[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG7YVGH173SJ1BWK3KG8VM":3,"skill-same:01M6XG7YVGH173SJ1BWK3KG8VM":92},{"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":58,"commitSha":59,"skillDirectory":60,"forksCount":14,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6XG7YVGH173SJ1BWK3KG8VM","tensorrt-llm",null,"Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.","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\u002Ftensorrt-llm\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tensorrt","tensorrt llm","scaling","when","batching","for","you","throughput","H100","synthetic","latency","pytorch","use","llm inference","Inference Serving","NVIDIA","FP8","Optimization","deployment","ml","2026-11-10 10:12:30",false,"---\nname: tensorrt-llm\ndescription: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.\ncategory: ml-inference\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [Inference Serving, TensorRT-LLM, NVIDIA, Inference Optimization, High Throughput, Low Latency, Production, FP8, INT4, In-Flight Batching, Multi-GPU]\ndependencies: [tensorrt-llm, torch]\n---\n\n# TensorRT-LLM\n\nNVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.\n\n## When to use TensorRT-LLM\n\n**Use TensorRT-LLM when:**\n- Deploying on NVIDIA GPUs (A100, H100, GB200)\n- Need maximum throughput (24,000+ tokens\u002Fsec on Llama 3)\n- Require low latency for real-time applications\n- Working with quantized models (FP8, INT4, FP4)\n- Scaling across multiple GPUs or nodes\n\n**Use vLLM instead when:**\n- Need simpler setup and Python-first API\n- Want PagedAttention without TensorRT compilation\n- Working with AMD GPUs or non-NVIDIA hardware\n\n**Use llama.cpp instead when:**\n- Deploying on CPU or Apple Silicon\n- Need edge deployment without NVIDIA GPUs\n- Want simpler GGUF quantization format\n\n## Quick start\n\n### Installation\n\n```bash\n# Docker (recommended)\ndocker pull nvidia\u002Ftensorrt_llm:latest\n\n# pip install\npip install tensorrt_llm==1.2.0rc3\n\n# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12\n```\n\n### Basic inference\n\n```python\nfrom tensorrt_llm import LLM, SamplingParams\n\n# Initialize model\nllm = LLM(model=\"meta-llama\u002FMeta-Llama-3-8B\")\n\n# Configure sampling\nsampling_params = SamplingParams(\n    max_tokens=100,\n    temperature=0.7,\n    top_p=0.9\n)\n\n# Generate\nprompts = [\"Explain quantum computing\"]\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    print(output.text)\n```\n\n### Serving with trtllm-serve\n\n```bash\n# Start server (automatic model download and compilation)\ntrtllm-serve meta-llama\u002FMeta-Llama-3-8B \\\n    --tp_size 4 \\              # Tensor parallelism (4 GPUs)\n    --max_batch_size 256 \\\n    --max_num_tokens 4096\n\n# Client request\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FMeta-Llama-3-8B\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Key features\n\n### Performance optimizations\n- **In-flight batching**: Dynamic batching during generation\n- **Paged KV cache**: Efficient memory management\n- **Flash Attention**: Optimized attention kernels\n- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference\n- **CUDA graphs**: Reduced kernel launch overhead\n\n### Parallelism\n- **Tensor parallelism (TP)**: Split model across GPUs\n- **Pipeline parallelism (PP)**: Layer-wise distribution\n- **Expert parallelism**: For Mixture-of-Experts models\n- **Multi-node**: Scale beyond single machine\n\n### Advanced features\n- **Speculative decoding**: Faster generation with draft models\n- **LoRA serving**: Efficient multi-adapter deployment\n- **Disaggregated serving**: Separate prefill and generation\n\n## Common patterns\n\n### Quantized model (FP8)\n\n```python\nfrom tensorrt_llm import LLM\n\n# Load FP8 quantized model (2× faster, 50% memory)\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-70B\",\n    dtype=\"fp8\",\n    max_num_tokens=8192\n)\n\n# Inference same as before\noutputs = llm.generate([\"Summarize this article...\"])\n```\n\n### Multi-GPU deployment\n\n```python\n# Tensor parallelism across 8 GPUs\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-405B\",\n    tensor_parallel_size=8,\n    dtype=\"fp8\"\n)\n```\n\n### Batch inference\n\n```python\n# Process 100 prompts efficiently\nprompts = [f\"Question {i}: ...