[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NH6CYJ6Y9CSRY96VZQJY5":3,"skill-same:01M71NH6CYJ6Y9CSRY96VZQJY5":89},{"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":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M71NH6CYJ6Y9CSRY96VZQJY5","modal-serverless-gpu",null,"Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.","q2805187159\u002FAnvil","q2805187159","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F105276843?v=4","01M71KF4TN999VNJX9FY9QT06A","铁甲勇士宋小宝","01M71KF4V97XXQ7AGES97R4Q9F",3,"MIT","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fcloud\u002Fmodal\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"modal serverless gpu","modal","serverless","gpu","deployment","for","orchestra","platform","GPU Cloud","as","running","infrastructure","automatic","On-Demand GPU","infrastructure management","batch","you","when","access","scaling","2026-11-10 10:56:23",false,"---\nname: modal-serverless-gpu\ndescription: Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [modal>=0.64.0]\nmetadata:\n  hermes:\n    tags: [Infrastructure, Serverless, GPU, Cloud, Deployment, Modal]\n\n---\n\n# Modal Serverless GPU\n\nComprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.\n\n## When to use Modal\n\n**Use Modal when:**\n- Running GPU-intensive ML workloads without managing infrastructure\n- Deploying ML models as auto-scaling APIs\n- Running batch processing jobs (training, inference, data processing)\n- Need pay-per-second GPU pricing without idle costs\n- Prototyping ML applications quickly\n- Running scheduled jobs (cron-like workloads)\n\n**Key features:**\n- **Serverless GPUs**: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand\n- **Python-native**: Define infrastructure in Python code, no YAML\n- **Auto-scaling**: Scale to zero, scale to 100+ GPUs instantly\n- **Sub-second cold starts**: Rust-based infrastructure for fast container launches\n- **Container caching**: Image layers cached for rapid iteration\n- **Web endpoints**: Deploy functions as REST APIs with zero-downtime updates\n\n**Use alternatives instead:**\n- **RunPod**: For longer-running pods with persistent state\n- **Lambda Labs**: For reserved GPU instances\n- **SkyPilot**: For multi-cloud orchestration and cost optimization\n- **Kubernetes**: For complex multi-service architectures\n\n## Quick start\n\n### Installation\n\n```bash\npip install modal\nmodal setup  # Opens browser for authentication\n```\n\n### Hello World with GPU\n\n```python\nimport modal\n\napp = modal.App(\"hello-gpu\")\n\n@app.function(gpu=\"T4\")\ndef gpu_info():\n    import subprocess\n    return subprocess.run([\"nvidia-smi\"], capture_output=True, text=True).stdout\n\n@app.local_entrypoint()\ndef main():\n    print(gpu_info.remote())\n```\n\nRun: `modal run hello_gpu.py`\n\n### Basic inference endpoint\n\n```python\nimport modal\n\napp = modal.App(\"text-generation\")\nimage = modal.Image.debian_slim().pip_install(\"transformers\", \"torch\", \"accelerate\")\n\n@app.cls(gpu=\"A10G\", image=image)\nclass TextGenerator:\n    @modal.enter()\n    def load_model(self):\n        from transformers import pipeline\n        self.pipe = pipeline(\"text-generation\", model=\"gpt2\", device=0)\n\n    @modal.method()\n    def generate(self, prompt: str) -> str:\n        return self.pipe(prompt, max_length=100)[0][\"generated_text\"]\n\n@app.local_entrypoint()\ndef main():\n    print(TextGenerator().generate.remote(\"Hello, world\"))\n```\n\n## Core concepts\n\n### Key components\n\n| Component | Purpose |\n|-----------|---------|\n| `App` | Container for functions and resources |\n| `Function` | Serverless function with compute specs |\n| `Cls` | Class-based functions with lifecycle hooks |\n| `Image` | Container image definition |\n| `Volume` | Persistent storage for models\u002Fdata |\n| `Secret` | Secure credential storage |\n\n### Execution modes\n\n| Command | Description |\n|---------|-------------|\n| `modal run script.py` | Execute and exit |\n| `modal serve script.py` | Development with live reload |\n| `modal deploy script.py` | Persistent cloud deployment |\n\n## GPU configuration\n\n### Available GPUs\n\n| GPU | VRAM | Best For |\n|-----|------|----------|\n| `T4` | 16GB | Budget inference, small models |\n| `L4` | 24GB | Inference, Ada Lovelace arch |\n| `A10G` | 24GB | Training\u002Finference, 3.3x faster than T4 |\n| `L40S` | 48GB | Recommended for inference (best cost\u002Fperf) |\n| `A100-40GB` | 40GB | Large model training |\n| `A100-80GB` | 80GB | Very large models |\n| `H100` | 80GB | Fastest, FP8 + Transformer Engine |\n| `H200` | 141GB | Auto-upgrade from H100, 4.8TB\u002Fs bandwidth |\n| `B200` | Latest | Blackwell architecture |\n\n### GPU specification patterns\n\n```python\n# Single GPU\n@app.function(gpu=\"A100\")\n\n# Specific memory variant\n@app.function(gpu=\"A100-80GB\")\n\n# Multiple GPUs (up to 8)\n@app.function(gpu=\"H100:4\")\n\n# GPU with fallbacks\n@app.function(gpu=[\"H100\", \"A100\", \"L40S\"])\n\n# Any available GPU\n@app.function(gpu=\"any\")\n```\n\n## Container images\n\n```python\n# Basic image with pip\nimage = modal.Image.debian_slim(python_version=\"3.11\").pip_install(\n    \"torch==2.1.0\", \"transformers==4.36.0\", \"accelerate\"\n)\n\n# From CUDA base\nimage = modal.Image.from_registry(\n    \"nvidia\u002Fcuda:12.1.0-cudnn8-devel-ubuntu22.04\",\n    add_python=\"3.11\"\n).pip_install(\"torch\", \"transformers\")\n\n# With system packages\nimage = modal.Image.debian_slim().apt_install(\"git\", \"ffmpeg\").pip_install(\"whisper\")\n```\n\n## Persistent storage\n\n```python\nvolume = modal.Volume.from_name(\"model-cache\", create_if_missing=True)\n\n@app.function(gpu=\"A10G\", volumes={\"\u002Fmodels\": volume})\ndef load_model():\n    import os\n    model_path = \"\u002Fmodels\u002Fllama-7b\"\n    if not os.path.exists(model_path):\n        model = download_model()\n        model.save_pretrained(model_path)\n        volume.commit()  # Persist changes\n    return load_from_path(model_path)\n```\n\n## Web endpoints\n\n### FastAPI endpoint decorator\n\n```python\n@app.function()\n@modal.fastapi_endpoint(method=\"POST\")\ndef predict(text: str) -> dict:\n    return {\"result\": model.predict(text)}\n```\n\n### Full ASGI app\n\n```python\nfrom fastapi import FastAPI\nweb_app = FastAPI()\n\n@web_app.post(\"\u002Fpredict\")\nasync def predict(text: str):\n    return {\"result\": await model.predict.remote.aio(text)}\n\n@app.function()\n@modal.asgi_app()\ndef fastapi_app():\n    return web_app\n```\n\n### Web endpoint types\n\n| Decorator | Use Case |\n|-----------|----------|\n| `@modal.fastapi_endpoint()` | Simple function → API |\n| `@modal.asgi_app()` | Full FastAPI\u002FStarlette apps |\n| `@modal.wsgi_app()` | Django\u002FFlask apps |\n| `@modal.web_server(port)` | Arbitrary HTTP servers |\n\n## Dynamic batching\n\n```python\n@app.function()\n@modal.batched(max_batch_size=32, wait_ms=100)\nasync def batch_predict(inputs: list[str]) -> list[dict]:\n    # Inputs automatically batched\n    return model.batch_predict(inputs)\n```\n\n## Secrets management\n\n```bash\n# Create secret\nmodal secret create huggingface HF_TOKEN=hf_xxx\n```\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```\n\n## Scheduling\n\n```python\n@app.function(schedule=modal.Cron(\"0 0 * * *\"))  # Daily midnight\ndef daily_job():\n    pass\n\n@app.function(schedule=modal.Period(hours=1))\ndef hourly_job():\n    pass\n```\n\n## Performance optimization\n\n### Cold start mitigation\n\n```python\n@app.function(\n    container_idle_timeout=300,  # Keep warm 5 min\n    allow_concurrent_inputs=10,  # Handle concurrent