[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NQG74REDNTA1V3X2DWRB2":3,"skill-same:01M71NQG74REDNTA1V3X2DWRB2":161},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":24,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M71NQG74REDNTA1V3X2DWRB2","scvi-tools",null,"Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI\u002FscANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI\u002FscArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.","vitalikpestov\u002FSkills-Catalog","vitalikpestov","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F13882997?v=4","01M71N4QG0CWRXX4R24JQZXDDQ","Vitalik Pestov","01M71N4QGMSYRW607DDSE0CQMM",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002FSKILL.md",[19],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"scvi tools","scvi","Tools","learning","for","velovi","mapping","seq","data","deconvolution","transcriptomics","velocity","RNA","spatial transcriptomics","batch","analysis","when","deep learning","destvi","deep","2026-11-10 10:56:23",false,"---\nname: scvi-tools\ndescription: Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI\u002FscANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI\u002FscArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.\n---\n\n# scvi-tools Deep Learning Skill\n\nThis skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.\n\n## How to Use This Skill\n\n1. Identify the appropriate workflow from the model\u002Fworkflow tables below\n2. Read the corresponding reference file for detailed steps and code\n3. Use scripts in `scripts\u002F` to avoid rewriting common code\n4. For installation or GPU issues, consult `references\u002Fenvironment_setup.md`\n5. For debugging, consult `references\u002Ftroubleshooting.md`\n\n## When to Use This Skill\n\n- When scvi-tools, scVI, scANVI, or related models are mentioned\n- When deep learning-based batch correction or integration is needed\n- When working with multi-modal data (CITE-seq, multiome)\n- When reference mapping or label transfer is required\n- When analyzing ATAC-seq or spatial transcriptomics data\n- When learning latent representations of single-cell data\n\n## Model Selection Guide\n\n| Data Type | Model | Primary Use Case |\n|-----------|-------|------------------|\n| scRNA-seq | **scVI** | Unsupervised integration, DE, imputation |\n| scRNA-seq + labels | **scANVI** | Label transfer, semi-supervised integration |\n| CITE-seq (RNA+protein) | **totalVI** | Multi-modal integration, protein denoising |\n| scATAC-seq | **PeakVI** | Chromatin accessibility analysis |\n| Multiome (RNA+ATAC) | **MultiVI** | Joint modality analysis |\n| Spatial + scRNA reference | **DestVI** | Cell type deconvolution |\n| RNA velocity | **veloVI** | Transcriptional dynamics |\n| Cross-technology | **sysVI** | System-level batch correction |\n\n## Workflow Reference Files\n\n| Workflow | Reference File | Description |\n|----------|---------------|-------------|\n| Environment Setup | `references\u002Fenvironment_setup.md` | Installation, GPU, version info |\n| Data Preparation | `references\u002Fdata_preparation.md` | Formatting data for any model |\n| scRNA Integration | `references\u002Fscrna_integration.md` | scVI\u002FscANVI batch correction |\n| ATAC-seq Analysis | `references\u002Fatac_peakvi.md` | PeakVI for accessibility |\n| CITE-seq Analysis | `references\u002Fciteseq_totalvi.md` | totalVI for protein+RNA |\n| Multiome Analysis | `references\u002Fmultiome_multivi.md` | MultiVI for RNA+ATAC |\n| Spatial Deconvolution | `references\u002Fspatial_deconvolution.md` | DestVI spatial analysis |\n| Label Transfer | `references\u002Flabel_transfer.md` | scANVI reference mapping |\n| scArches Mapping | `references\u002Fscarches_mapping.md` | Query-to-reference mapping |\n| Batch Correction | `references\u002Fbatch_correction_sysvi.md` | Advanced batch methods |\n| RNA Velocity | `references\u002Frna_velocity_velovi.md` | veloVI dynamics |\n| Troubleshooting | `references\u002Ftroubleshooting.md` | Common issues and solutions |\n\n## CLI Scripts\n\nModular scripts for common workflows. Chain together or modify as needed.