[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DNE5MSQVA5N4RGDFVEP7PW":3,"skill-same:01M6DNE5MSQVA5N4RGDFVEP7PW":77},{"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":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":58,"commitSha":59,"skillDirectory":5,"forksCount":14,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M6DNE5MSQVA5N4RGDFVEP7PW","boltzgen",null,"All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration.\nFor backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.","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\u002Fboltzgen\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"atom","all-atom","diffusion","boltz","binding","binder","sequence-design","structure-design","chain","design","generation","structure","methods","only","use","just","model","yaml","https","2026-11-10 10:12:30",false,"---\nname: boltzgen\ndescription: >\n  All-atom protein design using BoltzGen diffusion model. Use this skill when:\n  (1) Need side-chain aware design from the start,\n  (2) Designing around small molecules or ligands,\n  (3) Want all-atom diffusion (not just backbone),\n  (4) Require precise binding geometries,\n  (5) Using YAML-based configuration.\n\n  For backbone-only generation, use rfdiffusion.\n  For sequence-only design, use proteinmpnn.\n  For structure validation, use boltz.\nlicense: MIT\ncategory: design-tools\ntags: [structure-design, sequence-design, diffusion, all-atom, binder]\nproteinbase_slug: boltzgen\nproteinbase_url: https:\u002F\u002Fproteinbase.com\u002Fdesign-methods\u002Fboltzgen\nbiomodals_script: modal_boltzgen.py\n---\n\n# BoltzGen All-Atom Design\n\n## Prerequisites\n\n| Requirement | Minimum | Recommended |\n|-------------|---------|-------------|\n| Python | 3.10+ | 3.11 |\n| CUDA | 12.0+ | 12.1+ |\n| GPU VRAM | 24GB | 48GB (L40S) |\n| RAM | 32GB | 64GB |\n\n## How to run\n\n> **First time?** See [Installation Guide](..\u002F..\u002Fdocs\u002Finstallation.md) to set up Modal and biomodals.\n\n### Option 1: Modal (recommended)\n```bash\n# Clone biomodals\ngit clone https:\u002F\u002Fgithub.com\u002Fhgbrian\u002Fbiomodals && cd biomodals\n\n# Run BoltzGen (requires YAML config file)\nmodal run modal_boltzgen.py \\\n  --input-yaml binder_config.yaml \\\n  --protocol protein-anything \\\n  --num-designs 50\n\n# With custom GPU\nGPU=L40S modal run modal_boltzgen.py \\\n  --input-yaml binder_config.yaml \\\n  --protocol protein-anything \\\n  --num-designs 100\n```\n\n**GPU**: L40S (48GB) recommended | **Timeout**: 120min default\n\n**Available protocols**: `protein-anything`, `peptide-anything`, `protein-small_molecule`, `nanobody-anything`, `antibody-anything`\n\n### Option 2: Local installation\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHannesStark\u002Fboltzgen.git\ncd boltzgen\npip install -e .\n\npython sample.py config=config.yaml\n```\n\n### Option 3: Python API\n```python\nfrom boltzgen import BoltzGen\n\nmodel = BoltzGen.load_pretrained()\ndesigns = model.sample(\n    target_pdb=\"target.pdb\",\n    num_samples=50,\n    binder_length=80\n)\n```\n\n**GPU**: L40S (48GB) | **Time**: ~30-60s per design\n\n## Key parameters (CLI)\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `--input-yaml` | required | Path to YAML design specification |\n| `--protocol` | `protein-anything` | Design protocol |\n| `--num-designs` | 10 | Number of designs to generate |\n| `--steps` | all | Pipeline steps to run (e.g., `design inverse_folding`) |\n\n## YAML configuration\n\nBoltzGen uses an **entity-based YAML format** where you specify designed proteins and target structures as entities.