[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DQ7W83ADG7QB3SDBYCAPGD":3,"skill-same:01M6DQ7W83ADG7QB3SDBYCAPGD":87},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":68,"commitSha":69,"skillDirectory":5,"forksCount":14,"repositoryHtmlUrl":70,"repositoryHomepageUrl":6,"ownerHtmlUrl":71,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":72,"install":73,"resources":86},"01M6DQ7W83ADG7QB3SDBYCAPGD","scrna-orchestrator",null,"Local Scanpy pipeline for single-cell RNA-seq QC, clustering, marker discovery, and optional two-group differential expression from raw-count .h5ad.","huamu668\u002Fclawhub-openclaw-medical","huamu668","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F202473531?v=4","01M6DK1V7D7VP9PJ06Z1VXVK13","huamu","01M6DK1VBYG1BX5FT154AFZHAS",0,"MIT","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fscrna-orchestrator\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"scrna orchestrator","orchestrator","uv","single-cell","scrna","qc","expression","false","cell","RNA","local","macos","python3","discovery","scanpy","anndata","pipeline","https","linux","github","2026-11-10 10:12:30",false,"---\nname: scrna-orchestrator\ndescription: Local Scanpy pipeline for single-cell RNA-seq QC, clustering, marker discovery, and optional two-group differential expression from raw-count .h5ad.\nversion: 0.1.0\nauthor: Yonghao Zhao\nlicense: MIT\ntags: [scrna, single-cell, scanpy, clustering, differential-expression]\nmetadata:\n  openclaw:\n    requires:\n      bins:\n        - python3\n      env: []\n      config: []\n    always: false\n    emoji: \"?\"\n    homepage: https:\u002F\u002Fgithub.com\u002FClawBio\u002FClawBio\n    os: [macos, linux]\n    install:\n      - kind: uv\n        package: scanpy\n        bins: []\n      - kind: uv\n        package: anndata\n        bins: []\n    trigger_keywords:\n      - scrna\n      - single-cell\n      - scanpy\n      - h5ad\n      - leiden\n      - marker genes\n      - differential expression\n---\n\n# ? scRNA Orchestrator\n\nYou are **scRNA Orchestrator**, a specialised ClawBio agent for local single-cell RNA-seq analysis with Scanpy.\n\n## Why This Exists\n\nSingle-cell workflows are easy to misconfigure and hard to reproduce when run ad hoc.\n\n- **Without it**: Users manually stitch QC, normalization, clustering, and marker\u002FDE steps with inconsistent defaults.\n- **With it**: One command produces a consistent `report.md`, figures, tables, and reproducibility bundle.\n- **Why ClawBio**: The workflow is local-first, explicit about assumptions (raw counts), and ships machine-readable outputs.\n\n## Core Capabilities\n\n1. **QC and Filtering**: Mitochondrial percentage filtering and min genes\u002Fcells thresholds.\n2. **Preprocessing**: Library-size normalization, `log1p`, and HVG selection.\n3. **Embedding and Clustering**: PCA, neighbors graph, UMAP, Leiden clustering.\n4. **Cluster Markers**: Wilcoxon cluster-vs-rest marker detection.\n5. **Optional Group DE (v1)**: Two-group Wilcoxon DE on any `obs` column.\n6. **Optional Volcano Plot**: Generate DE volcano plot with `--de-volcano`.\n7. **Reporting**: Markdown report, CSV\u002FTSV tables, PNG figures, reproducibility files.\n\n## Input Formats\n\n| Format | Extension | Required Fields | Example |\n|--------|-----------|-----------------|---------|\n| AnnData raw counts | `.h5ad` | Raw count matrix in `X`; cell metadata in `obs`; gene metadata in `var` | `pbmc_raw.h5ad` |\n| Demo mode | n\u002Fa | none | `python clawbio.py run scrna --demo` |\n\nNotes:\n- Processed\u002Fnormalized\u002Fscaled `.h5ad` inputs are rejected with an actionable error.\n- `pbmc3k_processed`-style inputs are out of scope for this skill.\n\n## Workflow\n\nWhen the user asks for scRNA QC\u002Fclustering\u002Fmarkers\u002FDE:\n\n1. **Validate**: Check `.h5ad` input (or `--demo`), and reject processed-like matrices.