\n\n### Pipeline Scripts\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `prepare_data.py` | QC, filter, HVG selection | `python scripts\u002Fprepare_data.py raw.h5ad prepared.h5ad --batch-key batch` |\n| `train_model.py` | Train any scvi-tools model | `python scripts\u002Ftrain_model.py prepared.h5ad results\u002F --model scvi` |\n| `cluster_embed.py` | Neighbors, UMAP, Leiden | `python scripts\u002Fcluster_embed.py adata.h5ad results\u002F` |\n| `differential_expression.py` | DE analysis | `python scripts\u002Fdifferential_expression.py model\u002F adata.h5ad de.csv --groupby leiden` |\n| `transfer_labels.py` | Label transfer with scANVI | `python scripts\u002Ftransfer_labels.py ref_model\u002F query.h5ad results\u002F` |\n| `integrate_datasets.py` | Multi-dataset integration | `python scripts\u002Fintegrate_datasets.py results\u002F data1.h5ad data2.h5ad` |\n| `validate_adata.py` | Check data compatibility | `python scripts\u002Fvalidate_adata.py data.h5ad --batch-key batch` |\n\n### Example Workflow\n\n```bash\n# 1. Validate input data\npython scripts\u002Fvalidate_adata.py raw.h5ad --batch-key batch --suggest\n\n# 2. Prepare data (QC, HVG selection)\npython scripts\u002Fprepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000\n\n# 3. Train model\npython scripts\u002Ftrain_model.py prepared.h5ad results\u002F --model scvi --batch-key batch\n\n# 4. Cluster and visualize\npython scripts\u002Fcluster_embed.py results\u002Fadata_trained.h5ad results\u002F --resolution 0.8\n\n# 5. Differential expression\npython scripts\u002Fdifferential_expression.py results\u002Fmodel results\u002Fadata_clustered.h5ad results\u002Fde.csv --groupby leiden\n```\n\n### Python Utilities\n\nThe `scripts\u002Fmodel_utils.py` provides importable functions for custom workflows:\n\n| Function | Purpose |\n|----------|---------|\n| `prepare_adata()` | Data preparation (QC, HVG, layer setup) |\n| `train_scvi()` | Train scVI or scANVI |\n| `evaluate_integration()` | Compute integration metrics |\n| `get_marker_genes()` | Extract DE markers |\n| `save_results()` | Save model, data, plots |\n| `auto_select_model()` | Suggest best model |\n| `quick_clustering()` | Neighbors + UMAP + Leiden |\n\n## Critical Requirements\n\n1. **Raw counts required**: scvi-tools models require integer count data\n   ```python\n   adata.layers[\"counts\"] = adata.X.copy()  # Before normalization\n   scvi.model.SCVI.setup_anndata(adata, layer=\"counts\")\n   ```\n\n2. **HVG selection**: Use 2000-4000 highly variable genes\n   ```python\n   sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key=\"batch\", layer=\"counts\", flavor=\"seurat_v3\")\n   adata = adata[:, adata.var['highly_variable']].copy()\n   ```\n\n3. **Batch information**: Specify batch_key for integration\n   ```python\n   scvi.model.SCVI.setup_anndata(adata, layer=\"counts\", batch_key=\"batch\")\n   ```\n\n## Quick Decision Tree\n\n```\nNeed to integrate scRNA-seq data?\n├── Have cell type labels? → scANVI (references\u002Flabel_transfer.md)\n└── No labels? → scVI (references\u002Fscrna_integration.md)\n\nHave multi-modal data?\n├── CITE-seq (RNA + protein)? → totalVI (references\u002Fciteseq_totalvi.md)\n├── Multiome (RNA + ATAC)? → MultiVI (references\u002Fmultiome_multivi.md)\n└── scATAC-seq only? → PeakVI (references\u002Fatac_peakvi.md)\n\nHave spatial data?\n└── Need cell type deconvolution? → DestVI (references\u002Fspatial_deconvolution.md)\n\nHave pre-trained reference model?\n└── Map query to reference? → scArches (references\u002Fscarches_mapping.md)\n\nNeed RNA velocity?