[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ECA4Z3M1TYSEE5FSHDY8HB":3,"skill-same:01M6ECA4Z3M1TYSEE5FSHDY8HB":51},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":20,"frontMatter":21,"refName":22,"commitSha":23,"skillDirectory":24,"forksCount":25,"repositoryHtmlUrl":26,"repositoryHomepageUrl":27,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":29,"updatedAt":30,"install":31,"resources":44},"01M6ECA4Z3M1TYSEE5FSHDY8HB","lamindb",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Flamindb\u002FSKILL.md",[],[5],"2026-11-10 10:12:30",false,"# LaminDB\n\n## Overview\n\nLaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API.\n\n**Core Value Proposition:**\n- **Queryability**: Search and filter datasets by metadata, features, and ontology terms\n- **Traceability**: Automatic lineage tracking from raw data through analysis to results\n- **Reproducibility**: Version control for data, code, and environment\n- **FAIR Compliance**: Standardized annotations using biological ontologies\n\n## When to Use This Skill\n\nUse this skill when:\n\n- **Managing biological datasets**: scRNA-seq, bulk RNA-seq, spatial transcriptomics, flow cytometry, multi-modal data, EHR data\n- **Tracking computational workflows**: Notebooks, scripts, pipeline execution (Nextflow, Snakemake, Redun)\n- **Curating and validating data**: Schema validation, standardization, ontology-based annotation\n- **Working with biological ontologies**: Genes, proteins, cell types, tissues, diseases, pathways (via Bionty)\n- **Building data lakehouses**: Unified query interface across multiple datasets\n- **Ensuring reproducibility**: Automatic versioning, lineage tracking, environment capture\n- **Integrating ML pipelines**: Connecting with Weights & Biases, MLflow, HuggingFace, scVI-tools\n- **Deploying data infrastructure**: Setting up local or cloud-based data management systems\n- **Collaborating on datasets**: Sharing curated, annotated data with standardized metadata\n\n## Core Capabilities\n\nLaminDB provides six interconnected capability areas, each documented in detail in the references folder.\n\n### 1. Core Concepts and Data Lineage\n\n**Core entities:**\n- **Artifacts**: Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.)\n- **Records**: Experimental entities (samples, perturbations, instruments)\n- **Runs & Transforms**: Computational lineage tracking (what code produced what data)\n- **Features**: Typed metadata fields for annotation and querying\n\n**Key workflows:**\n- Create and version artifacts from files or Python objects\n- Track notebook\u002Fscript execution with `ln.track()` and `ln.finish()`\n- Annotate artifacts with typed features\n- Visualize data lineage graphs with `artifact.view_lineage()`\n- Query by provenance (find all outputs from specific code\u002Finputs)\n\n**Reference:** `references\u002Fcore-concepts.md` - Read this for detailed information on artifacts, records, runs, transforms, features, versioning, and lineage tracking.\n\n### 2. Data Management and Querying\n\n**Query capabilities:**\n- Registry exploration and lookup with auto-complete\n- Single record retrieval with `get()`, `one()`, `one_or_none()`\n- Filtering with comparison operators (`__gt`, `__lte`, `__contains`, `__startswith`)\n- Feature-based queries (query by annotated metadata)\n- Cross-registry traversal with double-underscore syntax\n- Full-text search across registries\n- Advanced logical queries with Q objects (AND, OR, NOT)\n- Streaming large datasets without loading into memory\n\n**Key workflows:**\n- Browse artifacts with filters and ordering\n- Query by features, creation date, creator, size, etc.\n- Stream large files in chunks or with array slicing\n- Organize data with hierarchical keys\n- Group artifacts into collections\n\n**Reference:** `references\u002Fdata-management.md` - Read this for comprehensive query patterns, filtering examples, streaming strategies, and data organization best practices.