[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7EPCZAGZPAPMYBWSJ52SS60":3,"skill-same:01M7EPCZAGZPAPMYBWSJ52SS60":64},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":14,"repositoryHtmlUrl":27,"repositoryHomepageUrl":28,"ownerHtmlUrl":29,"ownerBlogUrl":6,"ownerDescription":30,"updatedAt":31,"install":32,"resources":45},"01M7EPCZAGZPAPMYBWSJ52SS60","latchbio-integration",null,"Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow\u002F@task decorators, deploy serverless workflows, LatchFile\u002FLatchDir, Nextflow\u002FSnakemake integration.","marnixGeerkens\u002Frocketlauncher-claude-code","marnixGeerkens","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F143275741?v=4","01M7EJY825SFPHDDZNFDAJHCA9","Marnix Geerkens","01M7EJY82TBA6ABXJ62JWR9B9F",0,"MIT","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fscientific\u002Flatchbio-integration\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: latchbio-integration\ndescription: \"Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow\u002F@task decorators, deploy serverless workflows, LatchFile\u002FLatchDir, Nextflow\u002FSnakemake integration.\"\n---\n\n# LatchBio Integration\n\n## Overview\n\nLatch is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. Built on Flyte, create workflows with @workflow\u002F@task decorators, manage cloud data with LatchFile\u002FLatchDir, configure resources, and integrate Nextflow\u002FSnakemake pipelines.\n\n## Core Capabilities\n\nThe Latch platform provides four main areas of functionality:\n\n### 1. Workflow Creation and Deployment\n- Define serverless workflows using Python decorators\n- Support for native Python, Nextflow, and Snakemake pipelines\n- Automatic containerization with Docker\n- Auto-generated no-code user interfaces\n- Version control and reproducibility\n\n### 2. Data Management\n- Cloud storage abstractions (LatchFile, LatchDir)\n- Structured data organization with Registry (Projects → Tables → Records)\n- Type-safe data operations with links and enums\n- Automatic file transfer between local and cloud\n- Glob pattern matching for file selection\n\n### 3. Resource Configuration\n- Pre-configured task decorators (@small_task, @large_task, @small_gpu_task, @large_gpu_task)\n- Custom resource specifications (CPU, memory, GPU, storage)\n- GPU support (K80, V100, A100)\n- Timeout and storage configuration\n- Cost optimization strategies\n\n### 4. Verified Workflows\n- Production-ready pre-built pipelines\n- Bulk RNA-seq, DESeq2, pathway analysis\n- AlphaFold and ColabFold for protein structure prediction\n- Single-cell tools (ArchR, scVelo, emptyDropsR)\n- CRISPR analysis, phylogenetics, and more\n\n## Quick Start\n\n### Installation and Setup\n\n```bash\n# Install Latch SDK\npython3 -m uv pip install latch\n\n# Login to Latch\nlatch login\n\n# Initialize a new workflow\nlatch init my-workflow\n\n# Register workflow to platform\nlatch register my-workflow\n```\n\n**Prerequisites:**\n- Docker installed and running\n- Latch account credentials\n- Python 3.8+\n\n### Basic Workflow Example\n\n```python\nfrom latch import workflow, small_task\nfrom latch.types import LatchFile\n\n@small_task\ndef process_file(input_file: LatchFile) -> LatchFile:\n    \"\"\"Process a single file\"\"\"\n    # Processing logic\n    return output_file\n\n@workflow\ndef my_workflow(input_file: LatchFile) -> LatchFile:\n    \"\"\"\n    My bioinformatics workflow\n\n    Args:\n        input_file: Input data file\n    \"\"\"\n    return process_file(input_file=input_file)\n```\n\n## When to Use This Skill\n\nThis skill should be used when encountering any of the following scenarios:\n\n**Workflow Development:**\n- \"Create a Latch workflow for RNA-seq analysis\"\n- \"Deploy my pipeline to Latch\"\n- \"Convert my Nextflow pipeline to Latch\"\n- \"Add GPU support to my workflow\"\n- Working with `@workflow`, `@task` decorators\n\n**Data Management:**\n- \"Organize my sequencing data in Latch Registry\"\n- \"How do I use LatchFile and LatchDir?\"\n- \"Set up sample tracking in Latch\"\n- Working with `latch:\u002F\u002F\u002F` paths\n\n**Resource Configuration:**\n- \"Configure GPU for AlphaFold on Latch\"\n- \"My task is running out of memory\"\n- \"How do I optimize workflow costs?