[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NHPR2KBR3GE117R0BDRH5":3,"skill-same:01M71NHPR2KBR3GE117R0BDRH5":91},{"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":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M71NHPR2KBR3GE117R0BDRH5","llama-cpp",null,"Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.","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\u002Finference\u002Fllama-cpp\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"llama cpp","Llama","cpp","orchestra","NVIDIA","Intel GPUs","AMD GPUs","Non-NVIDIA","Edge Deployment","CPU Inference","llama.cpp","GGUF","silicon","CPU","Inference Serving","apple","when","pytorch","memory","bit","2026-11-10 10:56:23",false,"---\nname: llama-cpp\ndescription: Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [llama-cpp-python]\nmetadata:\n  hermes:\n    tags: [Inference Serving, Llama.cpp, CPU Inference, Apple Silicon, Edge Deployment, GGUF, Quantization, Non-NVIDIA, AMD GPUs, Intel GPUs, Embedded]\n\n---\n\n# llama.cpp\n\nPure C\u002FC++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.\n\n## When to use llama.cpp\n\n**Use llama.cpp when:**\n- Running on CPU-only machines\n- Deploying on Apple Silicon (M1\u002FM2\u002FM3\u002FM4)\n- Using AMD or Intel GPUs (no CUDA)\n- Edge deployment (Raspberry Pi, embedded systems)\n- Need simple deployment without Docker\u002FPython\n\n**Use TensorRT-LLM instead when:**\n- Have NVIDIA GPUs (A100\u002FH100)\n- Need maximum throughput (100K+ tok\u002Fs)\n- Running in datacenter with CUDA\n\n**Use vLLM instead when:**\n- Have NVIDIA GPUs\n- Need Python-first API\n- Want PagedAttention\n\n## Quick start\n\n### Installation\n\n```bash\n# macOS\u002FLinux\nbrew install llama.cpp\n\n# Or build from source\ngit clone https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\ncd llama.cpp\nmake\n\n# With Metal (Apple Silicon)\nmake LLAMA_METAL=1\n\n# With CUDA (NVIDIA)\nmake LLAMA_CUDA=1\n\n# With ROCm (AMD)\nmake LLAMA_HIP=1\n```\n\n### Download model\n\n```bash\n# Download from HuggingFace (GGUF format)\nhuggingface-cli download \\\n    TheBloke\u002FLlama-2-7B-Chat-GGUF \\\n    llama-2-7b-chat.Q4_K_M.gguf \\\n    --local-dir models\u002F\n\n# Or convert from HuggingFace\npython convert_hf_to_gguf.py models\u002Fllama-2-7b-chat\u002F\n```\n\n### Run inference\n\n```bash\n# Simple chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    -p \"Explain quantum computing\" \\\n    -n 256  # Max tokens\n\n# Interactive chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --interactive\n```\n\n### Server mode\n\n```bash\n# Start OpenAI-compatible server\n.\u002Fllama-server \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 32  # Offload 32 layers to GPU\n\n# Client request\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"llama-2-7b-chat\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Quantization formats\n\n### GGUF format overview\n\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\n|--------|------|-----------|-------|---------|----------|\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\n\n### Choosing quantization\n\n```bash\n# General use (balanced)\nQ4_K_M  # 4-bit, medium quality\n\n# Maximum speed (more degradation)\nQ2_K or Q3_K_M\n\n# Maximum quality (slower)\nQ6_K or Q8_0\n\n# Very large models (70B, 405B)\nQ3_K_M or Q4_K_S  # Lower bits to fit in memory\n```\n\n## Hardware acceleration\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake LLAMA_METAL=1\n\n# Run with GPU acceleration (automatic)\n.\u002Fllama-cli -m model.gguf -ngl 999  # Offload all layers\n\n# Performance: M3 Max 40-60 tokens\u002Fsec (Llama 2-7B Q4_K_M)\n```\n\n### NVIDIA GPUs (CUDA)\n\n```bash\n# Build with CUDA\nmake LLAMA_CUDA=1\n\n# Offload layers to GPU\n.\u002Fllama-cli -m model.gguf -ngl 35  # Offload 35\u002F40 layers\n\n# Hybrid CPU+GPU for large models\n.