requests\n)\ndef inference():\n    pass\n```\n\n### Model loading best practices\n\n```python\n@app.cls(gpu=\"A100\")\nclass Model:\n    @modal.enter()  # Run once at container start\n    def load(self):\n        self.model = load_model()  # Load during warm-up\n\n    @modal.method()\n    def predict(self, x):\n        return self.model(x)\n```\n\n## Parallel processing\n\n```python\n@app.function()\ndef process_item(item):\n    return expensive_computation(item)\n\n@app.function()\ndef run_parallel():\n    items = list(range(1000))\n    # Fan out to parallel containers\n    results = list(process_item.map(items))\n    return results\n```\n\n## Common configuration\n\n```python\n@app.function(\n    gpu=\"A100\",\n    memory=32768,              # 32GB RAM\n    cpu=4,                     # 4 CPU cores\n    timeout=3600,              # 1 hour max\n    container_idle_timeout=120,# Keep warm 2 min\n    retries=3,                 # Retry on failure\n    concurrency_limit=10,      # Max concurrent containers\n)\ndef my_function():\n    pass\n```\n\n## Debugging\n\n```python\n# Test locally\nif __name__ == \"__main__\":\n    result = my_function.local()\n\n# View logs\n# modal app logs my-app\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Cold start latency | Increase `container_idle_timeout`, use `@modal.enter()` |\n| GPU OOM | Use larger GPU (`A100-80GB`), enable gradient checkpointing |\n| Image build fails | Pin dependency versions, check CUDA compatibility |\n| Timeout errors | Increase `timeout`, add checkpointing |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-GPU, distributed training, cost optimization\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmodal.com\u002Fdocs\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fmodal-labs\u002Fmodal-examples\n- **Pricing**: https:\u002F\u002Fmodal.com\u002Fpricing\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fmodal\n","# Modal Serverless GPU\n\nComprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.\n\n## When to use Modal\n\n**Use Modal when:**\n- Running GPU-intensive ML workloads without managing infrastructure\n- Deploying ML models as auto-scaling APIs\n- Running batch processing jobs (training, inference, data processing)\n- Need pay-per-second GPU pricing without idle costs\n- Prototyping ML applications quickly\n- Running scheduled jobs (cron-like workloads)\n\n**Key features:**\n- **Serverless GPUs**: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand\n- **Python-native**: Define infrastructure in Python code, no YAML\n- **Auto-scaling**: Scale to zero, scale to 100+ GPUs instantly\n- **Sub-second cold starts**: Rust-based infrastructure for fast container launches\n- **Container caching**: Image layers cached for rapid iteration\n- **Web endpoints**: Deploy functions as REST APIs with zero-downtime updates\n\n**Use alternatives instead:**\n- **RunPod**: For longer-running pods with persistent state\n- **Lambda Labs**: For reserved GPU instances\n- **SkyPilot**: For multi-cloud orchestration and cost optimization\n- **Kubernetes**: For complex multi-service architectures\n\n## Quick start\n\n### Installation\n\n```bash\npip install modal\nmodal setup  # Opens browser for authentication\n```\n\n### Hello World with GPU\n\n```python\nimport modal\n\napp = modal.App(\"hello-gpu\")\n\n@app.function(gpu=\"T4\")\ndef gpu_info():\n    import subprocess\n    return subprocess.run([\"nvidia-smi\"], capture_output=True, text=True).stdout\n\n@app.local_entrypoint()\ndef main():\n    print(gpu_info.remote())\n```\n\nRun: `modal run hello_gpu.py`\n\n### Basic inference endpoint\n\n```python\nimport modal\n\napp = modal.App(\"text-generation\")\nimage = modal.Image.debian_slim().pip_install(\"transformers\", \"torch\", \"accelerate\")\n\n@app.cls(gpu=\"A10G\", image=image)\nclass