\n\n### 3. Annotation and Validation\n\n**Curation process:**\n1. **Validation**: Confirm datasets match desired schemas\n2. **Standardization**: Fix typos, map synonyms to canonical terms\n3. **Annotation**: Link datasets to metadata entities for queryability\n\n**Schema types:**\n- **Flexible schemas**: Validate only known columns, allow additional metadata\n- **Minimal required schemas**: Specify essential columns, permit extras\n- **Strict schemas**: Complete control over structure and values\n\n**Supported data types:**\n- DataFrames (Parquet, CSV)\n- AnnData (single-cell genomics)\n- MuData (multi-modal)\n- SpatialData (spatial transcriptomics)\n- TileDB-SOMA (scalable arrays)\n\n**Key workflows:**\n- Define features and schemas for data validation\n- Use `DataFrameCurator` or `AnnDataCurator` for validation\n- Standardize values with `.cat.standardize()`\n- Map to ontologies with `.cat.add_ontology()`\n- Save curated artifacts with schema linkage\n- Query validated datasets by features\n\n**Reference:** `references\u002Fannotation-validation.md` - Read this for detailed curation workflows, schema design patterns, handling validation errors, and best practices.\n\n### 4. Biological Ontologies\n\n**Available ontologies (via Bionty):**\n- Genes (Ensembl), Proteins (UniProt)\n- Cell types (CL), Cell lines (CLO)\n- Tissues (Uberon), Diseases (Mondo, DOID)\n- Phenotypes (HPO), Pathways (GO)\n- Experimental factors (EFO), Developmental stages\n- Organisms (NCBItaxon), Drugs (DrugBank)\n\n**Key workflows:**\n- Import public ontologies with `bt.CellType.import_source()`\n- Search ontologies with keyword or exact matching\n- Standardize terms using synonym mapping\n- Explore hierarchical relationships (parents, children, ancestors)\n- Validate data against ontology terms\n- Annotate datasets with ontology records\n- Create custom terms and hierarchies\n- Handle multi-organism contexts (human, mouse, etc.)\n\n**Reference:** `references\u002Fontologies.md` - Read this for comprehensive ontology operations, standardization strategies, hierarchy navigation, and annotation workflows.\n\n### 5. Integrations\n\n**Workflow managers:**\n- Nextflow: Track pipeline processes and outputs\n- Snakemake: Integrate into Snakemake rules\n- Redun: Combine with Redun task tracking\n\n**MLOps platforms:**\n- Weights & Biases: Link experiments with data artifacts\n- MLflow: Track models and experiments\n- HuggingFace: Track model fine-tuning\n- scVI-tools: Single-cell analysis workflows\n\n**Storage systems:**\n- Local filesystem, AWS S3, Google Cloud Storage\n- S3-compatible (MinIO, Cloudflare R2)\n- HTTP\u002FHTTPS endpoints (read-only)\n- HuggingFace datasets\n\n**Array stores:**\n- TileDB-SOMA (with cellxgene support)\n- DuckDB for SQL queries on Parquet files\n\n**Visualization:**\n- Vitessce for interactive spatial\u002Fsingle-cell visualization\n\n**Version control:**\n- Git integration for source code tracking\n\n**Reference:** `references\u002Fintegrations.md` - Read this for integration patterns, code examples, and troubleshooting for third-party systems.\n\n### 6. Setup and Deployment\n\n**Installation:**\n- Basic: `uv pip install lamindb`\n- With extras: `uv pip install 'lamindb[gcp,zarr,fcs]'`\n- Modules: bionty, wetlab, clinical\n\n**Instance types:**\n- Local SQLite (development)\n- Cloud storage + SQLite (small teams)\n- Cloud storage + PostgreSQL (production)\n\n**Storage options:**\n- Local filesystem\n- AWS S3 with configurable regions and permissions\n- Google Cloud Storage\n- S3-compatible endpoints (MinIO, Cloudflare R2)\n\n**Configuration:**\n- Cache management for cloud files\n- Multi-user system configurations\n- Git repository sync\n- Environment variables\n\n**Deployment patterns:**\n- Local dev → Cloud production migration\n- Multi-region deployments\n- Shared storage with personal instances\n\n**Reference:** `references\u002Fsetup-deployment.md` - Read this for detailed installation, configuration, storage setup, database management, security best practices, and troubleshooting.