\"\n- Working with task decorators\n\n**Verified Workflows:**\n- \"Run AlphaFold on Latch\"\n- \"Use DESeq2 for differential expression\"\n- \"Available pre-built workflows\"\n- Using `latch.verified` module\n\n## Detailed Documentation\n\nThis skill includes comprehensive reference documentation organized by capability:\n\n### references\u002Fworkflow-creation.md\n**Read this for:**\n- Creating and registering workflows\n- Task definition and decorators\n- Supporting Python, Nextflow, Snakemake\n- Launch plans and conditional sections\n- Workflow execution (CLI and programmatic)\n- Multi-step and parallel pipelines\n- Troubleshooting registration issues\n\n**Key topics:**\n- `latch init` and `latch register` commands\n- `@workflow` and `@task` decorators\n- LatchFile and LatchDir basics\n- Type annotations and docstrings\n- Launch plans with preset parameters\n- Conditional UI sections\n\n### references\u002Fdata-management.md\n**Read this for:**\n- Cloud storage with LatchFile and LatchDir\n- Registry system (Projects, Tables, Records)\n- Linked records and relationships\n- Enum and typed columns\n- Bulk operations and transactions\n- Integration with workflows\n- Account and workspace management\n\n**Key topics:**\n- `latch:\u002F\u002F\u002F` path format\n- File transfer and glob patterns\n- Creating and querying Registry tables\n- Column types (string, number, file, link, enum)\n- Record CRUD operations\n- Workflow-Registry integration\n\n### references\u002Fresource-configuration.md\n**Read this for:**\n- Task resource decorators\n- Custom CPU, memory, GPU configuration\n- GPU types (K80, V100, A100)\n- Timeout and storage settings\n- Resource optimization strategies\n- Cost-effective workflow design\n- Monitoring and debugging\n\n**Key topics:**\n- `@small_task`, `@large_task`, `@small_gpu_task`, `@large_gpu_task`\n- `@custom_task` with precise specifications\n- Multi-GPU configuration\n- Resource selection by workload type\n- Platform limits and quotas\n\n### references\u002Fverified-workflows.md\n**Read this for:**\n- Pre-built production workflows\n- Bulk RNA-seq and DESeq2\n- AlphaFold and ColabFold\n- Single-cell analysis (ArchR, scVelo)\n- CRISPR editing analysis\n- Pathway enrichment\n- Integration with custom workflows\n\n**Key topics:**\n- `latch.verified` module imports\n- Available verified workflows\n- Workflow parameters and options\n- Combining verified and custom steps\n- Version management\n\n## Common Workflow Patterns\n\n### Complete RNA-seq Pipeline\n\n```python\nfrom latch import workflow, small_task, large_task\nfrom latch.types import LatchFile, LatchDir\n\n@small_task\ndef quality_control(fastq: LatchFile) -> LatchFile:\n    \"\"\"Run FastQC\"\"\"\n    return qc_output\n\n@large_task\ndef alignment(fastq: LatchFile, genome: str) -> LatchFile:\n    \"\"\"STAR alignment\"\"\"\n    return bam_output\n\n@small_task\ndef quantification(bam: LatchFile) -> LatchFile:\n    \"\"\"featureCounts\"\"\"\n    return counts\n\n@workflow\ndef rnaseq_pipeline(\n    input_fastq: LatchFile,\n    genome: str,\n    output_dir: LatchDir\n) -> LatchFile:\n    \"\"\"RNA-seq analysis pipeline\"\"\"\n    qc = quality_control(fastq=input_fastq)\n    aligned = alignment(fastq=qc, genome=genome)\n    return quantification(bam=aligned)\n```\n\n### GPU-Accelerated Workflow\n\n```python\nfrom latch import workflow, small_task, large_gpu_task\nfrom latch.types import LatchFile\n\n@small_task\ndef preprocess(input_file: LatchFile) -> LatchFile:\n    \"\"\"Prepare data\"\"\"\n    return processed\n\n@large_gpu_task\ndef gpu_computation(data: LatchFile) -> LatchFile:\n    \"\"\"GPU-accelerated analysis\"\"\"\n    return results\n\n@workflow\ndef gpu_pipeline(input_file: LatchFile) -> LatchFile:\n    \"\"\"Pipeline with GPU tasks\"\"\"\n    preprocessed = preprocess(input_file=input_file)\n    return gpu_computation(data=preprocessed)\n```\n\n### Registry-Integrated Workflow\n\n```python\nfrom latch import workflow, small_task\nfrom latch.registry.table import Table\nfrom latch.registry.record