[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFGJ7DM67YKF9HC2CPF3SX":3,"skill-same:01M6AFGJ7DM67YKF9HC2CPF3SX":118},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6AFGJ7DM67YKF9HC2CPF3SX","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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33286,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"use","running","when","autoscaling","python code","with","GPU Acceleration","processing","scaling","compute","scheduling","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","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal",3909,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 11:53:30",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add davila7\u002Fclaude-code-templates --skill modal",{"tool":60,"command":61},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill modal",{"tool":63,"command":64},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill modal","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002FSKILL.md",true,[68,74,78,82,86,90,94,98,102,106,110,114],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fapi_reference.md","REFERENCE","参考文档",956,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fapi_reference.md",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fexamples.md",9782,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fexamples.md",{"path":79,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Ffunctions.md",4457,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Ffunctions.md",{"path":83,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fgetting-started.md",1984,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fgetting-started.md",{"path":87,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fgpu.md",3551,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fgpu.md",{"path":91,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fimages.md",5414,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fimages.md",{"path":95,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fresources.md",2681,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fresources.md",{"path":99,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fscaling.md",5354,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fscaling.md",{"path":103,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fscheduled-jobs.md",6032,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fscheduled-jobs.md",{"path":107,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fsecrets.md",3922,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fsecrets.md",{"path":111,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":112,"sourceHtmlUrl":113},"references\u002Fvolumes.md",6081,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fvolumes.md",{"path":115,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":116,"sourceHtmlUrl":117},"references\u002Fweb-endpoints.md",6928,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmodal\u002Freferences\u002Fweb-endpoints.md",[119,148,174,207,242,266],{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":130,"publishedAt":39},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[125],{"categoryNo":126,"categoryName":127,"taxonomyType":128,"taxonomyTypeDesc":129},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[131,132,133,134,135,136,137,138,19,21,139,24,140,141,142,143,144,145,146,147],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":39},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[154],{"categoryNo":126,"categoryName":127,"taxonomyType":128,"taxonomyTypeDesc":129},[156,157,158,159,160,161,162,163,164,165,166,21,34,167,168,169,170,171,172,173],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":189,"publishedAt":39},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[180,183,184],{"categoryNo":181,"categoryName":182,"taxonomyType":128,"taxonomyTypeDesc":129},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":126,"categoryName":127,"taxonomyType":128,"taxonomyTypeDesc":129},{"categoryNo":185,"categoryName":186,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[190,191,192,193,194,195,196,136,197,198,199,200,201,202,203,19,204,205,21,206],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":211,"licenseFileUrl":212,"sourceHtmlUrl":213,"categories":214,"keywords":218,"publishedAt":39},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[215],{"categoryNo":216,"categoryName":217,"taxonomyType":128,"taxonomyTypeDesc":129},"01JBSEEDCAT000000000000009","前端开发",[219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,136,241],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":243,"name":244,"nameZh":6,"description":245,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":211,"licenseFileUrl":246,"sourceHtmlUrl":247,"categories":248,"keywords":253,"publishedAt":39},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[249,252],{"categoryNo":250,"categoryName":251,"taxonomyType":128,"taxonomyTypeDesc":129},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":216,"categoryName":217,"taxonomyType":128,"taxonomyTypeDesc":129},[254,255,256,257,258,220,259,229,230,260,261,262,263,136,264,24,265],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":267,"name":268,"nameZh":6,"description":269,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":211,"licenseFileUrl":270,"sourceHtmlUrl":271,"categories":272,"keywords":280,"publishedAt":39},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[273,276,277],{"categoryNo":274,"categoryName":275,"taxonomyType":128,"taxonomyTypeDesc":129},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":126,"categoryName":127,"taxonomyType":128,"taxonomyTypeDesc":129},{"categoryNo":278,"categoryName":279,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000036","内容创作者",[281,282,283,284,35,285,286,287,222,288,232,34,289,290,291,292,293,202,294,21,295,296,297,298,299,145,300],"discernment nudge","nudge","interpretation","help","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]