\n\n## Common Use Case Workflows\n\n### Use Case 1: Single-Cell RNA-seq Analysis with Ontology Validation\n\n```python\nimport lamindb as ln\nimport bionty as bt\nimport anndata as ad\n\n# Start tracking\nln.track(params={\"analysis\": \"scRNA-seq QC and annotation\"})\n\n# Import cell type ontology\nbt.CellType.import_source()\n\n# Load data\nadata = ad.read_h5ad(\"raw_counts.h5ad\")\n\n# Validate and standardize cell types\nadata.obs[\"cell_type\"] = bt.CellType.standardize(adata.obs[\"cell_type\"])\n\n# Curate with schema\ncurator = ln.curators.AnnDataCurator(adata, schema)\ncurator.validate()\nartifact = curator.save_artifact(key=\"scrna\u002Fvalidated.h5ad\")\n\n# Link ontology annotations\ncell_types = bt.CellType.from_values(adata.obs.cell_type)\nartifact.feature_sets.add_ontology(cell_types)\n\nln.finish()\n```\n\n### Use Case 2: Building a Queryable Data Lakehouse\n\n```python\nimport lamindb as ln\n\n# Register multiple experiments\nfor i, file in enumerate(data_files):\n    artifact = ln.Artifact.from_anndata(\n        ad.read_h5ad(file),\n        key=f\"scrna\u002Fbatch_{i}.h5ad\",\n        description=f\"scRNA-seq batch {i}\"\n    ).save()\n\n    # Annotate with features\n    artifact.features.add_values({\n        \"batch\": i,\n        \"tissue\": tissues[i],\n        \"condition\": conditions[i]\n    })\n\n# Query across all experiments\nimmune_datasets = ln.Artifact.filter(\n    key__startswith=\"scrna\u002F\",\n    tissue=\"PBMC\",\n    condition=\"treated\"\n).to_dataframe()\n\n# Load specific datasets\nfor artifact in immune_datasets:\n    adata = artifact.load()\n    # Analyze\n```\n\n### Use Case 3: ML Pipeline with W&B Integration\n\n```python\nimport lamindb as ln\nimport wandb\n\n# Initialize both systems\nwandb.init(project=\"drug-response\", name=\"exp-42\")\nln.track(params={\"model\": \"random_forest\", \"n_estimators\": 100})\n\n# Load training data from LaminDB\ntrain_artifact = ln.Artifact.get(key=\"datasets\u002Ftrain.parquet\")\ntrain_data = train_artifact.load()\n\n# Train model\nmodel = train_model(train_data)\n\n# Log to W&B\nwandb.log({\"accuracy\": 0.95})\n\n# Save model in LaminDB with W&B linkage\nimport joblib\njoblib.dump(model, \"model.pkl\")\nmodel_artifact = ln.Artifact(\"model.pkl\", key=\"models\u002Fexp-42.pkl\").save()\nmodel_artifact.features.add_values({\"wandb_run_id\": wandb.run.id})\n\nln.finish()\nwandb.finish()\n```\n\n### Use Case 4: Nextflow Pipeline Integration\n\n```python\n# In Nextflow process script\nimport lamindb as ln\n\nln.track()\n\n# Load input artifact\ninput_artifact = ln.Artifact.get(key=\"raw\u002Fbatch_${batch_id}.fastq.gz\")\ninput_path = input_artifact.cache()\n\n# Process (alignment, quantification, etc.)\n# ... Nextflow process logic ...\n\n# Save output\noutput_artifact = ln.Artifact(\n    \"counts.csv\",\n    key=\"processed\u002Fbatch_${batch_id}_counts.csv\"\n).save()\n\nln.finish()\n```\n\n## Getting Started Checklist\n\nTo start using LaminDB effectively:\n\n1. **Installation & Setup** (`references\u002Fsetup-deployment.md`)\n   - Install LaminDB and required extras\n   - Authenticate with `lamin login`\n   - Initialize instance with `lamin init --storage ...`\n\n2. **Learn Core Concepts** (`references\u002Fcore-concepts.md`)\n   - Understand Artifacts, Records, Runs, Transforms\n   - Practice creating and retrieving artifacts\n   - Implement `ln.track()` and `ln.finish()` in workflows\n\n3. **Master Querying** (`references\u002Fdata-management.md`)\n   - Practice filtering and searching registries\n   - Learn feature-based queries\n   - Experiment with streaming large files\n\n4. **Set Up Validation** (`references\u002Fannotation-validation.md`)\n   - Define features relevant to research domain\n   - Create schemas for data types\n   - Practice curation workflows\n\n5. **Integrate Ontologies** (`references\u002Fontologies.md`)\n   - Import relevant biological ontologies (genes, cell types, etc.)\n   - Validate existing annotations\n   - Standardize metadata with ontology terms\n\n6. **Connect Tools** (`references\u002Fintegrations.md`)\n   - Integrate with existing workflow managers\n   - Link ML platforms for experiment tracking\n   - Configure cloud storage and compute\n\n## Key Principles\n\nFollow these principles when working with LaminDB:\n\n1. **Track everything**: Use `ln.track()` at the start of every analysis for automatic lineage capture\n\n2. **Validate early**: Define schemas and validate data before extensive analysis\n\n3. **Use ontologies**: Leverage public biological ontologies for standardized annotations\n\n4. **Organize with keys**: Structure