import Record\nfrom latch.types import LatchFile\n\n@small_task\ndef process_and_track(sample_id: str, table_id: str) -> str:\n    \"\"\"Process sample and update Registry\"\"\"\n    # Get sample from registry\n    table = Table.get(table_id=table_id)\n    records = Record.list(table_id=table_id, filter={\"sample_id\": sample_id})\n    sample = records[0]\n\n    # Process\n    input_file = sample.values[\"fastq_file\"]\n    output = process(input_file)\n\n    # Update registry\n    sample.update(values={\"status\": \"completed\", \"result\": output})\n    return \"Success\"\n\n@workflow\ndef registry_workflow(sample_id: str, table_id: str):\n    \"\"\"Workflow integrated with Registry\"\"\"\n    return process_and_track(sample_id=sample_id, table_id=table_id)\n```\n\n## Best Practices\n\n### Workflow Design\n1. Use type annotations for all parameters\n2. Write clear docstrings (appear in UI)\n3. Start with standard task decorators, scale up if needed\n4. Break complex workflows into modular tasks\n5. Implement proper error handling\n\n### Data Management\n6. Use consistent folder structures\n7. Define Registry schemas before bulk entry\n8. Use linked records for relationships\n9. Store metadata in Registry for traceability\n\n### Resource Configuration\n10. Right-size resources (don't over-allocate)\n11. Use GPU only when algorithms support it\n12. Monitor execution metrics and optimize\n13. Design for parallel execution when possible\n\n### Development Workflow\n14. Test locally with Docker before registration\n15. Use version control for workflow code\n16. Document resource requirements\n17. Profile workflows to determine actual needs\n\n## Troubleshooting\n\n### Common Issues\n\n**Registration Failures:**\n- Ensure Docker is running\n- Check authentication with `latch login`\n- Verify all dependencies in Dockerfile\n- Use `--verbose` flag for detailed logs\n\n**Resource Problems:**\n- Out of memory: Increase memory in task decorator\n- Timeouts: Increase timeout parameter\n- Storage issues: Increase ephemeral storage_gib\n\n**Data Access:**\n- Use correct `latch:\u002F\u002F\u002F` path format\n- Verify file exists in workspace\n- Check permissions for shared workspaces\n\n**Type Errors:**\n- Add type annotations to all parameters\n- Use LatchFile\u002FLatchDir for file\u002Fdirectory parameters\n- Ensure workflow return type matches actual return\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.latch.bio\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Flatchbio\u002Flatch\n- **Slack Community**: Join Latch SDK workspace\n- **API Reference**: https:\u002F\u002Fdocs.latch.bio\u002Fapi\u002Flatch.html\n- **Blog**: https:\u002F\u002Fblog.latch.bio\n\n## Support\n\nFor issues or questions:\n1. Check documentation links above\n2. Search GitHub issues\n3. Ask in Slack community\n4. Contact support@latch.bio\n","# LatchBio Integration\n\n## Overview\n\nLatch is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. Built on Flyte, create workflows with @workflow\u002F@task decorators, manage cloud data with LatchFile\u002FLatchDir, configure resources, and integrate Nextflow\u002FSnakemake pipelines.\n\n## Core Capabilities\n\nThe Latch platform provides four main areas of functionality:\n\n### 1. Workflow Creation and Deployment\n- Define serverless workflows using Python decorators\n- Support for native Python, Nextflow, and Snakemake pipelines\n- Automatic containerization with Docker\n- Auto-generated no-code user interfaces\n- Version control and reproducibility\n\n### 2. Data Management\n- Cloud storage abstractions (LatchFile, LatchDir)\n- Structured data organization with Registry (Projects → Tables → Records)\n- Type-safe data operations with links and enums\n- Automatic file transfer between local and cloud\n- Glob pattern matching for file selection\n\n### 3. Resource Configuration\n- Pre-configured task decorators (@small_task, @large_task, @small_gpu_task, @large_gpu_task)\n- Custom resource specifications (CPU, memory, GPU, storage)\n- GPU support (K80, V100, A100)\n- Timeout and storage configuration\n- Cost optimization strategies\n\n### 4. Verified