[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JKVDKXKW6CGVVRMAGE9JCY":3,"skill-same:01M6JKVDKXKW6CGVVRMAGE9JCY":115},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M6JKVDKXKW6CGVVRMAGE9JCY","modal",null,"Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"use","autoscaling","python code","with","GPU Acceleration","processing","scaling","compute","scheduling","models","python","cloud","batch","gpu","code","run","tasks","ml","containers","2026-11-10 10:12:30",false,"---\nname: modal\ndescription: Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.\n---\n\n# Modal\n\n## Overview\n\nModal is a serverless platform for running Python code in the cloud with minimal configuration. Execute functions on powerful GPUs, scale automatically to thousands of containers, and pay only for compute used.\n\nModal is particularly suited for AI\u002FML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https:\u002F\u002Fmodal.com and receive $30\u002Fmonth in credits.\n\n## When to Use This Skill\n\nUse Modal for:\n- Deploying and serving ML models (LLMs, image generation, embedding models)\n- Running GPU-accelerated computation (training, inference, rendering)\n- Batch processing large datasets in parallel\n- Scheduling compute-intensive jobs (daily data processing, model training)\n- Building serverless APIs that need automatic scaling\n- Scientific computing requiring distributed compute or specialized hardware\n\n## Authentication and Setup\n\nModal requires authentication via API token.\n\n### Initial Setup\n\n```bash\n# Install Modal\nuv uv pip install modal\n\n# Authenticate (opens browser for login)\nmodal token new\n```\n\nThis creates a token stored in `~\u002F.modal.toml`. The token authenticates all Modal operations.\n\n### Verify Setup\n\n```python\nimport modal\n\napp = modal.App(\"test-app\")\n\n@app.function()\ndef hello():\n    print(\"Modal is working!\")\n```\n\nRun with: `modal run script.py`\n\n## Core Capabilities\n\nModal provides serverless Python execution through Functions that run in containers. Define compute requirements, dependencies, and scaling behavior declaratively.\n\n### 1. Define Container Images\n\nSpecify dependencies and environment for functions using Modal Images.\n\n```python\nimport modal\n\n# Basic image with Python packages\nimage = (\n    modal.Image.debian_slim(python_version=\"3.12\")\n    .uv_pip_install(\"torch\", \"transformers\", \"numpy\")\n)\n\napp = modal.App(\"ml-app\", image=image)\n```\n\n**Common patterns:**\n- Install Python packages: `.uv_pip_install(\"pandas\", \"scikit-learn\")`\n- Install system packages: `.apt_install(\"ffmpeg\", \"git\")`\n- Use existing Docker images: `modal.Image.from_registry(\"nvidia\u002Fcuda:12.1.0-base\")`\n- Add local code: `.add_local_python_source(\"my_module\")`\n\nSee `references\u002Fimages.md` for comprehensive image building documentation.\n\n### 2. Create Functions\n\nDefine functions that run in the cloud with the `@app.function()` decorator.\n\n```python\n@app.function()\ndef process_data(file_path: str):\n    import pandas as pd\n    df = pd.read_csv(file_path)\n    return df.describe()\n```\n\n**Call functions:**\n```python\n# From local entrypoint\n@app.local_entrypoint()\ndef main():\n    result = process_data.remote(\"data.csv\")\n    print(result)\n```\n\nRun with: `modal run script.py`\n\nSee `references\u002Ffunctions.md` for function patterns, deployment, and parameter handling.\n\n### 3. Request GPUs\n\nAttach GPUs to functions for accelerated computation.