\" for i in range(100)]\n\noutputs = llm.generate(\n    prompts,\n    sampling_params=SamplingParams(max_tokens=200)\n)\n\n# Automatic in-flight batching for maximum throughput\n```\n\n## Performance benchmarks\n\n**Meta Llama 3-8B** (H100 GPU):\n- Throughput: 24,000 tokens\u002Fsec\n- Latency: ~10ms per token\n- vs PyTorch: **100× faster**\n\n**Llama 3-70B** (8× A100 80GB):\n- FP8 quantization: 2× faster than FP16\n- Memory: 50% reduction with FP8\n\n## Supported models\n\n- **LLaMA family**: Llama 2, Llama 3, CodeLlama\n- **GPT family**: GPT-2, GPT-J, GPT-NeoX\n- **Qwen**: Qwen, Qwen2, QwQ\n- **DeepSeek**: DeepSeek-V2, DeepSeek-V3\n- **Mixtral**: Mixtral-8x7B, Mixtral-8x22B\n- **Vision**: LLaVA, Phi-3-vision\n- **100+ models** on HuggingFace\n\n## References\n\n- **[Optimization Guide](references\u002Foptimization.md)** - Quantization, batching, KV cache tuning\n- **[Multi-GPU Setup](references\u002Fmulti-gpu.md)** - Tensor\u002Fpipeline parallelism, multi-node\n- **[Serving Guide](references\u002Fserving.md)** - Production deployment, monitoring, autoscaling\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fnvidia.github.io\u002FTensorRT-LLM\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=tensorrt_llm\n\n\n","# TensorRT-LLM\n\nNVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.\n\n## When to use TensorRT-LLM\n\n**Use TensorRT-LLM when:**\n- Deploying on NVIDIA GPUs (A100, H100, GB200)\n- Need maximum throughput (24,000+ tokens\u002Fsec on Llama 3)\n- Require low latency for real-time applications\n- Working with quantized models (FP8, INT4, FP4)\n- Scaling across multiple GPUs or nodes\n\n**Use vLLM instead when:**\n- Need simpler setup and Python-first API\n- Want PagedAttention without TensorRT compilation\n- Working with AMD GPUs or non-NVIDIA hardware\n\n**Use llama.cpp instead when:**\n- Deploying on CPU or Apple Silicon\n- Need edge deployment without NVIDIA GPUs\n- Want simpler GGUF quantization format\n\n## Quick start\n\n### Installation\n\n```bash\n# Docker (recommended)\ndocker pull nvidia\u002Ftensorrt_llm:latest\n\n# pip install\npip install tensorrt_llm==1.2.0rc3\n\n# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12\n```\n\n### Basic inference\n\n```python\nfrom tensorrt_llm import LLM, SamplingParams\n\n# Initialize model\nllm = LLM(model=\"meta-llama\u002FMeta-Llama-3-8B\")\n\n# Configure sampling\nsampling_params = SamplingParams(\n    max_tokens=100,\n    temperature=0.7,\n    top_p=0.9\n)\n\n# Generate\nprompts = [\"Explain quantum computing\"]\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    print(output.text)\n```\n\n### Serving with trtllm-serve\n\n```bash\n# Start server (automatic model download and compilation)\ntrtllm-serve meta-llama\u002FMeta-Llama-3-8B \\\n    --tp_size 4 \\              # Tensor parallelism (4 GPUs)\n    --max_batch_size 256 \\\n    --max_num_tokens 4096\n\n# Client request\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FMeta-Llama-3-8B\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Key features\n\n### Performance optimizations\n- **In-flight batching**: Dynamic batching during generation\n- **Paged KV cache**: Efficient memory management\n- **Flash Attention**: Optimized attention kernels\n- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference\n- **CUDA graphs**: Reduced kernel launch overhead\n\n### Parallelism\n- **Tensor parallelism (TP)**: Split model across GPUs\n- **Pipeline parallelism (PP)**: Layer-wise distribution\n- **Expert parallelism**: For Mixture-of-Experts models\n- **Multi-node**: Scale beyond single machine\n\n### Advanced features\n- **Speculative decoding**: Faster generation with draft models\n- **LoRA serving**: Efficient multi-adapter deployment\n- **Disaggregated serving**: Separate prefill and generation\n\n## Common patterns\n\n### Quantized model (FP8)\n\n```python\nfrom tensorrt_llm import LLM\n\n# Load FP8 quantized model (2× faster, 50% memory)\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-70B\",\n    dtype=\"fp8\",\n    max_num_tokens=8192\n)\n\n# Inference same as before\noutputs = llm.generate([\"Summarize this article...\"])\n```\n\n### Multi-GPU deployment\n\n```python\n# Tensor parallelism across 8 GPUs\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-405B\",\n    tensor_parallel_size=8,\n    dtype=\"fp8\"\n)\n```\n\n### Batch inference\n\n```python\n# Process 100 prompts efficiently\nprompts = [f\"Question {i}: ...