\n\n**Important notes:**\n- Residue indices use `label_seq_id` (1-indexed), not author residue numbers\n- File paths are relative to the YAML file location\n- Target files should be in CIF format (PDB also works but CIF preferred)\n- Run `boltzgen check config.yaml` to verify your specification before running\n\n### Basic Binder Config\n```yaml\nentities:\n  # Designed protein (variable length 80-140 residues)\n  - protein:\n      id: B\n      sequence: 80..140\n\n  # Target from structure file\n  - file:\n      path: target.cif\n      include:\n        - chain:\n            id: A\n      # Specify binding site residues (optional but recommended)\n      binding_types:\n        - chain:\n            id: A\n            binding: 45,67,89\n```\n\n### Binder with Specific Binding Site\n```yaml\nentities:\n  - protein:\n      id: G\n      sequence: 60..100\n\n  - file:\n      path: 5cqg.cif\n      include:\n        - chain:\n            id: A\n      binding_types:\n        - chain:\n            id: A\n            binding: 343,344,251\n      structure_groups: \"all\"\n```\n\n### Peptide Design (Cyclic)\n```yaml\nentities:\n  - protein:\n      id: S\n      sequence: 10..14C6C3  # With cysteines for disulfide\n\n  - file:\n      path: target.cif\n      include:\n        - chain:\n            id: A\n\nconstraints:\n  - bond:\n      atom1: [S, 11, SG]\n      atom2: [S, 18, SG]  # Disulfide bond\n```\n\n## Design protocols\n\n| Protocol | Use Case |\n|----------|----------|\n| `protein-anything` | Design proteins to bind proteins or peptides |\n| `peptide-anything` | Design cyclic peptides to bind proteins |\n| `protein-small_molecule` | Design proteins to bind small molecules |\n| `nanobody-anything` | Design nanobody CDRs |\n| `antibody-anything` | Design antibody CDRs |\n\n## Output format\n\n```\noutput\u002F\n├── sample_0\u002F\n│   ├── design.cif         # All-atom structure (CIF format)\n│   ├── metrics.json       # Confidence scores\n│   └── sequence.fasta     # Sequence\n├── sample_1\u002F\n│   └── ...\n└── summary.csv\n```\n\n**Note**: BoltzGen outputs CIF format. Convert to PDB if needed:\n```python\nfrom Bio.PDB import MMCIFParser, PDBIO\nparser = MMCIFParser()\nstructure = parser.get_structure(\"design\", \"design.cif\")\nio = PDBIO()\nio.set_structure(structure)\nio.save(\"design.pdb\")\n```\n\n## Sample output\n\n### Successful run\n```\n$ modal run modal_boltzgen.py --input-yaml binder.yaml --protocol protein-anything --num-designs 10\nRunning: boltzgen run binder.yaml --output \u002Ftmp\u002Fout --protocol protein-anything --num_designs 10\n[INFO] Loading BoltzGen model...\n[INFO] Generating designs...\n[INFO] Running inverse folding...\n[INFO] Running structure prediction...\n[INFO] Filtering and ranking...\n[INFO] Pipeline complete\n\nResults saved to: .\u002Fout\u002Fboltzgen\u002F2501161234\u002F\n```\n\n**Output directory structure:**\n```\nout\u002Fboltzgen\u002F2501161234\u002F\n├── intermediate_designs\u002F           # Raw diffusion outputs\n│   ├── design_0.cif\n│   └── design_0.npz\n├── intermediate_designs_inverse_folded\u002F\n│   ├── refold_cif\u002F                 # Refolded complexes\n│   └── aggregate_metrics_analyze.csv\n└── final_ranked_designs\u002F\n    ├── final_10_designs\u002F           # Top designs\n    └── results_overview.pdf        # Summary plots\n```\n\n**What good output looks like:**\n- Refolding RMSD \u003C 2.0A (design folds as predicted)\n- ipTM > 0.5 (confident interface)\n- All designs complete pipeline without errors\n\n## Decision tree\n\n```\nShould I use BoltzGen?\n│\n├─ What type of design?\n│  ├─ All-atom precision needed → BoltzGen ✓\n│  ├─ Ligand binding pocket → BoltzGen ✓\n│  └─ Standard miniprotein → RFdiffusion (faster)\n│\n├─ What matters most?\n│  ├─ Side-chain packing → BoltzGen ✓\n│  ├─ Speed \u002F diversity → RFdiffusion\n│  ├─ Highest success rate → BindCraft\n│  └─ AF2 optimization → ColabDesign\n│\n└─ Compute resources?\n   ├─ Have L40S\u002FA100 (48GB+) → BoltzGen ✓\n   └─ Only A10G (24GB) → Consider RFdiffusion\n```\n\n## Typical performance\n\n| Campaign Size | Time (L40S) | Cost (Modal) | Notes |\n|---------------|-------------|--------------|-------|\n| 50 designs | 30-45 min | ~$8 | Quick exploration |\n| 100 designs | 1-1.5h | ~$15 | Standard campaign |\n| 500 designs | 5-8h | ~$70 | Large campaign |\n\n**Per-design**: ~30-60s for typical binder.