\n\n```python\n@app.function(gpu=\"H100\")\ndef train_model():\n    import torch\n    assert torch.cuda.is_available()\n    # GPU-accelerated code here\n```\n\n**Available GPU types:**\n- `T4`, `L4` - Cost-effective inference\n- `A10`, `A100`, `A100-80GB` - Standard training\u002Finference\n- `L40S` - Excellent cost\u002Fperformance balance (48GB)\n- `H100`, `H200` - High-performance training\n- `B200` - Flagship performance (most powerful)\n\n**Request multiple GPUs:**\n```python\n@app.function(gpu=\"H100:8\")  # 8x H100 GPUs\ndef train_large_model():\n    pass\n```\n\nSee `references\u002Fgpu.md` for GPU selection guidance, CUDA setup, and multi-GPU configuration.\n\n### 4. Configure Resources\n\nRequest CPU cores, memory, and disk for functions.\n\n```python\n@app.function(\n    cpu=8.0,           # 8 physical cores\n    memory=32768,      # 32 GiB RAM\n    ephemeral_disk=10240  # 10 GiB disk\n)\ndef memory_intensive_task():\n    pass\n```\n\nDefault allocation: 0.125 CPU cores, 128 MiB memory. Billing based on reservation or actual usage, whichever is higher.\n\nSee `references\u002Fresources.md` for resource limits and billing details.\n\n### 5. Scale Automatically\n\nModal autoscales functions from zero to thousands of containers based on demand.\n\n**Process inputs in parallel:**\n```python\n@app.function()\ndef analyze_sample(sample_id: int):\n    # Process single sample\n    return result\n\n@app.local_entrypoint()\ndef main():\n    sample_ids = range(1000)\n    # Automatically parallelized across containers\n    results = list(analyze_sample.map(sample_ids))\n```\n\n**Configure autoscaling:**\n```python\n@app.function(\n    max_containers=100,      # Upper limit\n    min_containers=2,        # Keep warm\n    buffer_containers=5      # Idle buffer for bursts\n)\ndef inference():\n    pass\n```\n\nSee `references\u002Fscaling.md` for autoscaling configuration, concurrency, and scaling limits.\n\n### 6. Store Data Persistently\n\nUse Volumes for persistent storage across function invocations.\n\n```python\nvolume = modal.Volume.from_name(\"my-data\", create_if_missing=True)\n\n@app.function(volumes={\"\u002Fdata\": volume})\ndef save_results(data):\n    with open(\"\u002Fdata\u002Fresults.txt\", \"w\") as f:\n        f.write(data)\n    volume.commit()  # Persist changes\n```\n\nVolumes persist data between runs, store model weights, cache datasets, and share data between functions.\n\nSee `references\u002Fvolumes.md` for volume management, commits, and caching patterns.\n\n### 7. Manage Secrets\n\nStore API keys and credentials securely using Modal Secrets.\n\n```python\n@app.function(secrets=[modal.Secret.from_name(\"huggingface\")])\ndef download_model():\n    import os\n    token = os.environ[\"HF_TOKEN\"]\n    # Use token for authentication\n```\n\n**Create secrets in Modal dashboard or via CLI:**\n```bash\nmodal secret create my-secret KEY=value API_TOKEN=xyz\n```\n\nSee `references\u002Fsecrets.md` for secret management and authentication patterns.\n\n### 8. Deploy Web Endpoints\n\nServe HTTP endpoints, APIs, and webhooks with `@modal.web_endpoint()`.\n\n```python\n@app.function()\n@modal.web_endpoint(method=\"POST\")\ndef predict(data: dict):\n    # Process request\n    result = model.predict(data[\"input\"])\n    return {\"prediction\": result}\n```\n\n**Deploy with:**\n```bash\nmodal deploy script.py\n```\n\nModal provides HTTPS URL for the endpoint.\n\nSee `references\u002Fweb-endpoints.md` for FastAPI integration, streaming, authentication, and WebSocket support.\n\n### 9. Schedule Jobs\n\nRun functions on a schedule with cron expressions.\n\n```python\n@app.function(schedule=modal.Cron(\"0 2 * * *\"))  # Daily at 2 AM\ndef daily_backup():\n    # Backup data\n    pass\n\n@app.function(schedule=modal.Period(hours=4))  # Every 4 hours\ndef refresh_cache():\n    # Update cache\n    pass\n```\n\nScheduled functions run automatically without manual invocation.\n\nSee `references\u002Fscheduled-jobs.md` for cron syntax, timezone configuration, and monitoring.