Workflows\n- Production-ready pre-built pipelines\n- Bulk RNA-seq, DESeq2, pathway analysis\n- AlphaFold and ColabFold for protein structure prediction\n- Single-cell tools (ArchR, scVelo, emptyDropsR)\n- CRISPR analysis, phylogenetics, and more\n\n## Quick Start\n\n### Installation and Setup\n\n```bash\n# Install Latch SDK\npython3 -m uv pip install latch\n\n# Login to Latch\nlatch login\n\n# Initialize a new workflow\nlatch init my-workflow\n\n# Register workflow to platform\nlatch register my-workflow\n```\n\n**Prerequisites:**\n- Docker installed and running\n- Latch account credentials\n- Python 3.8+\n\n### Basic Workflow Example\n\n```python\nfrom latch import workflow, small_task\nfrom latch.types import LatchFile\n\n@small_task\ndef process_file(input_file: LatchFile) -> LatchFile:\n    \"\"\"Process a single file\"\"\"\n    # Processing logic\n    return output_file\n\n@workflow\ndef my_workflow(input_file: LatchFile) -> LatchFile:\n    \"\"\"\n    My bioinformatics workflow\n\n    Args:\n        input_file: Input data file\n    \"\"\"\n    return process_file(input_file=input_file)\n```\n\n## When to Use This Skill\n\nThis skill should be used when encountering any of the following scenarios:\n\n**Workflow Development:**\n- \"Create a Latch workflow for RNA-seq analysis\"\n- \"Deploy my pipeline to Latch\"\n- \"Convert my Nextflow pipeline to Latch\"\n- \"Add GPU support to my workflow\"\n- Working with `@workflow`, `@task` decorators\n\n**Data Management:**\n- \"Organize my sequencing data in Latch Registry\"\n- \"How do I use LatchFile and LatchDir?\"\n- \"Set up sample tracking in Latch\"\n- Working with `latch:\u002F\u002F\u002F` paths\n\n**Resource Configuration:**\n- \"Configure GPU for AlphaFold on Latch\"\n- \"My task is running out of memory\"\n- \"How do I optimize workflow costs?\"\n- Working with task decorators\n\n**Verified Workflows:**\n- \"Run AlphaFold on Latch\"\n- \"Use DESeq2 for differential expression\"\n- \"Available pre-built workflows\"\n- Using `latch.verified` module\n\n## Detailed Documentation\n\nThis skill includes comprehensive reference documentation organized by capability:\n\n### references\u002Fworkflow-creation.md\n**Read this for:**\n- Creating and registering workflows\n- Task definition and decorators\n- Supporting Python, Nextflow, Snakemake\n- Launch plans and conditional sections\n- Workflow execution (CLI and programmatic)\n- Multi-step and parallel pipelines\n- Troubleshooting registration issues\n\n**Key topics:**\n- `latch init` and `latch register` commands\n- `@workflow` and `@task` decorators\n- LatchFile and LatchDir basics\n- Type annotations and docstrings\n- Launch plans with preset parameters\n- Conditional UI sections\n\n### references\u002Fdata-management.md\n**Read this for:**\n- Cloud storage with LatchFile and LatchDir\n- Registry system (Projects, Tables, Records)\n- Linked records and relationships\n- Enum and typed columns\n- Bulk operations and transactions\n- Integration with workflows\n- Account and workspace management\n\n**Key topics:**\n- `latch:\u002F\u002F\u002F` path format\n- File transfer and glob patterns\n- Creating and querying Registry tables\n- Column types (string, number, file, link, enum)\n- Record CRUD operations\n- Workflow-Registry integration\n\n### references\u002Fresource-configuration.md\n**Read this for:**\n- Task resource decorators\n- Custom CPU, memory, GPU configuration\n- GPU types (K80, V100, A100)\n- Timeout and storage settings\n- Resource optimization strategies\n- Cost-effective workflow design\n- Monitoring and debugging\n\n**Key topics:**\n- `@small_task`, `@large_task`, `@small_gpu_task`, `@large_gpu_task`\n- `@custom_task` with precise specifications\n- Multi-GPU configuration\n- Resource selection by workload type\n- Platform limits and quotas\n\n### references\u002Fverified-workflows.md\n**Read this for:**\n- Pre-built production workflows\n- Bulk RNA-seq and DESeq2\n- AlphaFold and ColabFold\n- Single-cell analysis (ArchR, scVelo)\n- CRISPR editing analysis\n- Pathway enrichment\n- Integration with custom workflows\n\n**Key topics:**\n- `latch.verified` module imports\n- Available