002Fmass cytometry batch correction\n- **Solo**: Doublet detection\n- **CellAssign**: Marker-based cell type annotation\n\n## Typical Workflow\n\nAll scvi-tools models follow a consistent API pattern:\n\n```python\n# 1. Load and preprocess data (AnnData format)\nimport scvi\nimport scanpy as sc\n\nadata = scvi.data.heart_cell_atlas_subsampled()\nsc.pp.filter_genes(adata, min_counts=3)\nsc.pp.highly_variable_genes(adata, n_top_genes=1200)\n\n# 2. Register data with model (specify layers, covariates)\nscvi.model.SCVI.setup_anndata(\n    adata,\n    layer=\"counts\",  # Use raw counts, not log-normalized\n    batch_key=\"batch\",\n    categorical_covariate_keys=[\"donor\"],\n    continuous_covariate_keys=[\"percent_mito\"]\n)\n\n# 3. Create and train model\nmodel = scvi.model.SCVI(adata)\nmodel.train()\n\n# 4. Extract latent representations and normalized values\nlatent = model.get_latent_representation()\nnormalized = model.get_normalized_expression(library_size=1e4)\n\n# 5. Store in AnnData for downstream analysis\nadata.obsm[\"X_scVI\"] = latent\nadata.layers[\"scvi_normalized\"] = normalized\n\n# 6. Downstream analysis with scanpy\nsc.pp.neighbors(adata, use_rep=\"X_scVI\")\nsc.tl.umap(adata)\nsc.tl.leiden(adata)\n```\n\n**Key Design Principles:**\n- **Raw counts required**: Models expect unnormalized count data for optimal performance\n- **Unified API**: Consistent interface across all models (setup → train → extract)\n- **AnnData-centric**: Seamless integration with the scanpy ecosystem\n- **GPU acceleration**: Automatic utilization of available GPUs\n- **Batch correction**: Handle technical variation through covariate registration\n\n## Common Analysis Tasks\n\n### Differential Expression\nProbabilistic DE analysis using the learned generative models:\n\n```python\nde_results = model.differential_expression(\n    groupby=\"cell_type\",\n    group1=\"TypeA\",\n    group2=\"TypeB\",\n    mode=\"change\",  # Use composite hypothesis testing\n    delta=0.25      # Minimum effect size threshold\n)\n```\n\nSee `references\u002Fdifferential-expression.md` for detailed methodology and interpretation.\n\n### Model Persistence\nSave and load trained models:\n\n```python\n# Save model\nmodel.save(\".\u002Fmodel_directory\", overwrite=True)\n\n# Load model\nmodel = scvi.model.SCVI.load(\".\u002Fmodel_directory\", adata=adata)\n```\n\n### Batch Correction and Integration\nIntegrate datasets across batches or studies:\n\n```python\n# Register batch information\nscvi.model.SCVI.setup_anndata(adata, batch_key=\"study\")\n\n# Model automatically learns batch-corrected representations\nmodel = scvi.model.SCVI(adata)\nmodel.train()\nlatent = model.get_latent_representation()  # Batch-corrected\n```\n\n## Theoretical Foundations\n\nscvi-tools is built on:\n- **Variational inference**: Approximate posterior distributions for scalable Bayesian inference\n- **Deep generative models**: VAE architectures that learn complex data distributions\n- **Amortized inference**: Shared neural networks for efficient learning across cells\n- **Probabilistic modeling**: Principled uncertainty quantification and statistical testing\n\nSee `references\u002Ftheoretical-foundations.md` for detailed background on the mathematical framework.\n\n## Additional Resources\n\n- **Workflows**: `references\u002Fworkflows.md` contains common workflows, best practices, hyperparameter tuning, and GPU optimization\n- **Model References**: Detailed documentation for each model category in the `references\u002F` directory\n- **Official Documentation**: https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002F\n- **Tutorials**: https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002Ftutorials\u002Findex.html\n- **API Reference**: https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002Fapi\u002Findex.html\n\n## Installation\n\n```bash\nuv pip install scvi-tools\n# For GPU support\nuv pip install scvi-tools[cuda]\n```\n\n## Best Practices\n\n1. **Use raw counts**: Always provide unnormalized count data to models\n2. **Filter genes**: Remove low-count genes before analysis (e.g., `min_counts=3`)\n3. **Register covariates**: Include known technical factors (batch, donor, etc.) in `setup_anndata`\n4. **Feature selection**: Use highly variable genes for improved performance\n5. **Model saving**: Always save trained models to avoid retraining\n6. **GPU usage**: Enable GPU acceleration for large datasets (`accelerator=\"gpu\"`)\n7. **Scanpy integration**: Store outputs in AnnData objects for downstream analysis\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscvi-tools",3926,"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","2026-11-12 14:45:56",{"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 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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",[124],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[126,127,128,129,130,33,131,132,133,31,134,135,136,137,138,139,140,141,142,143],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":151,"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",[150],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[152,153,154,155,156,157,158,159,160,161,162,31,163,164,165,166,167,168,169,170],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":186,"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",[177,180,181],{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":182,"categoryName":183,"taxonomyType":184,"taxonomyTypeDesc":185},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[187,188,189,190,191,192,193,33,194,195,196,197,198,199,200,133,201,202,31,203],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":208,"licenseFileUrl":209,"sourceHtmlUrl":210,"categories":211,"keywords":215,"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",[212],{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[216,217,218,219,220,221,222,223,224,225,226,227,27,228,229,230,231,232,233,234,235,236,33,237],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":239,"name":240,"nameZh":6,"description":241,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":208,"licenseFileUrl":242,"sourceHtmlUrl":243,"categories":244,"keywords":249,"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",[245,248],{"categoryNo":246,"categoryName":247,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},[250,251,252,253,254,217,255,226,227,256,257,258,259,33,260,135,261],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":263,"name":264,"nameZh":6,"description":265,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":208,"licenseFileUrl":266,"sourceHtmlUrl":267,"categories":268,"keywords":276,"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. 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",[269,272,273],{"categoryNo":270,"categoryName":271,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":274,"categoryName":275,"taxonomyType":184,"taxonomyTypeDesc":185},"01JBSEEDCAT000000000000036","内容创作者",[277,278,279,280,281,282,43,283,219,284,228,163,285,286,287,288,289,199,290,31,291,42,292,293,294,141,295],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]