\n\n## Common Workflows\n\n### Deploy ML Model for Inference\n\n```python\nimport modal\n\n# Define dependencies\nimage = modal.Image.debian_slim().uv_pip_install(\"torch\", \"transformers\")\napp = modal.App(\"llm-inference\", image=image)\n\n# Download model at build time\n@app.function()\ndef download_model():\n    from transformers import AutoModel\n    AutoModel.from_pretrained(\"bert-base-uncased\")\n\n# Serve model\n@app.cls(gpu=\"L40S\")\nclass Model:\n    @modal.enter()\n    def load_model(self):\n        from transformers import pipeline\n        self.pipe = pipeline(\"text-classification\", device=\"cuda\")\n\n    @modal.method()\n    def predict(self, text: str):\n        return self.pipe(text)\n\n@app.local_entrypoint()\ndef main():\n    model = Model()\n    result = model.predict.remote(\"Modal is great!\")\n    print(result)\n```\n\n### Batch Process Large Dataset\n\n```python\n@app.function(cpu=2.0, memory=4096)\ndef process_file(file_path: str):\n    import pandas as pd\n    df = pd.read_csv(file_path)\n    # Process data\n    return df.shape[0]\n\n@app.local_entrypoint()\ndef main():\n    files = [\"file1.csv\", \"file2.csv\", ...]  # 1000s of files\n    # Automatically parallelized across containers\n    for count in process_file.map(files):\n        print(f\"Processed {count} rows\")\n```\n\n### Train Model on GPU\n\n```python\n@app.function(\n    gpu=\"A100:2\",      # 2x A100 GPUs\n    timeout=3600       # 1 hour timeout\n)\ndef train_model(config: dict):\n    import torch\n    # Multi-GPU training code\n    model = create_model(config)\n    train(model)\n    return metrics\n```\n\n## Reference Documentation\n\nDetailed documentation for specific features:\n\n- **`references\u002Fgetting-started.md`** - Authentication, setup, basic concepts\n- **`references\u002Fimages.md`** - Image building, dependencies, Dockerfiles\n- **`references\u002Ffunctions.md`** - Function patterns, deployment, parameters\n- **`references\u002Fgpu.md`** - GPU types, CUDA, multi-GPU configuration\n- **`references\u002Fresources.md`** - CPU, memory, disk management\n- **`references\u002Fscaling.md`** - Autoscaling, parallel execution, concurrency\n- **`references\u002Fvolumes.md`** - Persistent storage, data management\n- **`references\u002Fsecrets.md`** - Environment variables, authentication\n- **`references\u002Fweb-endpoints.md`** - APIs, webhooks, endpoints\n- **`references\u002Fscheduled-jobs.md`** - Cron jobs, periodic tasks\n- **`references\u002Fexamples.md`** - Common patterns for scientific computing\n\n## Best Practices\n\n1. **Pin dependencies** in `.uv_pip_install()` for reproducible builds\n2. **Use appropriate GPU types** - L40S for inference, H100\u002FA100 for training\n3. **Leverage caching** - Use Volumes for model weights and datasets\n4. **Configure autoscaling** - Set `max_containers` and `min_containers` based on workload\n5. **Import packages in function body** if not available locally\n6. **Use `.map()` for parallel processing** instead of sequential loops\n7. **Store secrets securely** - Never hardcode API keys\n8. **Monitor costs** - Check Modal dashboard for usage and billing\n\n## Troubleshooting\n\n**\"Module not found\" errors:**\n- Add packages to image with `.uv_pip_install(\"package-name\")`\n- Import packages inside function body if not available locally\n\n**GPU not detected:**\n- Verify GPU specification: `@app.function(gpu=\"A100\")`\n- Check CUDA availability: `torch.cuda.is_available()`\n\n**Function timeout:**\n- Increase timeout: `@app.function(timeout=3600)`\n- Default timeout is 5 minutes\n\n**Volume changes not persisting:**\n- Call `volume.commit()` after writing files\n- Verify volume mounted correctly in function decorator\n\nFor additional help, see Modal documentation at https:\u002F\u002Fmodal.com\u002Fdocs or join Modal Slack community.