[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XGCN64MH75T51BMMJV3KZN":3,"skill-same:01M6XGCN64MH75T51BMMJV3KZN":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},"01M6XGCN64MH75T51BMMJV3KZN","nanogpt",null,"Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).","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-training\u002Fnanogpt\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"NanoGPT","synthetic","scratch","tiktoken","for","WandB","Transformer","GPT-2","Model Architecture","CPU","architecture","clean","karpathy","shakespeare","model","learning","Transformers","code","training","ml","2026-11-10 10:12:30",false,"---\nname: nanogpt\ndescription: Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).\ncategory: ml-training\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [Model Architecture, NanoGPT, GPT-2, Educational, Andrej Karpathy, Transformer, Minimalist, From Scratch, Training]\ndependencies: [torch, transformers, datasets, tiktoken, wandb]\n---\n\n# nanoGPT - Minimalist GPT Training\n\n## Quick start\n\nnanoGPT is a simplified GPT implementation designed for learning and experimentation.\n\n**Installation**:\n```bash\npip install torch numpy transformers datasets tiktoken wandb tqdm\n```\n\n**Train on Shakespeare** (CPU-friendly):\n```bash\n# Prepare data\npython data\u002Fshakespeare_char\u002Fprepare.py\n\n# Train (5 minutes on CPU)\npython train.py config\u002Ftrain_shakespeare_char.py\n\n# Generate text\npython sample.py --out_dir=out-shakespeare-char\n```\n\n**Output**:\n```\nROMEO:\nWhat say'st thou? Shall I speak, and be a man?\n\nJULIET:\nI am afeard, and yet I'll speak; for thou art\nOne that hath been a man, and yet I know not\nWhat thou art.\n```\n\n## Common workflows\n\n### Workflow 1: Character-level Shakespeare\n\n**Complete training pipeline**:\n```bash\n# Step 1: Prepare data (creates train.bin, val.bin)\npython data\u002Fshakespeare_char\u002Fprepare.py\n\n# Step 2: Train small model\npython train.py config\u002Ftrain_shakespeare_char.py\n\n# Step 3: Generate text\npython sample.py --out_dir=out-shakespeare-char\n```\n\n**Config** (`config\u002Ftrain_shakespeare_char.py`):\n```python\n# Model config\nn_layer = 6          # 6 transformer layers\nn_head = 6           # 6 attention heads\nn_embd = 384         # 384-dim embeddings\nblock_size = 256     # 256 char context\n\n# Training config\nbatch_size = 64\nlearning_rate = 1e-3\nmax_iters = 5000\neval_interval = 500\n\n# Hardware\ndevice = 'cpu'  # Or 'cuda'\ncompile = False # Set True for PyTorch 2.0\n```\n\n**Training time**: ~5 minutes (CPU), ~1 minute (GPU)\n\n### Workflow 2: Reproduce GPT-2 (124M)\n\n**Multi-GPU training on OpenWebText**:\n```bash\n# Step 1: Prepare OpenWebText (takes ~1 hour)\npython data\u002Fopenwebtext\u002Fprepare.py\n\n# Step 2: Train GPT-2 124M with DDP (8 GPUs)\ntorchrun --standalone --nproc_per_node=8 \\\n  train.py config\u002Ftrain_gpt2.py\n\n# Step 3: Sample from trained model\npython sample.py --out_dir=out\n```\n\n**Config** (`config\u002Ftrain_gpt2.py`):\n```python\n# GPT-2 (124M) architecture\nn_layer = 12\nn_head = 12\nn_embd = 768\nblock_size = 1024\ndropout = 0.0\n\n# Training\nbatch_size = 12\ngradient_accumulation_steps = 5 * 8  # Total batch ~0.5M tokens\nlearning_rate = 6e-4\nmax_iters = 600000\nlr_decay_iters = 600000\n\n# System\ncompile = True  # PyTorch 2.0\n```\n\n**Training time**: ~4 days (8× A100)\n\n### Workflow 3: Fine-tune pretrained GPT-2\n\n**Start from OpenAI checkpoint**:\n```python\n# In train.py or