\n\n---\n\n## Verify\n\n```bash\nfind output -name \"*.cif\" | wc -l  # Should match num_samples\n```\n\n---\n\n## Troubleshooting\n\n**Verify config first**: Always run `boltzgen check config.yaml` before running the full pipeline\n**Slow generation**: Use fewer designs for initial testing, then scale up\n**OOM errors**: Use A100-80GB or reduce `--num-designs`\n**Wrong binding site**: Residue indices use `label_seq_id` (1-indexed), check in Molstar viewer\n\n### Error interpretation\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `RuntimeError: CUDA out of memory` | Large design or long protein | Use A100-80GB or reduce designs |\n| `FileNotFoundError: *.cif` | Target file not found | File paths are relative to YAML location |\n| `ValueError: invalid chain` | Chain not in target | Verify chain IDs with Molstar or PyMOL |\n| `modal: command not found` | Modal CLI not installed | Run `pip install modal && modal setup` |\n\n---\n\n**Next**: Validate with `boltz` or `chai` → `protein-qc` for filtering.\n","# BoltzGen All-Atom Design\n\n## Prerequisites\n\n| Requirement | Minimum | Recommended |\n|-------------|---------|-------------|\n| Python | 3.10+ | 3.11 |\n| CUDA | 12.0+ | 12.1+ |\n| GPU VRAM | 24GB | 48GB (L40S) |\n| RAM | 32GB | 64GB |\n\n## How to run\n\n> **First time?** See [Installation Guide](..\u002F..\u002Fdocs\u002Finstallation.md) to set up Modal and biomodals.\n\n### Option 1: Modal (recommended)\n```bash\n# Clone biomodals\ngit clone https:\u002F\u002Fgithub.com\u002Fhgbrian\u002Fbiomodals && cd biomodals\n\n# Run BoltzGen (requires YAML config file)\nmodal run modal_boltzgen.py \\\n  --input-yaml binder_config.yaml \\\n  --protocol protein-anything \\\n  --num-designs 50\n\n# With custom GPU\nGPU=L40S modal run modal_boltzgen.py \\\n  --input-yaml binder_config.yaml \\\n  --protocol protein-anything \\\n  --num-designs 100\n```\n\n**GPU**: L40S (48GB) recommended | **Timeout**: 120min default\n\n**Available protocols**: `protein-anything`, `peptide-anything`, `protein-small_molecule`, `nanobody-anything`, `antibody-anything`\n\n### Option 2: Local installation\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHannesStark\u002Fboltzgen.git\ncd boltzgen\npip install -e .\n\npython sample.py config=config.yaml\n```\n\n### Option 3: Python API\n```python\nfrom boltzgen import BoltzGen\n\nmodel = BoltzGen.load_pretrained()\ndesigns = model.sample(\n    target_pdb=\"target.pdb\",\n    num_samples=50,\n    binder_length=80\n)\n```\n\n**GPU**: L40S (48GB) | **Time**: ~30-60s per design\n\n## Key parameters (CLI)\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `--input-yaml` | required | Path to YAML design specification |\n| `--protocol` | `protein-anything` | Design protocol |\n| `--num-designs` | 10 | Number of designs to generate |\n| `--steps` | all | Pipeline steps to run (e.g., `design inverse_folding`) |\n\n## YAML configuration\n\nBoltzGen uses an **entity-based YAML format** where you specify designed proteins and target structures as entities.\n\n**Important notes:**\n- Residue indices use `label_seq_id` (1-indexed), not author residue numbers\n- File paths are relative to the YAML file location\n- Target files should be in CIF format (PDB also works but CIF preferred)\n- Run `boltzgen check config.yaml` to verify your specification before running\n\n### Basic Binder Config\n```yaml\nentities:\n  # Designed protein (variable length 80-140 residues)\n  - protein:\n      id: B\n      sequence: 80..140\n\n  # Target from structure file\n  - file:\n      path: target.cif\n      include:\n        - chain:\n            id: A\n      # Specify binding site residues (optional but recommended)\n      