\n","# Modal\n\n## Overview\n\nModal is a serverless platform for running Python code in the cloud with minimal configuration. Execute functions on powerful GPUs, scale automatically to thousands of containers, and pay only for compute used.\n\nModal is particularly suited for AI\u002FML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https:\u002F\u002Fmodal.com and receive $30\u002Fmonth in credits.\n\n## When to Use This Skill\n\nUse Modal for:\n- Deploying and serving ML models (LLMs, image generation, embedding models)\n- Running GPU-accelerated computation (training, inference, rendering)\n- Batch processing large datasets in parallel\n- Scheduling compute-intensive jobs (daily data processing, model training)\n- Building serverless APIs that need automatic scaling\n- Scientific computing requiring distributed compute or specialized hardware\n\n## Authentication and Setup\n\nModal requires authentication via API token.\n\n### Initial Setup\n\n```bash\n# Install Modal\nuv uv pip install modal\n\n# Authenticate (opens browser for login)\nmodal token new\n```\n\nThis creates a token stored in `~\u002F.modal.toml`. The token authenticates all Modal operations.\n\n### Verify Setup\n\n```python\nimport modal\n\napp = modal.App(\"test-app\")\n\n@app.function()\ndef hello():\n    print(\"Modal is working!\")\n```\n\nRun with: `modal run script.py`\n\n## Core Capabilities\n\nModal provides serverless Python execution through Functions that run in containers. Define compute requirements, dependencies, and scaling behavior declaratively.\n\n### 1. Define Container Images\n\nSpecify dependencies and environment for functions using Modal Images.\n\n```python\nimport modal\n\n# Basic image with Python packages\nimage = (\n    modal.Image.debian_slim(python_version=\"3.12\")\n    .uv_pip_install(\"torch\", \"transformers\", \"numpy\")\n)\n\napp = modal.App(\"ml-app\", image=image)\n```\n\n**Common patterns:**\n- Install Python packages: `.uv_pip_install(\"pandas\", \"scikit-learn\")`\n- Install system packages: `.apt_install(\"ffmpeg\", \"git\")`\n- Use existing Docker images: `modal.Image.from_registry(\"nvidia\u002Fcuda:12.1.0-base\")`\n- Add local code: `.add_local_python_source(\"my_module\")`\n\nSee `references\u002Fimages.md` for comprehensive image building documentation.\n\n### 2. Create Functions\n\nDefine functions that run in the cloud with the `@app.function()` decorator.\n\n```python\n@app.function()\ndef process_data(file_path: str):\n    import pandas as pd\n    df = pd.read_csv(file_path)\n    return df.describe()\n```\n\n**Call functions:**\n```python\n# From local entrypoint\n@app.local_entrypoint()\ndef main():\n    result = process_data.remote(\"data.csv\")\n    print(result)\n```\n\nRun with: `modal run script.py`\n\nSee `references\u002Ffunctions.md` for function patterns, deployment, and parameter handling.\n\n### 3. Request GPUs\n\nAttach GPUs to functions for accelerated computation.\n\n```python\n@app.function(gpu=\"H100\")\ndef train_model():\n    import torch\n    assert torch.cuda.is_available()\n    # GPU-accelerated code here\n```\n\n**Available GPU types:**\n- `T4`, `L4` - Cost-effective inference\n- `A10`, `A100`, `A100-80GB` - Standard training\u002Finference\n- `L40S` - Excellent cost\u002Fperformance balance (48GB)\n- `H100`, `H200` - High-performance training\n- `B200` - Flagship performance (most powerful)\n\n**Request multiple GPUs:**\n```python\n@app.function(gpu=\"H100:8\")  # 8x H100 GPUs\ndef train_large_model():\n    pass\n```\n\nSee `references\u002Fgpu.md` for GPU selection guidance, CUDA setup, and multi-GPU configuration.\n\n### 4. Configure Resources\n\nRequest CPU cores, memory, and disk for functions.