config\ninit_from = 'gpt2'  # Options: gpt2, gpt2-medium, gpt2-large, gpt2-xl\n\n# Model loads OpenAI weights automatically\npython train.py config\u002Ffinetune_shakespeare.py\n```\n\n**Example config** (`config\u002Ffinetune_shakespeare.py`):\n```python\n# Start from GPT-2\ninit_from = 'gpt2'\n\n# Dataset\ndataset = 'shakespeare_char'\nbatch_size = 1\nblock_size = 1024\n\n# Fine-tuning\nlearning_rate = 3e-5  # Lower LR for fine-tuning\nmax_iters = 2000\nwarmup_iters = 100\n\n# Regularization\nweight_decay = 1e-1\n```\n\n### Workflow 4: Custom dataset\n\n**Train on your own text**:\n```python\n# data\u002Fcustom\u002Fprepare.py\nimport numpy as np\n\n# Load your data\nwith open('my_data.txt', 'r') as f:\n    text = f.read()\n\n# Create character mappings\nchars = sorted(list(set(text)))\nstoi = {ch: i for i, ch in enumerate(chars)}\nitos = {i: ch for i, ch in enumerate(chars)}\n\n# Tokenize\ndata = np.array([stoi[ch] for ch in text], dtype=np.uint16)\n\n# Split train\u002Fval\nn = len(data)\ntrain_data = data[:int(n*0.9)]\nval_data = data[int(n*0.9):]\n\n# Save\ntrain_data.tofile('data\u002Fcustom\u002Ftrain.bin')\nval_data.tofile('data\u002Fcustom\u002Fval.bin')\n```\n\n**Train**:\n```bash\npython data\u002Fcustom\u002Fprepare.py\npython train.py --dataset=custom\n```\n\n## When to use vs alternatives\n\n**Use nanoGPT when**:\n- Learning how GPT works\n- Experimenting with transformer variants\n- Teaching\u002Feducation purposes\n- Quick prototyping\n- Limited compute (can run on CPU)\n\n**Simplicity advantages**:\n- **~300 lines**: Entire model in `model.py`\n- **~300 lines**: Training loop in `train.py`\n- **Hackable**: Easy to modify\n- **No abstractions**: Pure PyTorch\n\n**Use alternatives instead**:\n- **HuggingFace Transformers**: Production use, many models\n- **Megatron-LM**: Large-scale distributed training\n- **LitGPT**: More architectures, production-ready\n- **PyTorch Lightning**: Need high-level framework\n\n## Common issues\n\n**Issue: CUDA out of memory**\n\nReduce batch size or context length:\n```python\nbatch_size = 1  # Reduce from 12\nblock_size = 512  # Reduce from 1024\ngradient_accumulation_steps = 40  # Increase to maintain effective batch\n```\n\n**Issue: Training too slow**\n\nEnable compilation (PyTorch 2.0+):\n```python\ncompile = True  # 2× speedup\n```\n\nUse mixed precision:\n```python\ndtype = 'bfloat16'  # Or 'float16'\n```\n\n**Issue: Poor generation quality**\n\nTrain longer:\n```python\nmax_iters = 10000  # Increase from 5000\n```\n\nLower temperature:\n```python\n# In sample.py\ntemperature = 0.7  # Lower from 1.0\ntop_k = 200       # Add top-k sampling\n```\n\n**Issue: Can't load GPT-2 weights**\n\nInstall transformers:\n```bash\npip install transformers\n```\n\nCheck model name:\n```python\ninit_from = 'gpt2'  # Valid: gpt2, gpt2-medium, gpt2-large, gpt2-xl\n```\n\n## Advanced topics\n\n**Model architecture**: See [references\u002Farchitecture.md](references\u002Farchitecture.md) for GPT block structure, multi-head attention, and MLP layers explained simply.\n\n**Training loop**: See [references\u002Ftraining.md](references\u002Ftraining.md) for learning rate schedule, gradient accumulation, and distributed data parallel setup.\n\n**Data preparation**: See [references\u002Fdata.md](references\u002Fdata.md) for tokenization strategies (character-level vs BPE) and binary format details.