\" for i in range(100)]\n\noutputs = llm.generate(\n    prompts,\n    sampling_params=SamplingParams(max_tokens=200)\n)\n\n# Automatic in-flight batching for maximum throughput\n```\n\n## Performance benchmarks\n\n**Meta Llama 3-8B** (H100 GPU):\n- Throughput: 24,000 tokens\u002Fsec\n- Latency: ~10ms per token\n- vs PyTorch: **100× faster**\n\n**Llama 3-70B** (8× A100 80GB):\n- FP8 quantization: 2× faster than FP16\n- Memory: 50% reduction with FP8\n\n## Supported models\n\n- **LLaMA family**: Llama 2, Llama 3, CodeLlama\n- **GPT family**: GPT-2, GPT-J, GPT-NeoX\n- **Qwen**: Qwen, Qwen2, QwQ\n- **DeepSeek**: DeepSeek-V2, DeepSeek-V3\n- **Mixtral**: Mixtral-8x7B, Mixtral-8x22B\n- **Vision**: LLaVA, Phi-3-vision\n- **100+ models** on HuggingFace\n\n## References\n\n- **[Optimization Guide](references\u002Foptimization.md)** - Quantization, batching, KV cache tuning\n- **[Multi-GPU Setup](references\u002Fmulti-gpu.md)** - Tensor\u002Fpipeline parallelism, multi-node\n- **[Serving Guide](references\u002Fserving.md)** - Production deployment, monitoring, autoscaling\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fnvidia.github.io\u002FTensorRT-LLM\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=tensorrt_llm\n\n\n",{"name":5,"tags":44,"author":53,"license":15,"version":54,"category":55,"description":7,"dependencies":56},[33,45,34,46,47,48,49,35,50,51,52],"TensorRT-LLM","Inference Optimization","High Throughput","Low Latency","Production","INT4","In-Flight Batching","Multi-GPU","Synthetic Sciences","1.0.0","ml-inference",[5,57],"torch","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Ftensorrt-llm","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add fadrienne\u002FMyGitHub --skill tensorrt-llm",{"tool":70,"command":71},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill tensorrt-llm",{"tool":73,"command":74},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill tensorrt-llm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Ftensorrt-llm\u002FSKILL.md",true,[78,84,88],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fmulti-gpu.md","REFERENCE","参考文档",6699,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Ftensorrt-llm\u002Freferences\u002Fmulti-gpu.md",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Foptimization.md",5613,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Ftensorrt-llm\u002Freferences\u002Foptimization.md",{"path":89,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fserving.md",9798,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Ftensorrt-llm\u002Freferences\u002Fserving.md",[93,124,148,182,209,236],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":104,"publishedAt":39},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. 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Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[105,106,107,108,109,110,111,112,22,113,114,115,116,117,118,119,120,121,122,123],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":133,"publishedAt":39},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[130],{"categoryNo":131,"categoryName":132,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000008","测试与质量",[115,134,105,135,136,137,22,25,138,139,24,140,31,141,142,143,144,145,146,147],"statistical analysis","academic research","implementing","selection","reporting","data","tests","with","test","research","results","statsmodels","models","help",{"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":163,"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",[154,157,158],{"categoryNo":155,"categoryName":156,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":159,"categoryName":160,"taxonomyType":161,"taxonomyTypeDesc":162},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[164,165,166,167,168,169,170,171,172,173,113,174,175,176,177,178,179,180,138,181],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":192,"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",[188,191],{"categoryNo":189,"categoryName":190,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[193,165,194,195,196,197,198,199,200,172,22,201,202,203,204,205,206,207,24,208],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":221,"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",[215,218],{"categoryNo":216,"categoryName":217,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":219,"categoryName":220,"taxonomyType":161,"taxonomyTypeDesc":162},"01JBSEEDCAT000000000000030","开发工程师",[222,223,143,224,225,226,227,112,228,22,113,115,229,230,231,232,233,234,180,235],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":237,"name":238,"nameZh":6,"description":239,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":240,"licenseFileUrl":6,"sourceHtmlUrl":241,"categories":242,"keywords":249,"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",[243,246,247,248],{"categoryNo":244,"categoryName":245,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":155,"categoryName":156,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":159,"categoryName":160,"taxonomyType":161,"taxonomyTypeDesc":162},[164,250,251,252,253,254,193,255,256,22,113,115,257,258,259,260,261,262,165,180],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]