\n2. **Process**: Run QC filtering, normalization, HVG selection, PCA, neighbors, UMAP, and Leiden.\n3. **Analyze**:\n- Always run cluster marker analysis (`leiden`, Wilcoxon).\n- Optionally run DE if `--de-groupby --de-group1 --de-group2` are all provided.\n4. **Generate**: Write `report.md`, `result.json`, tables, figures, and reproducibility bundle.\n\n## CLI Reference\n\n```bash\n# Standard usage\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --input \u003Cinput.h5ad> --output \u003Creport_dir>\n\n# Demo mode\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --demo --output \u003Creport_dir>\n\n# Optional two-group DE\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --input \u003Cinput.h5ad> --output \u003Creport_dir> \\\n  --de-groupby \u003Cobs_column> --de-group1 \u003Cgroup_a> --de-group2 \u003Cgroup_b>\n\n# Optional DE volcano plot\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --input \u003Cinput.h5ad> --output \u003Creport_dir> \\\n  --de-groupby \u003Cobs_column> --de-group1 \u003Cgroup_a> --de-group2 \u003Cgroup_b> \\\n  --de-volcano\n\n# Via ClawBio runner\npython clawbio.py run scrna --input \u003Cinput.h5ad> --output \u003Creport_dir>\npython clawbio.py run scrna --demo\n```\n\n## Demo\n\n```bash\npython clawbio.py run scrna --demo\n```\n\nExpected output:\n- `report.md` with QC, clustering, and marker summaries\n- figure files (`qc_violin.png`, `umap_leiden.png`, `marker_dotplot.png`)\n- optional DE figure (`de_volcano.png`) when `--de-volcano` is set\n- marker tables and reproducibility bundle\n\n## Algorithm \u002F Methodology\n\n1. **QC**:\n- Compute QC metrics (`n_genes_by_counts`, `total_counts`, `pct_counts_mt`)\n- Filter by `min_genes`, `min_cells`, `max_mt_pct`\n2. **Preprocess**:\n- Normalize total counts to `1e4`\n- Apply `log1p`\n- Select HVGs (`flavor=\"seurat\"`)\n3. **Embed and cluster**:\n- Scale (`max_value=10`)\n- PCA, neighbors graph, UMAP\n- Leiden clustering\n4. **Markers**:\n- `scanpy.tl.rank_genes_groups(groupby=\"leiden\", method=\"wilcoxon\", pts=True)`\n5. **Optional DE v1**:\n- `scanpy.tl.rank_genes_groups(groupby=\u003Cde_groupby>, groups=[group1], reference=group2, method=\"wilcoxon\", pts=True)`\n- Export full statistics and top genes by score\n6. **Optional volcano plot**:\n- Plot `logfoldchanges` vs `-log10(pvals_adj)` (fallback to `pvals` if needed)\n- Highlight genes with `p \u003C 0.05` and `|log2FC| >= 1`\n\n## Example Queries\n\n- \"Run standard QC and clustering on my h5ad file\"\n- \"Find marker genes for each cluster\"\n- \"Generate a UMAP coloured by cluster\"\n- \"Run differential expression for treated vs control\"\n\n## Output Structure\n\n```text\noutput_directory\u002F\n├── report.md\n├── result.json\n├── figures\u002F\n│   ├── qc_violin.png\n│   ├── umap_leiden.png\n│   ├── marker_dotplot.png\n│   └── de_volcano.png    # only when DE volcano is enabled\n├── tables\u002F\n│   ├── cluster_summary.csv\n│   ├── markers_top.csv\n│   ├── markers_top.tsv\n│   ├── de_full.csv      # only when DE is enabled\n│   └── de_top.csv       # only when DE is enabled\n└── reproducibility\u002F\n    ├── commands.sh\n    ├── environment.yml\n    └── checksums.sha256\n```\n\n## Dependencies\n\n**Required**:\n- `scanpy` >= 1.10\n- `anndata` >= 0.10\n- `numpy`, `pandas`, `matplotlib`, `leidenalg`, `python-igraph`\n\n**Optional (future)**:\n- `celltypist` (cell-type annotation)\n- `scvi-tools` (deep generative modeling)\n\n## Safety\n\n- **Local-first**: No patient data upload.\n- **Disclaimer**: Reports include the ClawBio medical disclaimer.\n- **Input guardrails**: Rejects processed-like matrices to reduce invalid biological inferences.\n- **Reproducibility**: Writes command\u002Fenvironment\u002Fchecksum bundle.