TextGenerator:\n    @modal.enter()\n    def load_model(self):\n        from transformers import pipeline\n        self.pipe = pipeline(\"text-generation\", model=\"gpt2\", device=0)\n\n    @modal.method()\n    def generate(self, prompt: str) -> str:\n        return self.pipe(prompt, max_length=100)[0][\"generated_text\"]\n\n@app.local_entrypoint()\ndef main():\n    print(TextGenerator().generate.remote(\"Hello, world\"))\n```\n\n## Core concepts\n\n### Key components\n\n| Component | Purpose |\n|-----------|---------|\n| `App` | Container for functions and resources |\n| `Function` | Serverless function with compute specs |\n| `Cls` | Class-based functions with lifecycle hooks |\n| `Image` | Container image definition |\n| `Volume` | Persistent storage for models\u002Fdata |\n| `Secret` | Secure credential storage |\n\n### Execution modes\n\n| Command | Description |\n|---------|-------------|\n| `modal run script.py` | Execute and exit |\n| `modal serve script.py` | Development with live reload |\n| `modal deploy script.py` | Persistent cloud deployment |\n\n## GPU configuration\n\n### Available GPUs\n\n| GPU | VRAM | Best For |\n|-----|------|----------|\n| `T4` | 16GB | Budget inference, small models |\n| `L4` | 24GB | Inference, Ada Lovelace arch |\n| `A10G` | 24GB | Training\u002Finference, 3.3x faster than T4 |\n| `L40S` | 48GB | Recommended for inference (best cost\u002Fperf) |\n| `A100-40GB` | 40GB | Large model training |\n| `A100-80GB` | 80GB | Very large models |\n| `H100` | 80GB | Fastest, FP8 + Transformer Engine |\n| `H200` | 141GB | Auto-upgrade from H100, 4.8TB\u002Fs bandwidth |\n| `B200` | Latest | Blackwell architecture |\n\n### GPU specification patterns\n\n```python\n# Single GPU\n@app.function(gpu=\"A100\")\n\n# Specific memory variant\n@app.function(gpu=\"A100-80GB\")\n\n# Multiple GPUs (up to 8)\n@app.function(gpu=\"H100:4\")\n\n# GPU with fallbacks\n@app.function(gpu=[\"H100\", \"A100\", \"L40S\"])\n\n# Any available GPU\n@app.function(gpu=\"any\")\n```\n\n## Container images\n\n```python\n# Basic image with pip\nimage = modal.Image.debian_slim(python_version=\"3.11\").pip_install(\n    \"torch==2.1.0\", \"transformers==4.36.0\", \"accelerate\"\n)\n\n# From CUDA base\nimage = modal.Image.from_registry(\n    \"nvidia\u002Fcuda:12.1.0-cudnn8-devel-ubuntu22.04\",\n    add_python=\"3.11\"\n).pip_install(\"torch\", \"transformers\")\n\n# With system packages\nimage = modal.Image.debian_slim().apt_install(\"git\", \"ffmpeg\").pip_install(\"whisper\")\n```\n\n## Persistent storage\n\n```python\nvolume = modal.Volume.from_name(\"model-cache\", create_if_missing=True)\n\n@app.function(gpu=\"A10G\", volumes={\"\u002Fmodels\": volume})\ndef load_model():\n    import os\n    model_path = \"\u002Fmodels\u002Fllama-7b\"\n    if not os.path.exists(model_path):\n        model = download_model()\n        model.save_pretrained(model_path)\n        volume.commit()  # Persist changes\n    return load_from_path(model_path)\n```\n\n## Web endpoints\n\n### FastAPI endpoint decorator\n\n```python\n@app.function()\n@modal.fastapi_endpoint(method=\"POST\")\ndef predict(text: str) -> dict:\n    return {\"result\": model.predict(text)}\n```\n\n### Full ASGI app\n\n```python\nfrom fastapi import FastAPI\nweb_app = FastAPI()\n\n@web_app.post(\"\u002Fpredict\")\nasync def predict(text: str):\n    return {\"result\": await model.predict.remote.aio(text)}\n\n@app.function()\n@modal.asgi_app()\ndef fastapi_app():\n    return web_app\n```\n\n### Web endpoint types\n\n| Decorator | Use Case |\n|-----------|----------|\n| `@modal.fastapi_endpoint()` | Simple function → API |\n| `@modal.asgi_app()` | Full FastAPI\u002FStarlette apps |\n| `@modal.wsgi_app()` | Django\u002FFlask apps |\n| `@modal.web_server(port)` | Arbitrary HTTP servers |\n\n## Dynamic