binding_types:\n        - chain:\n            id: A\n            binding: 45,67,89\n```\n\n### Binder with Specific Binding Site\n```yaml\nentities:\n  - protein:\n      id: G\n      sequence: 60..100\n\n  - file:\n      path: 5cqg.cif\n      include:\n        - chain:\n            id: A\n      binding_types:\n        - chain:\n            id: A\n            binding: 343,344,251\n      structure_groups: \"all\"\n```\n\n### Peptide Design (Cyclic)\n```yaml\nentities:\n  - protein:\n      id: S\n      sequence: 10..14C6C3  # With cysteines for disulfide\n\n  - file:\n      path: target.cif\n      include:\n        - chain:\n            id: A\n\nconstraints:\n  - bond:\n      atom1: [S, 11, SG]\n      atom2: [S, 18, SG]  # Disulfide bond\n```\n\n## Design protocols\n\n| Protocol | Use Case |\n|----------|----------|\n| `protein-anything` | Design proteins to bind proteins or peptides |\n| `peptide-anything` | Design cyclic peptides to bind proteins |\n| `protein-small_molecule` | Design proteins to bind small molecules |\n| `nanobody-anything` | Design nanobody CDRs |\n| `antibody-anything` | Design antibody CDRs |\n\n## Output format\n\n```\noutput\u002F\n├── sample_0\u002F\n│   ├── design.cif         # All-atom structure (CIF format)\n│   ├── metrics.json       # Confidence scores\n│   └── sequence.fasta     # Sequence\n├── sample_1\u002F\n│   └── ...\n└── summary.csv\n```\n\n**Note**: BoltzGen outputs CIF format. Convert to PDB if needed:\n```python\nfrom Bio.PDB import MMCIFParser, PDBIO\nparser = MMCIFParser()\nstructure = parser.get_structure(\"design\", \"design.cif\")\nio = PDBIO()\nio.set_structure(structure)\nio.save(\"design.pdb\")\n```\n\n## Sample output\n\n### Successful run\n```\n$ modal run modal_boltzgen.py --input-yaml binder.yaml --protocol protein-anything --num-designs 10\nRunning: boltzgen run binder.yaml --output \u002Ftmp\u002Fout --protocol protein-anything --num_designs 10\n[INFO] Loading BoltzGen model...\n[INFO] Generating designs...\n[INFO] Running inverse folding...\n[INFO] Running structure prediction...\n[INFO] Filtering and ranking...\n[INFO] Pipeline complete\n\nResults saved to: .\u002Fout\u002Fboltzgen\u002F2501161234\u002F\n```\n\n**Output directory structure:**\n```\nout\u002Fboltzgen\u002F2501161234\u002F\n├── intermediate_designs\u002F           # Raw diffusion outputs\n│   ├── design_0.cif\n│   └── design_0.npz\n├── intermediate_designs_inverse_folded\u002F\n│   ├── refold_cif\u002F                 # Refolded complexes\n│   └── aggregate_metrics_analyze.csv\n└── final_ranked_designs\u002F\n    ├── final_10_designs\u002F           # Top designs\n    └── results_overview.pdf        # Summary plots\n```\n\n**What good output looks like:**\n- Refolding RMSD \u003C 2.0A (design folds as predicted)\n- ipTM > 0.5 (confident interface)\n- All designs complete pipeline without errors\n\n## Decision tree\n\n```\nShould I use BoltzGen?\n│\n├─ What type of design?\n│  ├─ All-atom precision needed → BoltzGen ✓\n│  ├─ Ligand binding pocket → BoltzGen ✓\n│  └─ Standard miniprotein → RFdiffusion (faster)\n│\n├─ What matters most?\n│  ├─ Side-chain packing → BoltzGen ✓\n│  ├─ Speed \u002F diversity → RFdiffusion\n│  ├─ Highest success rate → BindCraft\n│  └─ AF2 optimization → ColabDesign\n│\n└─ Compute resources?\n   ├─ Have L40S\u002FA100 (48GB+) → BoltzGen ✓\n   └─ Only A10G (24GB) → Consider RFdiffusion\n```\n\n## Typical performance\n\n| Campaign Size | Time (L40S) | Cost (Modal) | Notes |\n|---------------|-------------|--------------|-------|\n| 50 designs | 30-45 min | ~$8 | Quick exploration |\n| 100 designs | 1-1.5h | ~$15 | Standard campaign |\n| 500 designs | 5-8h | ~$70 | Large campaign |\n\n**Per-design**: ~30-60s for typical binder.