verified workflows\n- Workflow parameters and options\n- Combining verified and custom steps\n- Version management\n\n## Common Workflow Patterns\n\n### Complete RNA-seq Pipeline\n\n```python\nfrom latch import workflow, small_task, large_task\nfrom latch.types import LatchFile, LatchDir\n\n@small_task\ndef quality_control(fastq: LatchFile) -> LatchFile:\n    \"\"\"Run FastQC\"\"\"\n    return qc_output\n\n@large_task\ndef alignment(fastq: LatchFile, genome: str) -> LatchFile:\n    \"\"\"STAR alignment\"\"\"\n    return bam_output\n\n@small_task\ndef quantification(bam: LatchFile) -> LatchFile:\n    \"\"\"featureCounts\"\"\"\n    return counts\n\n@workflow\ndef rnaseq_pipeline(\n    input_fastq: LatchFile,\n    genome: str,\n    output_dir: LatchDir\n) -> LatchFile:\n    \"\"\"RNA-seq analysis pipeline\"\"\"\n    qc = quality_control(fastq=input_fastq)\n    aligned = alignment(fastq=qc, genome=genome)\n    return quantification(bam=aligned)\n```\n\n### GPU-Accelerated Workflow\n\n```python\nfrom latch import workflow, small_task, large_gpu_task\nfrom latch.types import LatchFile\n\n@small_task\ndef preprocess(input_file: LatchFile) -> LatchFile:\n    \"\"\"Prepare data\"\"\"\n    return processed\n\n@large_gpu_task\ndef gpu_computation(data: LatchFile) -> LatchFile:\n    \"\"\"GPU-accelerated analysis\"\"\"\n    return results\n\n@workflow\ndef gpu_pipeline(input_file: LatchFile) -> LatchFile:\n    \"\"\"Pipeline with GPU tasks\"\"\"\n    preprocessed = preprocess(input_file=input_file)\n    return gpu_computation(data=preprocessed)\n```\n\n### Registry-Integrated Workflow\n\n```python\nfrom latch import workflow, small_task\nfrom latch.registry.table import Table\nfrom latch.registry.record import Record\nfrom latch.types import LatchFile\n\n@small_task\ndef process_and_track(sample_id: str, table_id: str) -> str:\n    \"\"\"Process sample and update Registry\"\"\"\n    # Get sample from registry\n    table = Table.get(table_id=table_id)\n    records = Record.list(table_id=table_id, filter={\"sample_id\": sample_id})\n    sample = records[0]\n\n    # Process\n    input_file = sample.values[\"fastq_file\"]\n    output = process(input_file)\n\n    # Update registry\n    sample.update(values={\"status\": \"completed\", \"result\": output})\n    return \"Success\"\n\n@workflow\ndef registry_workflow(sample_id: str, table_id: str):\n    \"\"\"Workflow integrated with Registry\"\"\"\n    return process_and_track(sample_id=sample_id, table_id=table_id)\n```\n\n## Best Practices\n\n### Workflow Design\n1. Use type annotations for all parameters\n2. Write clear docstrings (appear in UI)\n3. Start with standard task decorators, scale up if needed\n4. Break complex workflows into modular tasks\n5. Implement proper error handling\n\n### Data Management\n6. Use consistent folder structures\n7. Define Registry schemas before bulk entry\n8. Use linked records for relationships\n9. Store metadata in Registry for traceability\n\n### Resource Configuration\n10. Right-size resources (don't over-allocate)\n11. Use GPU only when algorithms support it\n12. Monitor execution metrics and optimize\n13. Design for parallel execution when possible\n\n### Development Workflow\n14. Test locally with Docker before registration\n15. Use version control for workflow code\n16. Document resource requirements\n17. Profile workflows to determine actual needs\n\n## Troubleshooting\n\n### Common Issues\n\n**Registration Failures:**\n- Ensure Docker is running\n- Check authentication with `latch login`\n- Verify all dependencies in Dockerfile\n- Use `--verbose` flag for detailed logs\n\n**Resource Problems:**\n- Out of memory: Increase memory in task decorator\n- Timeouts: Increase timeout parameter\n- Storage issues: Increase ephemeral storage_gib\n\n**Data Access:**\n- Use correct `latch:\u002F\u002F\u002F` path format\n- Verify file exists in workspace\n- Check permissions for shared workspaces\n\n**Type Errors:**\n- Add type annotations to all parameters\n- Use LatchFile\u002FLatchDir for file\u002Fdirectory parameters\n- Ensure workflow return type matches actual