\u002Fllama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest\n```\n\n### AMD GPUs (ROCm)\n\n```bash\n# Build with ROCm\nmake LLAMA_HIP=1\n\n# Run with AMD GPU\n.\u002Fllama-cli -m model.gguf -ngl 999\n```\n\n## Common patterns\n\n### Batch processing\n\n```bash\n# Process multiple prompts from file\ncat prompts.txt | .\u002Fllama-cli \\\n    -m model.gguf \\\n    --batch-size 512 \\\n    -n 100\n```\n\n### Constrained generation\n\n```bash\n# JSON output with grammar\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -p \"Generate a person: \" \\\n    --grammar-file grammars\u002Fjson.gbnf\n\n# Outputs valid JSON only\n```\n\n### Context size\n\n```bash\n# Increase context (default 512)\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -c 4096  # 4K context window\n\n# Very long context (if model supports)\n.\u002Fllama-cli -m model.gguf -c 32768  # 32K context\n```\n\n## Performance benchmarks\n\n### CPU performance (Llama 2-7B Q4_K_M)\n\n| CPU | Threads | Speed | Cost |\n|-----|---------|-------|------|\n| Apple M3 Max | 16 | 50 tok\u002Fs | $0 (local) |\n| AMD Ryzen 9 7950X | 32 | 35 tok\u002Fs | $0.50\u002Fhour |\n| Intel i9-13900K | 32 | 30 tok\u002Fs | $0.40\u002Fhour |\n| AWS c7i.16xlarge | 64 | 40 tok\u002Fs | $2.88\u002Fhour |\n\n### GPU acceleration (Llama 2-7B Q4_K_M)\n\n| GPU | Speed | vs CPU | Cost |\n|-----|-------|--------|------|\n| NVIDIA RTX 4090 | 120 tok\u002Fs | 3-4× | $0 (local) |\n| NVIDIA A10 | 80 tok\u002Fs | 2-3× | $1.00\u002Fhour |\n| AMD MI250 | 70 tok\u002Fs | 2× | $2.00\u002Fhour |\n| Apple M3 Max (Metal) | 50 tok\u002Fs | ~Same | $0 (local) |\n\n## Supported models\n\n**LLaMA family**:\n- Llama 2 (7B, 13B, 70B)\n- Llama 3 (8B, 70B, 405B)\n- Code Llama\n\n**Mistral family**:\n- Mistral 7B\n- Mixtral 8x7B, 8x22B\n\n**Other**:\n- Falcon, BLOOM, GPT-J\n- Phi-3, Gemma, Qwen\n- LLaVA (vision), Whisper (audio)\n\n**Find models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n\n## References\n\n- **[Quantization Guide](references\u002Fquantization.md)** - GGUF formats, conversion, quality comparison\n- **[Server Deployment](references\u002Fserver.md)** - API endpoints, Docker, monitoring\n- **[Optimization](references\u002Foptimization.md)** - Performance tuning, hybrid CPU+GPU\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fllama-cpp\n","# llama.cpp\n\nPure C\u002FC++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.\n\n## When to use llama.cpp\n\n**Use llama.cpp when:**\n- Running on CPU-only machines\n- Deploying on Apple Silicon (M1\u002FM2\u002FM3\u002FM4)\n- Using AMD or Intel GPUs (no CUDA)\n- Edge deployment (Raspberry Pi, embedded systems)\n- Need simple deployment without Docker\u002FPython\n\n**Use TensorRT-LLM instead when:**\n- Have NVIDIA GPUs (A100\u002FH100)\n- Need maximum throughput (100K+ tok\u002Fs)\n- Running in datacenter with CUDA\n\n**Use vLLM instead when:**\n- Have NVIDIA GPUs\n- Need Python-first API\n- Want PagedAttention\n\n## Quick start\n\n### Installation\n\n```bash\n# macOS\u002FLinux\nbrew install llama.cpp\n\n# Or build from source\ngit clone https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\ncd llama.cpp\nmake\n\n# With Metal (Apple Silicon)\nmake LLAMA_METAL=1\n\n# With CUDA (NVIDIA)\nmake LLAMA_CUDA=1\n\n# With ROCm (AMD)\nmake LLAMA_HIP=1\n```\n\n### Download model\n\n```bash\n# Download from HuggingFace (GGUF format)\nhuggingface-cli download \\\n    TheBloke\u002FLlama-2-7B-Chat-GGUF \\\n    llama-2-7b-chat.Q4_K_M.gguf \\\n    --local-dir models\u002F\n\n# Or convert from HuggingFace\npython convert_hf_to_gguf.py models\u002Fllama-2-7b-chat\u002F\n```\n\n### Run inference\n\n```bash\n# Simple chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    -p \"Explain quantum computing\" \\\n    -n 256  # Max tokens\n\n# Interactive chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --interactive\n```\n\n### Server mode\n\n```bash\n# Start OpenAI-compatible server\n.