\n\n---\n\n## Verify\n\n```bash\nfind output -name \"*.cif\" | wc -l  # Should match num_samples\n```\n\n---\n\n## Troubleshooting\n\n**Verify config first**: Always run `boltzgen check config.yaml` before running the full pipeline\n**Slow generation**: Use fewer designs for initial testing, then scale up\n**OOM errors**: Use A100-80GB or reduce `--num-designs`\n**Wrong binding site**: Residue indices use `label_seq_id` (1-indexed), check in Molstar viewer\n\n### Error interpretation\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| `RuntimeError: CUDA out of memory` | Large design or long protein | Use A100-80GB or reduce designs |\n| `FileNotFoundError: *.cif` | Target file not found | File paths are relative to YAML location |\n| `ValueError: invalid chain` | Chain not in target | Verify chain IDs with Molstar or PyMOL |\n| `modal: command not found` | Modal CLI not installed | Run `pip install modal && modal setup` |\n\n---\n\n**Next**: Validate with `boltz` or `chai` → `protein-qc` for filtering.\n",{"name":5,"tags":53,"license":15,"category":54,"description":55,"proteinbase_url":56,"biomodals_script":57,"proteinbase_slug":5},[36,35,31,30,34],"design-tools","All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration.\nFor backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.\n","https:\u002F\u002Fproteinbase.com\u002Fdesign-methods\u002Fboltzgen","modal_boltzgen.py","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":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add huamu668\u002Fclawhub-openclaw-medical --skill boltzgen",{"tool":69,"command":70},"bunx","bunx skills add huamu668\u002Fclawhub-openclaw-medical --skill boltzgen",{"tool":72,"command":73},"pnpm","pnpm dlx skills add huamu668\u002Fclawhub-openclaw-medical --skill boltzgen","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhuamu668\u002Fclawhub-openclaw-medical\u002Fblob\u002Fmain\u002Fboltzgen\u002FSKILL.md",true,[],[78,98,126,146,160,181],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":48},"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",[],[85,86,87,88,89,90,91,92,93,94,95,96,97],"python","zarr python","storage","scientific computing","numpy","arrays","pipelines","parallel","dask","cloud","gcs","s3","integration",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":107,"publishedAt":48},"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",[104],{"categoryNo":105,"categoryName":106,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",[100,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,43,124,125],"machine learning","in memory","learning","csv","visualization","create","core","ml","big data","memory","visualizations","fit","statistics","do","build","files","operations","work",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":48},"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",[],[133,110,134,135,136,137,138,139,140,141,142,143,144,145],"torch geometric","node","Neural","link","gat","molecular","graphs","deep","networks","graph","neural networks","deep learning","classification",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":48},"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",[],[148,153,154,155,156,157,158,92,159,87],"data","genomics","sample","queries","ingestion","export","capabilities",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":168,"publishedAt":48},"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",[166,167],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[162,169,170,43,171,172,173,38,174,175,176,177,178,179,180,35],"inclusion","yield","modal","Tools","aggregation","proteins","solubility","proteinmpnn","ligandmpnn","ligand-aware","inverse-folding","expression",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":185,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":188,"publishedAt":48},"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",[],[183,189,190,191,192,193,194,195,196,197,198,172,153,199,200],"RNA","single-cell","infer","cell","transitions","time","compute","velocity","scanpy","analysis","state","inference"]