batching\n\n```python\n@app.function()\n@modal.batched(max_batch_size=32, wait_ms=100)\nasync def batch_predict(inputs: list[str]) -> list[dict]:\n    # Inputs automatically batched\n    return model.batch_predict(inputs)\n```\n\n## Secrets management\n\n```bash\n# Create secret\nmodal secret create huggingface HF_TOKEN=hf_xxx\n```\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```\n\n## Scheduling\n\n```python\n@app.function(schedule=modal.Cron(\"0 0 * * *\"))  # Daily midnight\ndef daily_job():\n    pass\n\n@app.function(schedule=modal.Period(hours=1))\ndef hourly_job():\n    pass\n```\n\n## Performance optimization\n\n### Cold start mitigation\n\n```python\n@app.function(\n    container_idle_timeout=300,  # Keep warm 5 min\n    allow_concurrent_inputs=10,  # Handle concurrent requests\n)\ndef inference():\n    pass\n```\n\n### Model loading best practices\n\n```python\n@app.cls(gpu=\"A100\")\nclass Model:\n    @modal.enter()  # Run once at container start\n    def load(self):\n        self.model = load_model()  # Load during warm-up\n\n    @modal.method()\n    def predict(self, x):\n        return self.model(x)\n```\n\n## Parallel processing\n\n```python\n@app.function()\ndef process_item(item):\n    return expensive_computation(item)\n\n@app.function()\ndef run_parallel():\n    items = list(range(1000))\n    # Fan out to parallel containers\n    results = list(process_item.map(items))\n    return results\n```\n\n## Common configuration\n\n```python\n@app.function(\n    gpu=\"A100\",\n    memory=32768,              # 32GB RAM\n    cpu=4,                     # 4 CPU cores\n    timeout=3600,              # 1 hour max\n    container_idle_timeout=120,# Keep warm 2 min\n    retries=3,                 # Retry on failure\n    concurrency_limit=10,      # Max concurrent containers\n)\ndef my_function():\n    pass\n```\n\n## Debugging\n\n```python\n# Test locally\nif __name__ == \"__main__\":\n    result = my_function.local()\n\n# View logs\n# modal app logs my-app\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Cold start latency | Increase `container_idle_timeout`, use `@modal.enter()` |\n| GPU OOM | Use larger GPU (`A100-80GB`), enable gradient checkpointing |\n| Image build fails | Pin dependency versions, check CUDA compatibility |\n| Timeout errors | Increase `timeout`, add checkpointing |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-GPU, distributed training, cost optimization\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmodal.com\u002Fdocs\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fmodal-labs\u002Fmodal-examples\n- **Pricing**: https:\u002F\u002Fmodal.com\u002Fpricing\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fmodal\n",{"name":5,"author":44,"license":15,"version":45,"metadata":46,"description":7,"dependencies":55},"Orchestra Research","1.0.0",{"hermes":47},{"tags":48},[49,50,51,52,53,54],"Infrastructure","Serverless","GPU","Cloud","Deployment","Modal",[56],"modal>=0.64.0","main","6ffadc67c9777282fa1f9330ec12ac8778064409","skills\u002Fmlops\u002Fcloud\u002Fmodal",0,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil","https:\u002F\u002Fgithub.com\u002Fq2805187159","Independent Researcher\r\n\r\nEmail: q2805187159@gmail.com\r\n\r\nGithub: q2805187159\r\n\r\nResearch Interests:\r\n- LLM Agents\r\n- Tool Routing\r\n- RAG","2026-06-24 18:31:41",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add q2805187159\u002FAnvil --skill modal-serverless-gpu",{"tool":71,"command":72},"bunx","bunx skills add q2805187159\u002FAnvil --skill modal-serverless-gpu",{"tool":74,"command":75},"pnpm","pnpm dlx skills add q2805187159\u002FAnvil --skill modal-serverless-gpu","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fcloud\u002Fmodal\u002FSKILL.md",true,[79,85],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",10903,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fcloud\u002Fmodal\u002Freferences\u002Fadvanced-usage.md",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Ftroubleshooting.md",10516,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fcloud\u002Fmodal\u002Freferences\u002Ftroubleshooting.md",[90,123,155,162,188,210],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":95,"sourceHtmlUrl":96,"categories":97,"keywords":106,"publishedAt":39},"01M71NPWVVMB1AZ8XGKNJFT0YP","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.