\u002Fllama-server \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 32  # Offload 32 layers to GPU\n\n# Client request\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"llama-2-7b-chat\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Quantization formats\n\n### GGUF format overview\n\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\n|--------|------|-----------|-------|---------|----------|\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\n\n### Choosing quantization\n\n```bash\n# General use (balanced)\nQ4_K_M  # 4-bit, medium quality\n\n# Maximum speed (more degradation)\nQ2_K or Q3_K_M\n\n# Maximum quality (slower)\nQ6_K or Q8_0\n\n# Very large models (70B, 405B)\nQ3_K_M or Q4_K_S  # Lower bits to fit in memory\n```\n\n## Hardware acceleration\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake LLAMA_METAL=1\n\n# Run with GPU acceleration (automatic)\n.\u002Fllama-cli -m model.gguf -ngl 999  # Offload all layers\n\n# Performance: M3 Max 40-60 tokens\u002Fsec (Llama 2-7B Q4_K_M)\n```\n\n### NVIDIA GPUs (CUDA)\n\n```bash\n# Build with CUDA\nmake LLAMA_CUDA=1\n\n# Offload layers to GPU\n.\u002Fllama-cli -m model.gguf -ngl 35  # Offload 35\u002F40 layers\n\n# Hybrid CPU+GPU for large models\n.\u002Fllama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest\n```\n\n### AMD GPUs (ROCm)\n\n```bash\n# Build with ROCm\nmake LLAMA_HIP=1\n\n# Run with AMD GPU\n.\u002Fllama-cli -m model.gguf -ngl 999\n```\n\n## Common patterns\n\n### Batch processing\n\n```bash\n# Process multiple prompts from file\ncat prompts.txt | .\u002Fllama-cli \\\n    -m model.gguf \\\n    --batch-size 512 \\\n    -n 100\n```\n\n### Constrained generation\n\n```bash\n# JSON output with grammar\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -p \"Generate a person: \" \\\n    --grammar-file grammars\u002Fjson.gbnf\n\n# Outputs valid JSON only\n```\n\n### Context size\n\n```bash\n# Increase context (default 512)\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -c 4096  # 4K context window\n\n# Very long context (if model supports)\n.\u002Fllama-cli -m model.gguf -c 32768  # 32K context\n```\n\n## Performance benchmarks\n\n### CPU performance (Llama 2-7B Q4_K_M)\n\n| CPU | Threads | Speed | Cost |\n|-----|---------|-------|------|\n| Apple M3 Max | 16 | 50 tok\u002Fs | $0 (local) |\n| AMD Ryzen 9 7950X | 32 | 35 tok\u002Fs | $0.50\u002Fhour |\n| Intel i9-13900K | 32 | 30 tok\u002Fs | $0.40\u002Fhour |\n| AWS c7i.16xlarge | 64 | 40 tok\u002Fs | $2.88\u002Fhour |\n\n### GPU acceleration (Llama 2-7B Q4_K_M)\n\n| GPU | Speed | vs CPU | Cost |\n|-----|-------|--------|------|\n| NVIDIA RTX 4090 | 120 tok\u002Fs | 3-4× | $0 (local) |\n| NVIDIA A10 | 80 tok\u002Fs | 2-3× | $1.00\u002Fhour |\n| AMD MI250 | 70 tok\u002Fs | 2× | $2.00\u002Fhour |\n| Apple M3 Max (Metal) | 50 tok\u002Fs | ~Same | $0 (local) |\n\n## Supported models\n\n**LLaMA family**:\n- Llama 2 (7B, 13B, 70B)\n- Llama 3 (8B, 70B, 405B)\n- Code Llama\n\n**Mistral family**:\n- Mistral 7B\n- Mixtral 8x7B, 8x22B\n\n**Other**:\n- Falcon, BLOOM, GPT-J\n- Phi-3, Gemma, Qwen\n- LLaVA (vision), Whisper (audio)\n\n**Find models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n\n## References\n\n- **[Quantization Guide](references\u002Fquantization.md)** - GGUF formats, conversion, quality comparison\n- **[Server Deployment](references\u002Fserver.md)** - API endpoints, Docker, monitoring\n- **[Optimization](references\u002Foptimization.md)** - Performance tuning, hybrid CPU+GPU\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fllama-cpp\n",{"name":5,"author":44,"license":15,"version":45,"metadata":46,"description":7,"dependencies":53},"Orchestra Research","1.0.0",{"hermes":47},{"tags":48},[33,49,28,50,27,30,51,26,25,24,52],"Llama.cpp","Apple Silicon","Quantization","Embedded",[54],"llama-cpp-python","main","6ffadc67c9777282fa1f9330ec12ac8778064409","skills\u002Fmlops\u002Finference\u002Fllama-cpp",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":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add