artifact keys hierarchically (e.g., `project\u002Fexperiment\u002Fbatch\u002Ffile.h5ad`)\n\n5. **Query metadata first**: Filter and search before loading large files\n\n6. **Version, don't duplicate**: Use built-in versioning instead of creating new keys for modifications\n\n7. **Annotate with features**: Define typed features for queryable metadata\n\n8. **Document thoroughly**: Add descriptions to artifacts, schemas, and transforms\n\n9. **Leverage lineage**: Use `view_lineage()` to understand data provenance\n\n10. **Start local, scale cloud**: Develop locally with SQLite, deploy to cloud with PostgreSQL\n\n## Reference Files\n\nThis skill includes comprehensive reference documentation organized by capability:\n\n- **`references\u002Fcore-concepts.md`** - Artifacts, records, runs, transforms, features, versioning, lineage\n- **`references\u002Fdata-management.md`** - Querying, filtering, searching, streaming, organizing data\n- **`references\u002Fannotation-validation.md`** - Schema design, curation workflows, validation strategies\n- **`references\u002Fontologies.md`** - Biological ontology management, standardization, hierarchies\n- **`references\u002Fintegrations.md`** - Workflow managers, MLOps platforms, storage systems, tools\n- **`references\u002Fsetup-deployment.md`** - Installation, configuration, deployment, troubleshooting\n\nRead the relevant reference file(s) based on the specific LaminDB capability needed for the task at hand.\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.lamin.ai\n- **API Reference**: https:\u002F\u002Fdocs.lamin.ai\u002Fapi\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Flaminlabs\u002Flamindb\n- **Tutorial**: https:\u002F\u002Fdocs.lamin.ai\u002Ftutorial\n- **FAQ**: https:\u002F\u002Fdocs.lamin.ai\u002Ffaq\n",{},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Flamindb",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":7,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill lamindb",{"tool":37,"command":38},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill lamindb",{"tool":40,"command":41},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill lamindb","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Flamindb\u002FSKILL.md",true,[45],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"agents\u002Fopenai.yaml","REFERENCE","参考文档",353,"https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fplugins\u002Fexpo\u002Fskills\u002Fexpo-ui-jetpack-compose\u002Fagents\u002Fopenai.yaml",[52,66,76,85,91,98],{"skillNo":53,"name":54,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":62,"publishedAt":18},"01M6FKJN13GMG4KY57J3QD8GY5","model-hierarchy-skill","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzscole\u002Fmodel-hierarchy-skill\u002Fmodel-hierarchy-skill\u002FSKILL.md",[57],{"categoryNo":58,"categoryName":59,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[63,64,65],"model","model hierarchy","model hierarchy skill",{"skillNo":67,"name":68,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":72,"publishedAt":18},"01M6FKJJJ6GCAMR4PPPRD12M5X","zoom-mcp","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-mcp\u002FSKILL.md",[71],{"categoryNo":58,"categoryName":59,"taxonomyType":60,"taxonomyTypeDesc":61},[73,74,75],"MCP","zoom","zoom mcp",{"skillNo":77,"name":78,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":18},"01M6FKJE3KYT3KPEZMEEHYKA6K","zoom-apps-sdk","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[82,83,84,74],"sdk","zoom apps sdk","apps",{"skillNo":86,"name":87,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":18},"01M6FKJ9EDH9Q5VWDF8HAJRSP0","windows","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwindows\u002FSKILL.md",[],[87],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":97,"publishedAt":18},"01M6FKJ666BEEFC3AE9GJ72Q93","whiteboard","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwhiteboard\u002FSKILL.md",[96],{"categoryNo":58,"categoryName":59,"taxonomyType":60,"taxonomyTypeDesc":61},[93],{"skillNo":99,"name":100,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":18},"01M6FKJ21WFCDGTJQ2Q91T9DVW","websockets","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwebsockets\u002FSKILL.md",[],[100]]