\n└── veloVI (references\u002Frna_velocity_velovi.md)\n\nStrong cross-technology batch effects?\n└── sysVI (references\u002Fbatch_correction_sysvi.md)\n```\n\n## Key Resources\n\n- [scvi-tools Documentation](https:\u002F\u002Fdocs.scvi-tools.org\u002F)\n- [scvi-tools Tutorials](https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002Ftutorials\u002Findex.html)\n- [Model Hub](https:\u002F\u002Fhuggingface.co\u002Fscvi-tools)\n- [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fscverse\u002Fscvi-tools\u002Fissues)\n","# scvi-tools Deep Learning Skill\n\nThis skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.\n\n## How to Use This Skill\n\n1. Identify the appropriate workflow from the model\u002Fworkflow tables below\n2. Read the corresponding reference file for detailed steps and code\n3. Use scripts in `scripts\u002F` to avoid rewriting common code\n4. For installation or GPU issues, consult `references\u002Fenvironment_setup.md`\n5. For debugging, consult `references\u002Ftroubleshooting.md`\n\n## When to Use This Skill\n\n- When scvi-tools, scVI, scANVI, or related models are mentioned\n- When deep learning-based batch correction or integration is needed\n- When working with multi-modal data (CITE-seq, multiome)\n- When reference mapping or label transfer is required\n- When analyzing ATAC-seq or spatial transcriptomics data\n- When learning latent representations of single-cell data\n\n## Model Selection Guide\n\n| Data Type | Model | Primary Use Case |\n|-----------|-------|------------------|\n| scRNA-seq | **scVI** | Unsupervised integration, DE, imputation |\n| scRNA-seq + labels | **scANVI** | Label transfer, semi-supervised integration |\n| CITE-seq (RNA+protein) | **totalVI** | Multi-modal integration, protein denoising |\n| scATAC-seq | **PeakVI** | Chromatin accessibility analysis |\n| Multiome (RNA+ATAC) | **MultiVI** | Joint modality analysis |\n| Spatial + scRNA reference | **DestVI** | Cell type deconvolution |\n| RNA velocity | **veloVI** | Transcriptional dynamics |\n| Cross-technology | **sysVI** | System-level batch correction |\n\n## Workflow Reference Files\n\n| Workflow | Reference File | Description |\n|----------|---------------|-------------|\n| Environment Setup | `references\u002Fenvironment_setup.md` | Installation, GPU, version info |\n| Data Preparation | `references\u002Fdata_preparation.md` | Formatting data for any model |\n| scRNA Integration | `references\u002Fscrna_integration.md` | scVI\u002FscANVI batch correction |\n| ATAC-seq Analysis | `references\u002Fatac_peakvi.md` | PeakVI for accessibility |\n| CITE-seq Analysis | `references\u002Fciteseq_totalvi.md` | totalVI for protein+RNA |\n| Multiome Analysis | `references\u002Fmultiome_multivi.md` | MultiVI for RNA+ATAC |\n| Spatial Deconvolution | `references\u002Fspatial_deconvolution.md` | DestVI spatial analysis |\n| Label Transfer | `references\u002Flabel_transfer.md` | scANVI reference mapping |\n| scArches Mapping | `references\u002Fscarches_mapping.md` | Query-to-reference mapping |\n| Batch Correction | `references\u002Fbatch_correction_sysvi.md` | Advanced batch methods |\n| RNA Velocity | `references\u002Frna_velocity_velovi.md` | veloVI dynamics |\n| Troubleshooting | `references\u002Ftroubleshooting.md` | Common issues and solutions |\n\n## CLI Scripts\n\nModular scripts for common workflows. Chain together or modify as needed.