\n\n```python\n@app.function(\n    cpu=8.0,           # 8 physical cores\n    memory=32768,      # 32 GiB RAM\n    ephemeral_disk=10240  # 10 GiB disk\n)\ndef memory_intensive_task():\n    pass\n```\n\nDefault allocation: 0.125 CPU cores, 128 MiB memory. Billing based on reservation or actual usage, whichever is higher.\n\nSee `references\u002Fresources.md` for resource limits and billing details.\n\n### 5. Scale Automatically\n\nModal autoscales functions from zero to thousands of containers based on demand.\n\n**Process inputs in parallel:**\n```python\n@app.function()\ndef analyze_sample(sample_id: int):\n    # Process single sample\n    return result\n\n@app.local_entrypoint()\ndef main():\n    sample_ids = range(1000)\n    # Automatically parallelized across containers\n    results = list(analyze_sample.map(sample_ids))\n```\n\n**Configure autoscaling:**\n```python\n@app.function(\n    max_containers=100,      # Upper limit\n    min_containers=2,        # Keep warm\n    buffer_containers=5      # Idle buffer for bursts\n)\ndef inference():\n    pass\n```\n\nSee `references\u002Fscaling.md` for autoscaling configuration, concurrency, and scaling limits.\n\n### 6. Store Data Persistently\n\nUse Volumes for persistent storage across function invocations.\n\n```python\nvolume = modal.Volume.from_name(\"my-data\", create_if_missing=True)\n\n@app.function(volumes={\"\u002Fdata\": volume})\ndef save_results(data):\n    with open(\"\u002Fdata\u002Fresults.txt\", \"w\") as f:\n        f.write(data)\n    volume.commit()  # Persist changes\n```\n\nVolumes persist data between runs, store model weights, cache datasets, and share data between functions.\n\nSee `references\u002Fvolumes.md` for volume management, commits, and caching patterns.\n\n### 7. Manage Secrets\n\nStore API keys and credentials securely using Modal Secrets.\n\n```python\n@app.function(secrets=[modal.Secret.from_name(\"huggingface\")])\ndef download_model():\n    import os\n    token = os.environ[\"HF_TOKEN\"]\n    # Use token for authentication\n```\n\n**Create secrets in Modal dashboard or via CLI:**\n```bash\nmodal secret create my-secret KEY=value API_TOKEN=xyz\n```\n\nSee `references\u002Fsecrets.md` for secret management and authentication patterns.\n\n### 8. Deploy Web Endpoints\n\nServe HTTP endpoints, APIs, and webhooks with `@modal.web_endpoint()`.\n\n```python\n@app.function()\n@modal.web_endpoint(method=\"POST\")\ndef predict(data: dict):\n    # Process request\n    result = model.predict(data[\"input\"])\n    return {\"prediction\": result}\n```\n\n**Deploy with:**\n```bash\nmodal deploy script.py\n```\n\nModal provides HTTPS URL for the endpoint.\n\nSee `references\u002Fweb-endpoints.md` for FastAPI integration, streaming, authentication, and WebSocket support.\n\n### 9. Schedule Jobs\n\nRun functions on a schedule with cron expressions.\n\n```python\n@app.function(schedule=modal.Cron(\"0 2 * * *\"))  # Daily at 2 AM\ndef daily_backup():\n    # Backup data\n    pass\n\n@app.function(schedule=modal.Period(hours=4))  # Every 4 hours\ndef refresh_cache():\n    # Update cache\n    pass\n```\n\nScheduled functions run automatically without manual invocation.\n\nSee `references\u002Fscheduled-jobs.md` for cron syntax, timezone configuration, and monitoring.\n\n## Common Workflows\n\n### Deploy ML Model for Inference\n\n```python\nimport modal\n\n# Define dependencies\nimage = modal.Image.debian_slim().uv_pip_install(\"torch\", \"transformers\")\napp = modal.App(\"llm-inference\", image=image)\n\n# Download model at build time\n@app.function()\ndef download_model():\n    from transformers import AutoModel\n    AutoModel.from_pretrained(\"bert-base-uncased\")\n\n# Serve model\n@app.cls(gpu=\"L40S\")\nclass Model:\n    @modal.enter()\n    def load_model(self):\n        from transformers import pipeline\n        self.pipe = pipeline(\"text-classification\", device=\"cuda\")\n\n    @modal.method()\n    def predict(self, text: str):\n        return self.pipe(text)\n\n@app.local_entrypoint()\ndef main():\n    model = Model()\n    result = model.predict.remote(\"Modal is great!