q2805187159\u002FAnvil --skill llama-cpp",{"tool":69,"command":70},"bunx","bunx skills add q2805187159\u002FAnvil --skill llama-cpp",{"tool":72,"command":73},"pnpm","pnpm dlx skills add q2805187159\u002FAnvil --skill llama-cpp","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Finference\u002Fllama-cpp\u002FSKILL.md",true,[77,83,87],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Foptimization.md","REFERENCE","参考文档",1659,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Finference\u002Fllama-cpp\u002Freferences\u002Foptimization.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fquantization.md",4956,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Finference\u002Fllama-cpp\u002Freferences\u002Fquantization.md",{"path":88,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fserver.md",2259,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Finference\u002Fllama-cpp\u002Freferences\u002Fserver.md",[92,126,158,165,191,214],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":96,"licenseFileUrl":97,"sourceHtmlUrl":98,"categories":99,"keywords":108,"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",[100,105],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":106,"categoryName":107,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000009","前端开发",[109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125],"webapp","testing","webapp testing","for","applications","local","frontend","debugging","toolkit","logs","browser","with","playwright","web","behavior","ui","screenshots",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":140,"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",[132,135],{"categoryNo":133,"categoryName":134,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[141,142,122,143,144,112,145,146,147,148,35,149,150,151,152,153,154,155,156,157],"guidelines","design","web design guidelines","web design","vercel","interface","web interface guidelines","use","best practices","file","code","review","check","compliance","audit","ux","accessibility",{"skillNo":159,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":164,"publishedAt":39},"01M71NPS2D31Y2BGFYA9DB7CE4","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fweb-design-guidelines copy\u002FSKILL.md",[162,163],{"categoryNo":133,"categoryName":134,"taxonomyType":103,"taxonomyTypeDesc":104},{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},[141,142,122,143,144,112,145,146,147,148,35,149,150,151,152,153,154,155,156,157],{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":177,"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",[171,174],{"categoryNo":172,"categoryName":173,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":175,"categoryName":176,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000023","Skill与智能体开发",[178,179,180,112,148,35,181,182,183,184,185,186,187,188,189,190],"Video","video generation","generation","image","create","imagine","requests","generate","videos","reference","prompts","user","structured",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":198,"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",[197],{"categoryNo":172,"categoryName":173,"taxonomyType":103,"taxonomyTypeDesc":104},[199,200,201,202,203,204,205,148,35,206,120,207,208,209,210,211,186,212,189,213],"youtube","youtube downloader","mp4","as","downloads","Audio","only","mp3","options","quality","format","settings","grab","formats","download",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":220,"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",[],[221,222,223,224,225,226,227,148,228,35,229,230,231,232,233,234],"verification","verification before completion","running","evidence","complete","prs","claim","assertions","committing","output","claims","work","about","commands"]