\n\n### Pipeline Scripts\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `prepare_data.py` | QC, filter, HVG selection | `python scripts\u002Fprepare_data.py raw.h5ad prepared.h5ad --batch-key batch` |\n| `train_model.py` | Train any scvi-tools model | `python scripts\u002Ftrain_model.py prepared.h5ad results\u002F --model scvi` |\n| `cluster_embed.py` | Neighbors, UMAP, Leiden | `python scripts\u002Fcluster_embed.py adata.h5ad results\u002F` |\n| `differential_expression.py` | DE analysis | `python scripts\u002Fdifferential_expression.py model\u002F adata.h5ad de.csv --groupby leiden` |\n| `transfer_labels.py` | Label transfer with scANVI | `python scripts\u002Ftransfer_labels.py ref_model\u002F query.h5ad results\u002F` |\n| `integrate_datasets.py` | Multi-dataset integration | `python scripts\u002Fintegrate_datasets.py results\u002F data1.h5ad data2.h5ad` |\n| `validate_adata.py` | Check data compatibility | `python scripts\u002Fvalidate_adata.py data.h5ad --batch-key batch` |\n\n### Example Workflow\n\n```bash\n# 1. Validate input data\npython scripts\u002Fvalidate_adata.py raw.h5ad --batch-key batch --suggest\n\n# 2. Prepare data (QC, HVG selection)\npython scripts\u002Fprepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000\n\n# 3. Train model\npython scripts\u002Ftrain_model.py prepared.h5ad results\u002F --model scvi --batch-key batch\n\n# 4. Cluster and visualize\npython scripts\u002Fcluster_embed.py results\u002Fadata_trained.h5ad results\u002F --resolution 0.8\n\n# 5. Differential expression\npython scripts\u002Fdifferential_expression.py results\u002Fmodel results\u002Fadata_clustered.h5ad results\u002Fde.csv --groupby leiden\n```\n\n### Python Utilities\n\nThe `scripts\u002Fmodel_utils.py` provides importable functions for custom workflows:\n\n| Function | Purpose |\n|----------|---------|\n| `prepare_adata()` | Data preparation (QC, HVG, layer setup) |\n| `train_scvi()` | Train scVI or scANVI |\n| `evaluate_integration()` | Compute integration metrics |\n| `get_marker_genes()` | Extract DE markers |\n| `save_results()` | Save model, data, plots |\n| `auto_select_model()` | Suggest best model |\n| `quick_clustering()` | Neighbors + UMAP + Leiden |\n\n## Critical Requirements\n\n1. **Raw counts required**: scvi-tools models require integer count data\n   ```python\n   adata.layers[\"counts\"] = adata.X.copy()  # Before normalization\n   scvi.model.SCVI.setup_anndata(adata, layer=\"counts\")\n   ```\n\n2. **HVG selection**: Use 2000-4000 highly variable genes\n   ```python\n   sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key=\"batch\", layer=\"counts\", flavor=\"seurat_v3\")\n   adata = adata[:, adata.var['highly_variable']].copy()\n   ```\n\n3. **Batch information**: Specify batch_key for integration\n   ```python\n   scvi.model.SCVI.setup_anndata(adata, layer=\"counts\", batch_key=\"batch\")\n   ```\n\n## Quick Decision Tree\n\n```\nNeed to integrate scRNA-seq data?\n├── Have cell type labels? → scANVI (references\u002Flabel_transfer.md)\n└── No labels? → scVI (references\u002Fscrna_integration.md)\n\nHave multi-modal data?\n├── CITE-seq (RNA + protein)? → totalVI (references\u002Fciteseq_totalvi.md)\n├── Multiome (RNA + ATAC)? → MultiVI (references\u002Fmultiome_multivi.md)\n└── scATAC-seq only? → PeakVI (references\u002Fatac_peakvi.md)\n\nHave spatial data?\n└── Need cell type deconvolution? → DestVI (references\u002Fspatial_deconvolution.md)\n\nHave pre-trained reference model?\n└── Map query to reference? → scArches (references\u002Fscarches_mapping.md)\n\nNeed RNA velocity?\n└── veloVI (references\u002Frna_velocity_velovi.md)\n\nStrong cross-technology batch effects?\n└── sysVI (references\u002Fbatch_correction_sysvi.md)\n```\n\n## Key Resources\n\n- [scvi-tools Documentation](https:\u002F\u002Fdocs.scvi-tools.org\u002F)\n- [scvi-tools Tutorials](https:\u002F\u002Fdocs.scvi-tools.org\u002Fen\u002Fstable\u002Ftutorials\u002Findex.html)\n- [Model Hub](https:\u002F\u002Fhuggingface.co\u002Fscvi-tools)\n- [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fscverse\u002Fscvi-tools\u002Fissues)\n",{"name":5,"description":7},"main","fbfb242f793fdf040f3a1a6b100896c188c228c8","_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools",1,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog","https:\u002F\u002Fgithub.com\u002Fvitalikpestov","Listing