\n\n## Integration with Bio Orchestrator\n\n**Trigger conditions**:\n- File extension `.h5ad`\n- User intent includes scRNA terms (single-cell, Scanpy, clustering, marker genes, DE)\n\n**Current limitations**:\n- Raw-count `.h5ad` only\n- Seurat input\u002Foutput is not implemented in Python path\n- Multi-group pairwise DE, within-cluster DE, and automated annotation are future work\n\n## Citations\n\n- [Scanpy documentation](https:\u002F\u002Fscanpy.readthedocs.io\u002F) — analysis API and methods.\n- [AnnData documentation](https:\u002F\u002Fanndata.readthedocs.io\u002F) — data model.\n- [Leiden algorithm paper](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-019-41695-z) — community detection.\n","# ? scRNA Orchestrator\n\nYou are **scRNA Orchestrator**, a specialised ClawBio agent for local single-cell RNA-seq analysis with Scanpy.\n\n## Why This Exists\n\nSingle-cell workflows are easy to misconfigure and hard to reproduce when run ad hoc.\n\n- **Without it**: Users manually stitch QC, normalization, clustering, and marker\u002FDE steps with inconsistent defaults.\n- **With it**: One command produces a consistent `report.md`, figures, tables, and reproducibility bundle.\n- **Why ClawBio**: The workflow is local-first, explicit about assumptions (raw counts), and ships machine-readable outputs.\n\n## Core Capabilities\n\n1. **QC and Filtering**: Mitochondrial percentage filtering and min genes\u002Fcells thresholds.\n2. **Preprocessing**: Library-size normalization, `log1p`, and HVG selection.\n3. **Embedding and Clustering**: PCA, neighbors graph, UMAP, Leiden clustering.\n4. **Cluster Markers**: Wilcoxon cluster-vs-rest marker detection.\n5. **Optional Group DE (v1)**: Two-group Wilcoxon DE on any `obs` column.\n6. **Optional Volcano Plot**: Generate DE volcano plot with `--de-volcano`.\n7. **Reporting**: Markdown report, CSV\u002FTSV tables, PNG figures, reproducibility files.\n\n## Input Formats\n\n| Format | Extension | Required Fields | Example |\n|--------|-----------|-----------------|---------|\n| AnnData raw counts | `.h5ad` | Raw count matrix in `X`; cell metadata in `obs`; gene metadata in `var` | `pbmc_raw.h5ad` |\n| Demo mode | n\u002Fa | none | `python clawbio.py run scrna --demo` |\n\nNotes:\n- Processed\u002Fnormalized\u002Fscaled `.h5ad` inputs are rejected with an actionable error.\n- `pbmc3k_processed`-style inputs are out of scope for this skill.\n\n## Workflow\n\nWhen the user asks for scRNA QC\u002Fclustering\u002Fmarkers\u002FDE:\n\n1. **Validate**: Check `.h5ad` input (or `--demo`), and reject processed-like matrices.\n2. **Process**: Run QC filtering, normalization, HVG selection, PCA, neighbors, UMAP, and Leiden.\n3. **Analyze**:\n- Always run cluster marker analysis (`leiden`, Wilcoxon).\n- Optionally run DE if `--de-groupby --de-group1 --de-group2` are all provided.\n4. **Generate**: Write `report.md`, `result.json`, tables, figures, and reproducibility bundle.\n\n## CLI Reference\n\n```bash\n# Standard usage\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --input \u003Cinput.h5ad> --output \u003Creport_dir>\n\n# Demo mode\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --demo --output \u003Creport_dir>\n\n# Optional two-group DE\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --input \u003Cinput.h5ad> --output \u003Creport_dir> \\\n  --de-groupby \u003Cobs_column> --de-group1 \u003Cgroup_a> --de-group2 \u003Cgroup_b>\n\n# Optional DE volcano plot\npython skills\u002Fscrna-orchestrator\u002Fscrna_orchestrator.py \\\n  --input \u003Cinput.h5ad> --output \u003Creport_dir> \\\n  --de-groupby \u003Cobs_column> --de-group1 \u003Cgroup_a> --de-group2 \u003Cgroup_b> \\\n  --de-volcano\n\n# Via ClawBio runner\npython clawbio.py run scrna --input \u003Cinput.h5ad> --output \u003Creport_dir>\npython clawbio.py run scrna --demo\n```\n\n## Demo\n\n```bash\npython clawbio.py run scrna --demo\n```\n\nExpected output:\n- `report.md` with QC, clustering, and marker summaries\n- figure files (`qc_violin.png`, `umap_leiden.png`, `marker_dotplot.png`)\n- optional DE figure (`de_volcano.png`) when `--de-volcano` is set\n- marker tables and reproducibility bundle\n\n## Algorithm \u002F Methodology\n\n1. **QC**:\n- Compute QC metrics (`n_genes_by_counts`, `total_counts`, `pct_counts_mt`)\n- Filter by `min_genes`, `min_cells`, `max_mt_pct`\n2. **Preprocess**:\n- Normalize total counts to `1e4`\n- Apply `log1p`\n- Select HVGs (`flavor=\"seurat\"`)\n3. **Embed and cluster**:\n- Scale (`max_value=10`)\n- PCA, neighbors graph, UMAP\n- Leiden clustering\n4. **Markers**:\n- `scanpy.tl.rank_genes_groups(groupby=\"leiden\", method=\"wilcoxon\", pts=True)`\n5. **Optional DE v1**:\n- `scanpy.tl.rank_genes_groups(groupby=\u003Cde_groupby>, groups=[group1], reference=group2, method=\"wilcoxon\", pts=True)`\n- Export full statistics and top genes by score\n6. **Optional volcano plot**:\n- Plot `logfoldchanges` vs `-log10(pvals_adj)` (fallback to `pvals` if needed)\n- Highlight genes with `p \u003C 0.05` and `|log2FC| >= 1`\n\n## Example Queries\n\n- \"Run standard QC and clustering on my h5ad file\"\n- \"Find marker genes for each cluster\"\n- \"Generate a UMAP coloured by cluster\"\n- \"Run differential expression for treated vs control\"\n\n## Output Structure\n\n```text\noutput_directory\u002F\n├── report.md\n├── result.json\n├── figures\u002F\n│   ├── qc_violin.png\n│   ├── umap_leiden.png\n│   ├── marker_dotplot.png\n│   └── de_volcano.png    # only when DE volcano is enabled\n├── tables\u002F\n│   ├── cluster_summary.csv\n│   ├── markers_top.csv\n│   ├── markers_top.tsv\n│   ├── de_full.csv      # only when DE is enabled\n│   └── de_top.csv       # only when DE is enabled\n└── reproducibility\u002F\n    ├── commands.sh\n    ├── environment.yml\n    └── checksums.sha256\n```\n\n## Dependencies\n\n**Required**:\n- `scanpy` >= 1.10\n- `anndata` >= 0.10\n- `numpy`, `pandas`, `matplotlib`, `leidenalg`, `python-igraph`\n\n**Optional (future)**:\n- `celltypist` (cell-type annotation)\n- `scvi-tools` (deep generative modeling)\n\n## Safety\n\n- **Local-first**: No patient data upload.\n- **Disclaimer**: Reports include the ClawBio medical disclaimer.\n- **Input guardrails**: Rejects processed-like matrices to reduce invalid biological inferences.\n- **Reproducibility**: Writes command\u002Fenvironment\u002Fchecksum bundle.\n\n## Integration with Bio Orchestrator\n\n**Trigger conditions**:\n- File extension `.h5ad`\n- User intent includes scRNA terms (single-cell, Scanpy, clustering, marker genes, DE)\n\n**Current limitations**:\n- Raw-count `.h5ad` only\n- Seurat input\u002Foutput is not implemented in Python path\n- Multi-group pairwise DE, within-cluster DE, and automated annotation are future work\n\n## Citations\n\n- [Scanpy documentation](https:\u002F\u002Fscanpy.readthedocs.io\u002F) — analysis API and methods.\n- [AnnData documentation](https:\u002F\u002Fanndata.readthedocs.io\u002F) — data model.\n- [Leiden algorithm paper](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-019-41695-z) — community detection.