return\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.latch.bio\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Flatchbio\u002Flatch\n- **Slack Community**: Join Latch SDK workspace\n- **API Reference**: https:\u002F\u002Fdocs.latch.bio\u002Fapi\u002Flatch.html\n- **Blog**: https:\u002F\u002Fblog.latch.bio\n\n## Support\n\nFor issues or questions:\n1. Check documentation links above\n2. Search GitHub issues\n3. Ask in Slack community\n4. Contact support@latch.bio\n",{"name":5,"description":7},"main","4e647b68aabe849e143b6950971c380f7adccdef","components\u002Fskills\u002Fscientific\u002Flatchbio-integration","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code","https:\u002F\u002Frocketlauncher.ai\u002Fclaude-code","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens","CEO of Rocketlauncher.ai\r\n\r\nObsessed with AI\r\n\r\njoin  https:\u002F\u002Fwww.skool.com\u002Frocketlauncher-university\r\n👉🏻 it's free ","2026-10-13 00:52:14",{"source":8,"skillName":5,"commands":33,"prompt":43,"zipAvailable":44},[34,37,40],{"tool":35,"command":36},"npx","npx skills add marnixGeerkens\u002Frocketlauncher-claude-code --skill latchbio-integration",{"tool":38,"command":39},"bunx","bunx skills add marnixGeerkens\u002Frocketlauncher-claude-code --skill latchbio-integration",{"tool":41,"command":42},"pnpm","pnpm dlx skills add marnixGeerkens\u002Frocketlauncher-claude-code --skill latchbio-integration","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fscientific\u002Flatchbio-integration\u002FSKILL.md",true,[46,52,56,60],{"path":47,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":50,"sourceHtmlUrl":51},"references\u002Fdata-management.md","REFERENCE","参考文档",9737,"https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fscientific\u002Flatchbio-integration\u002Freferences\u002Fdata-management.md",{"path":53,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":54,"sourceHtmlUrl":55},"references\u002Fresource-configuration.md",9569,"https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fscientific\u002Flatchbio-integration\u002Freferences\u002Fresource-configuration.md",{"path":57,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":58,"sourceHtmlUrl":59},"references\u002Fverified-workflows.md",10472,"https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fscientific\u002Flatchbio-integration\u002Freferences\u002Fverified-workflows.md",{"path":61,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":62,"sourceHtmlUrl":63},"references\u002Fworkflow-creation.md",6333,"https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fscientific\u002Flatchbio-integration\u002Freferences\u002Fworkflow-creation.md",[65,72,81,88,95,102],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":19},"01M7EQFDF4T3K89WMFVFQVT3C8","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":77,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M7EQFA5XQ0811Q4Q0WCFASZG","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","Apache-2.0","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":19},"01M7EQF4J6C2WKQWD9E2RQC6MJ","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[],[],{"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":19},"01M7EQF1AT655CYYZBEQ8V6Z9D","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":19},"01M7EQEY9ENH0G2EJ1WD6C1CE8","slack-automation","Automate Slack workspace operations including messaging, search, channel management, and reaction workflows through Composio's Slack toolkit.","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fslack-automation\u002FSKILL.md",[],[],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":19},"01M7EQESHEKPTE0QPW9R6XV5K2","rote","Compile a proven agent skill (a SKILL.md plus references) into a deterministic pipeline that runs without an LLM in the loop, then serve it back to Claude as an MCP tool. Use when: rote, compile this skill, turn this skill into a workflow, make this skill deterministic, make this skill cheaper or faster, harden this skill for production, run this skill as a background job.","https:\u002F\u002Fgithub.com\u002FmarnixGeerkens\u002Frocketlauncher-claude-code\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Frote\u002FSKILL.md",[],[]]