on crypto exchanges","2026-06-15 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s-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Fcluster_embed.py",{"path":134,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":135,"sourceHtmlUrl":136},"scripts\u002Fdifferential_expression.py",6821,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Fdifferential_expression.py",{"path":138,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":139,"sourceHtmlUrl":140},"scripts\u002Fintegrate_datasets.py",7027,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Fintegrate_datasets.py",{"path":142,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":143,"sourceHtmlUrl":144},"scripts\u002Fmodel_utils.py",17434,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Fmodel_utils.py",{"path":146,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":147,"sourceHtmlUrl":148},"scripts\u002Fprepare_data.py",5218,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Fprepare_data.py",{"path":150,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":151,"sourceHtmlUrl":152},"scripts\u002Ftrain_model.py",11604,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Ftrain_model.py",{"path":154,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":155,"sourceHtmlUrl":156},"scripts\u002Ftransfer_labels.py",7354,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Ftransfer_labels.py",{"path":158,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":159,"sourceHtmlUrl":160},"scripts\u002Fvalidate_adata.py",12211,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Fscvi-tools\u002Fscripts\u002Fvalidate_adata.py",[162,203,227,244,267,300],{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":181,"publishedAt":202},"01M71V1MC994P553631JZSWVYD","solana-dev","Use when user asks to \"build a Solana dapp\", \"write an Anchor program\", \"create a token\", \"debug Solana errors\", \"set up wallet connection\", \"test my Solana program\", \"deploy to devnet\", or \"explain Solana concepts\" (rent, accounts, PDAs, CPIs, etc.). End-to-end Solana development playbook covering wallet connection, Anchor\u002FPinocchio programs, Codama client generation, LiteSVM\u002FMollusk\u002FSurfpool testing, and security checklists. Integrates with the Solana MCP server for live documentation search. Prefers framework-kit (@solana\u002Fclient + @solana\u002Freact-hooks) for UI, wallet-standard-first connection (incl. ConnectorKit), @solana\u002Fkit for client\u002FRPC code, and @solana\u002Fweb3-compat for legacy boundaries.","MIT","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Web3\u002Fsolana-dev\u002Fskill\u002FSKILL.md",[169,172,175,176],{"categoryNo":170,"categoryName":171,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":173,"categoryName":174,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":177,"categoryName":178,"taxonomyType":179,"taxonomyTypeDesc":180},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201],"dev","solana dev","solana","Wallet","search","testing","solana cli","kit","MCP","web3","react","create","server","write","playbook","live","security","ui","concepts","toolchain","2026-11-10 13:02:02",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":209,"publishedAt":202},"01M71TYQPHE2YGKTT7DG9QCW3K","generating-trading-signals","Generate trading signals using technical indicators (RSI, MACD, Bollinger Bands, etc.).\nCombines multiple indicators into composite signals with confidence scores.\nUse when analyzing assets for trading opportunities or checking technical indicators.