\")\n    print(result)\n```\n\n### Batch Process Large Dataset\n\n```python\n@app.function(cpu=2.0, memory=4096)\ndef process_file(file_path: str):\n    import pandas as pd\n    df = pd.read_csv(file_path)\n    # Process data\n    return df.shape[0]\n\n@app.local_entrypoint()\ndef main():\n    files = [\"file1.csv\", \"file2.csv\", ...]  # 1000s of files\n    # Automatically parallelized across containers\n    for count in process_file.map(files):\n        print(f\"Processed {count} rows\")\n```\n\n### Train Model on GPU\n\n```python\n@app.function(\n    gpu=\"A100:2\",      # 2x A100 GPUs\n    timeout=3600       # 1 hour timeout\n)\ndef train_model(config: dict):\n    import torch\n    # Multi-GPU training code\n    model = create_model(config)\n    train(model)\n    return metrics\n```\n\n## Reference Documentation\n\nDetailed documentation for specific features:\n\n- **`references\u002Fgetting-started.md`** - Authentication, setup, basic concepts\n- **`references\u002Fimages.md`** - Image building, dependencies, Dockerfiles\n- **`references\u002Ffunctions.md`** - Function patterns, deployment, parameters\n- **`references\u002Fgpu.md`** - GPU types, CUDA, multi-GPU configuration\n- **`references\u002Fresources.md`** - CPU, memory, disk management\n- **`references\u002Fscaling.md`** - Autoscaling, parallel execution, concurrency\n- **`references\u002Fvolumes.md`** - Persistent storage, data management\n- **`references\u002Fsecrets.md`** - Environment variables, authentication\n- **`references\u002Fweb-endpoints.md`** - APIs, webhooks, endpoints\n- **`references\u002Fscheduled-jobs.md`** - Cron jobs, periodic tasks\n- **`references\u002Fexamples.md`** - Common patterns for scientific computing\n\n## Best Practices\n\n1. **Pin dependencies** in `.uv_pip_install()` for reproducible builds\n2. **Use appropriate GPU types** - L40S for inference, H100\u002FA100 for training\n3. **Leverage caching** - Use Volumes for model weights and datasets\n4. **Configure autoscaling** - Set `max_containers` and `min_containers` based on workload\n5. **Import packages in function body** if not available locally\n6. **Use `.map()` for parallel processing** instead of sequential loops\n7. **Store secrets securely** - Never hardcode API keys\n8. **Monitor costs** - Check Modal dashboard for usage and billing\n\n## Troubleshooting\n\n**\"Module not found\" errors:**\n- Add packages to image with `.uv_pip_install(\"package-name\")`\n- Import packages inside function body if not available locally\n\n**GPU not detected:**\n- Verify GPU specification: `@app.function(gpu=\"A100\")`\n- Check CUDA availability: `torch.cuda.is_available()`\n\n**Function timeout:**\n- Increase timeout: `@app.function(timeout=3600)`\n- Default timeout is 5 minutes\n\n**Volume changes not persisting:**\n- Call `volume.commit()` after writing files\n- Verify volume mounted correctly in function decorator\n\nFor additional help, see Modal documentation at https:\u002F\u002Fmodal.com\u002Fdocs or join Modal Slack community.\n",{"name":5,"description":7},"main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 10:12:59",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill modal",{"tool":57,"command":58},"bunx","bunx skills add 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Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[241,244],{"categoryNo":242,"categoryName":243,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},[133,246,185,247,129,248,249,178,250,35,221,251,252,253,254,255,256,24,257,258],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","http api","scheduled"]