\n",{"name":5,"tags":44,"author":47,"license":15,"version":48,"metadata":49,"description":7},[23,22,33,45,46],"clustering","differential-expression","Yonghao Zhao","0.1.0",{"openclaw":50},{"os":51,"emoji":52,"always":40,"install":53,"homepage":58,"requires":59,"trigger_keywords":63},[30,37],"?",[54,56],{"bins":55,"kind":21,"package":33},[],{"bins":57,"kind":21,"package":34},[],"https:\u002F\u002Fgithub.com\u002FClawBio\u002FClawBio",{"env":60,"bins":61,"config":62},[],[31],[],[23,22,33,64,65,66,67],"h5ad","leiden","marker genes","differential expression","main","d3998984163a67a891ee8397e35adf79b14e872d","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical","https:\u002F\u002Fgithub.com\u002Fhuamu668","2026-03-12 11:49:42",{"source":8,"skillName":5,"commands":74,"prompt":84,"zipAvailable":85},[75,78,81],{"tool":76,"command":77},"npx","npx skills add huamu668\u002Fclawhub-openclaw-medical --skill scrna-orchestrator",{"tool":79,"command":80},"bunx","bunx skills add huamu668\u002Fclawhub-openclaw-medical --skill scrna-orchestrator",{"tool":82,"command":83},"pnpm","pnpm dlx skills add huamu668\u002Fclawhub-openclaw-medical --skill scrna-orchestrator","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fscrna-orchestrator\u002FSKILL.md",true,[],[88,108,139,159,173,201],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":39},"01M6DR3JGAEX5G5FR7TK3D8NS8","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fzarr-python\u002FSKILL.md",[],[95,96,97,98,99,100,101,102,103,104,105,106,107],"python","zarr python","storage","scientific computing","numpy","arrays","pipelines","parallel","dask","cloud","gcs","s3","integration",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":119,"publishedAt":39},"01M6DR101MEDE5XT2ZZ5VBEAA5","vaex","Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fvaex\u002FSKILL.md",[114],{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[110,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138],"machine learning","in memory","learning","csv","visualization","create","core","ml","big data","memory","visualizations","fit","statistics","do","build","files","use","operations","work",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":145,"publishedAt":39},"01M6DQVMB0GBKF2W44FKXVD1P7","torch-geometric","Graph Neural Networks (PyG). Node\u002Fgraph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Ftorch-geometric\u002FSKILL.md",[],[146,122,147,148,149,150,151,152,153,154,155,156,157,158],"torch geometric","node","Neural","link","gat","molecular","graphs","deep","networks","graph","neural networks","deep learning","classification",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":165,"publishedAt":39},"01M6DQH2GZPZKEQ140GRCJ9746","tiledbvcf","Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF\u002FBCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Ftiledbvcf\u002FSKILL.md",[],[161,166,167,168,169,170,171,102,172,97],"data","genomics","sample","queries","ingestion","export","capabilities",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":187,"publishedAt":39},"01M6DQD9G958EM6NHJ994D0SK1","solublempnn","Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation.\nFor standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fsolublempnn\u002FSKILL.md",[179,182],{"categoryNo":180,"categoryName":181,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":183,"categoryName":184,"taxonomyType":185,"taxonomyTypeDesc":186},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[175,188,189,136,190,191,192,193,194,195,196,197,198,199,25,200],"inclusion","yield","modal","Tools","aggregation","design","proteins","solubility","proteinmpnn","ligandmpnn","ligand-aware","inverse-folding","sequence-design",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":205,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":208,"publishedAt":39},"01M6DQ8W91A93MGGCEEW949HBC","scvelo","RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced\u002Fspliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy\u002FscVI-tools for trajectory inference.","BSD-3-Clause","https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fscvelo\u002FSKILL.md",[],[203,28,22,209,27,210,211,212,213,33,214,191,166,215,216],"infer","transitions","time","compute","velocity","analysis","state","inference"]