\nTrigger with phrases like \"get trading signals\", \"check indicators\", \"analyze for entry\",\n\"scan for opportunities\", \"generate buy\u002Fsell signals\", or \"technical analysis\".","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Trading-Finance\u002Fgenerating-trading-signals\u002FSKILL.md",[],[210,211,212,213,214,215,29,216,217,218,219,220,221,41,222,40,223,224,225,226],"generating trading signals","signals","trading signals","trading","get","technical indicators","scan","rsi","trigger","python","technical","use","Bash","code","io","check","analyze",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":6,"sourceHtmlUrl":231,"categories":232,"keywords":233,"publishedAt":202},"01M71TY4CR3GKGQ3M12X9WN95Y","prediction-monitor","Prediction Monitor - Auto-activating skill for ML Deployment.\nTriggers on: prediction monitor, prediction monitor\nPart of the ML Deployment skill category.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Trading-Finance\u002FAI Predictions\u002Fprediction-monitor\u002FSKILL.md",[],[234,235,236,29,237,238,195,239,240,241,242,243,222],"prediction","monitor","prediction monitor","deployment","read","edit","triggers","grep","auto","ml",{"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":166,"licenseFileUrl":6,"sourceHtmlUrl":248,"categories":249,"keywords":250,"publishedAt":202},"01M71TXD9MYXZC39HCPP909HTZ","strategy-advisor","High-level strategic thinking and business decision guidance for planning and direction-setting.\nUse when: making strategic decisions, evaluating business options, setting direction, analyzing\ntrade-offs, or when user mentions strategy, business planning, competitive analysis, or long-term planning.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Fstrategy-advisor\u002FSKILL.md",[],[251,252,253,254,255,256,257,258,259,29,260,261,262,221,263,264,41,265,40,266],"strategy advisor","strategy","advisor","competitive","setting","thinking","planning","direction","trade-offs","long","decision","strategic","business","apps","decisions","trade",{"skillNo":268,"name":269,"nameZh":6,"description":270,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":271,"sourceHtmlUrl":272,"categories":273,"keywords":280,"publishedAt":202},"01M71TWHBT2R1P1FNWTW67ZREP","app-paywall-pilot","Design, audit, debug, and implement App Store-compliant mobile paywalls, subscription flows, pricing screens, trials, offers, feature gates, and subscription lifecycle UX. Use when the user asks to audit my paywall, fix App Store 3.1.2 rejection, improve trial-to-paid, choose plans\u002Fprices\u002Ftrials, review RevenueCat\u002FAdapty\u002FApphud\u002FSuperwall setup, calculate LTV\u002FROAS, or build paywall implementation.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffrom-dosesync\u002Fapp-paywall-pilot\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffrom-dosesync\u002Fapp-paywall-pilot\u002FSKILL.md",[274,277],{"categoryNo":275,"categoryName":276,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":278,"categoryName":279,"taxonomyType":179,"taxonomyTypeDesc":180},"01JBSEEDCAT000000000000032","设计师",[281,282,283,284,285,286,287,288,289,290,291,292,41,293,294,295,296,297,298,299],"app","paywall","app paywall pilot","paid","paywalls","prices","choose","flows","fix","improve","design","app store","review","audit","ux","subscription","mobile","my","calculate",{"skillNo":301,"name":302,"nameZh":6,"description":303,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":304,"sourceHtmlUrl":305,"categories":306,"keywords":309,"publishedAt":202},"01M71TWET8GFKEB7ZHJPMQX29V","figma","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffigma\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffigma\u002FSKILL.md",[307,308],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":278,"categoryName":279,"taxonomyType":179,"taxonomyTypeDesc":180},[302,310,311,312,313,218,291,190,221,41,314,315,223,316,194,317,318,319,320,321],"fetch","ids","mcp setup","translate","mcp server","Variables","task","Nodes","figma mcp","code implementation","node","assets"]