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FSKILL.md",[98,103],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":104,"categoryName":105,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000009","前端开发",[107,108,109,24,110,111,112,113,114,115,116,117,118,119,120,121,122],"webapp","testing","webapp testing","applications","local","frontend","debugging","toolkit","logs","browser","with","playwright","web","behavior","ui","screenshots",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":137,"publishedAt":39},"01M71NPTQ96H4XSZVZT422BNRB","web-design-guidelines","Review UI code for Web Interface Guidelines compliance. Use when asked to \"review my UI\", \"check accessibility\", \"audit design\", \"review UX\", or \"check my site against best practices\".","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fweb-design-guidelines\u002FSKILL.md",[129,132],{"categoryNo":130,"categoryName":131,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[138,139,119,140,141,24,142,143,144,145,36,146,147,148,149,150,151,152,153,154],"guidelines","design","web design guidelines","web design","vercel","interface","web interface guidelines","use","best practices","file","code","review","check","compliance","audit","ux","accessibility",{"skillNo":156,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":161,"publishedAt":39},"01M71NPS2D31Y2BGFYA9DB7CE4","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fweb-design-guidelines copy\u002FSKILL.md",[159,160],{"categoryNo":130,"categoryName":131,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},[138,139,119,140,141,24,142,143,144,145,36,146,147,148,149,150,151,152,153,154],{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":174,"publishedAt":39},"01M71NPQ9DRT54QSQJ0BHBM3VG","video-generation","Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fvideo-generation\u002FSKILL.md",[168,171],{"categoryNo":169,"categoryName":170,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":172,"categoryName":173,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发",[175,176,177,24,145,36,178,179,180,181,182,183,184,185,186,187],"Video","video generation","generation","image","create","imagine","requests","generate","videos","reference","prompts","user","structured",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":195,"publishedAt":39},"01M71NPN57CT154SWXAS52475X","youtube-downloader","Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3.","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fvideo-downloader\u002FSKILL.md",[194],{"categoryNo":169,"categoryName":170,"taxonomyType":101,"taxonomyTypeDesc":102},[196,197,198,28,199,200,201,145,36,202,117,203,204,205,206,207,183,208,186,209],"youtube","youtube downloader","mp4","downloads","Audio","only","mp3","options","quality","format","settings","grab","formats","download",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":214,"categories":215,"keywords":216,"publishedAt":39},"01M71NPKD95YB8N3J6J78QGFFW","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fverification-before-completion\u002FSKILL.md",[],[217,218,29,219,220,221,222,145,223,36,224,225,226,227,228,229],"verification","verification before completion","evidence","complete","prs","claim","assertions","committing","output","claims","work","about","commands"]