[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG71DGBHT3N3HN35AFA4AW":3,"skill-same:01M6XG71DGBHT3N3HN35AFA4AW":88},{"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":54,"commitSha":55,"skillDirectory":56,"forksCount":14,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6XG71DGBHT3N3HN35AFA4AW","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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"cpp","Llama","llama cpp","silicon","when","Intel GPUs","AMD GPUs","Non-NVIDIA","Edge Deployment","CPU Inference","llama.cpp","GGUF","CPU","memory","synthetic","bit","apple","Inference Serving","NVIDIA","pytorch","2026-11-10 10:12:30",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.\ncategory: ml-inference\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [Inference Serving, Llama.cpp, CPU Inference, Apple Silicon, Edge Deployment, GGUF, Quantization, Non-NVIDIA, AMD GPUs, Intel GPUs, Embedded]\ndependencies: [llama-cpp-python]\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\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\n\n",{"name":5,"tags":44,"author":49,"license":15,"version":50,"category":51,"description":7,"dependencies":52},[36,45,28,46,27,30,47,26,25,24,48],"Llama.cpp","Apple Silicon","Quantization","Embedded","Synthetic Sciences","1.0.0","ml-inference",[53],"llama-cpp-python","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add fadrienne\u002FMyGitHub --skill llama-cpp",{"tool":66,"command":67},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill llama-cpp",{"tool":69,"command":70},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill llama-cpp","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002FSKILL.md",true,[74,80,84],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Foptimization.md","REFERENCE","参考文档",1659,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002Freferences\u002Foptimization.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fquantization.md",4956,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002Freferences\u002Fquantization.md",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fserver.md",2259,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002Freferences\u002Fserver.md",[89,120,147,181,208,234],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":100,"publishedAt":39},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. 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For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[126],{"categoryNo":127,"categoryName":128,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000008","测试与质量",[111,130,101,131,132,133,23,134,135,136,137,138,139,140,141,142,143,144,145,146],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":162,"publishedAt":39},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[153,156,157],{"categoryNo":154,"categoryName":155,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[163,164,165,166,167,168,169,170,171,172,109,173,174,175,176,177,178,179,135,180],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":191,"publishedAt":39},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[187,190],{"categoryNo":188,"categoryName":189,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},[192,164,193,194,195,196,197,198,199,171,23,200,201,202,203,204,205,206,137,207],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":220,"publishedAt":39},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[214,217],{"categoryNo":215,"categoryName":216,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":218,"categoryName":219,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000030","开发工程师",[221,222,142,223,32,224,225,108,226,23,109,111,227,228,229,230,231,232,179,233],"code","research code","which","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":238,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":247,"publishedAt":39},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[241,244,245,246],{"categoryNo":242,"categoryName":243,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":154,"categoryName":155,"taxonomyType":98,"taxonomyTypeDesc":99},{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},[163,248,249,250,251,252,192,253,254,23,109,111,255,256,257,258,259,260,164,179],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]