\n\n## Hardware requirements\n\n- **Shakespeare (char-level)**:\n  - CPU: 5 minutes\n  - GPU (T4): 1 minute\n  - VRAM: \u003C1GB\n\n- **GPT-2 (124M)**:\n  - 1× A100: ~1 week\n  - 8× A100: ~4 days\n  - VRAM: ~16GB per GPU\n\n- **GPT-2 Medium (350M)**:\n  - 8× A100: ~2 weeks\n  - VRAM: ~40GB per GPU\n\n**Performance**:\n- With `compile=True`: 2× speedup\n- With `dtype=bfloat16`: 50% memory reduction\n\n## Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002Fkarpathy\u002FnanoGPT ⭐ 48,000+\n- Video: \"Let's build GPT\" by Andrej Karpathy\n- Paper: \"Attention is All You Need\" (Vaswani et al.)\n- OpenWebText: https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FSkylion007\u002Fopenwebtext\n- Educational: Best for understanding transformers from scratch\n\n\n","# nanoGPT - Minimalist GPT Training\n\n## Quick start\n\nnanoGPT is a simplified GPT implementation designed for learning and experimentation.\n\n**Installation**:\n```bash\npip install torch numpy transformers datasets tiktoken wandb tqdm\n```\n\n**Train on Shakespeare** (CPU-friendly):\n```bash\n# Prepare data\npython data\u002Fshakespeare_char\u002Fprepare.py\n\n# Train (5 minutes on CPU)\npython train.py config\u002Ftrain_shakespeare_char.py\n\n# Generate text\npython sample.py --out_dir=out-shakespeare-char\n```\n\n**Output**:\n```\nROMEO:\nWhat say'st thou? Shall I speak, and be a man?\n\nJULIET:\nI am afeard, and yet I'll speak; for thou art\nOne that hath been a man, and yet I know not\nWhat thou art.\n```\n\n## Common workflows\n\n### Workflow 1: Character-level Shakespeare\n\n**Complete training pipeline**:\n```bash\n# Step 1: Prepare data (creates train.bin, val.bin)\npython data\u002Fshakespeare_char\u002Fprepare.py\n\n# Step 2: Train small model\npython train.py config\u002Ftrain_shakespeare_char.py\n\n# Step 3: Generate text\npython sample.py --out_dir=out-shakespeare-char\n```\n\n**Config** (`config\u002Ftrain_shakespeare_char.py`):\n```python\n# Model config\nn_layer = 6          # 6 transformer layers\nn_head = 6           # 6 attention heads\nn_embd = 384         # 384-dim embeddings\nblock_size = 256     # 256 char context\n\n# Training config\nbatch_size = 64\nlearning_rate = 1e-3\nmax_iters = 5000\neval_interval = 500\n\n# Hardware\ndevice = 'cpu'  # Or 'cuda'\ncompile = False # Set True for PyTorch 2.0\n```\n\n**Training time**: ~5 minutes (CPU), ~1 minute (GPU)\n\n### Workflow 2: Reproduce GPT-2 (124M)\n\n**Multi-GPU training on OpenWebText**:\n```bash\n# Step 1: Prepare OpenWebText (takes ~1 hour)\npython data\u002Fopenwebtext\u002Fprepare.py\n\n# Step 2: Train GPT-2 124M with DDP (8 GPUs)\ntorchrun --standalone --nproc_per_node=8 \\\n  train.py config\u002Ftrain_gpt2.py\n\n# Step 3: Sample from trained model\npython sample.py --out_dir=out\n```\n\n**Config** (`config\u002Ftrain_gpt2.py`):\n```python\n# GPT-2 (124M) architecture\nn_layer = 12\nn_head = 12\nn_embd = 768\nblock_size = 1024\ndropout = 0.0\n\n# Training\nbatch_size = 12\ngradient_accumulation_steps = 5 * 8  # Total batch ~0.5M tokens\nlearning_rate = 6e-4\nmax_iters = 600000\nlr_decay_iters = 600000\n\n# System\ncompile = True  # PyTorch 2.0\n```\n\n**Training time**: ~4 days (8× A100)\n\n### Workflow 3: Fine-tune pretrained GPT-2\n\n**Start from OpenAI checkpoint**:\n```python\n# In train.py or config\ninit_from = 'gpt2'  # Options: gpt2, gpt2-medium, gpt2-large, gpt2-xl\n\n# Model loads OpenAI weights automatically\npython train.py config\u002Ffinetune_shakespeare.py\n```\n\n**Example config** (`config\u002Ffinetune_shakespeare.py`):\n```python\n# Start from GPT-2\ninit_from = 'gpt2'\n\n# Dataset\ndataset = 'shakespeare_char'\nbatch_size = 1\nblock_size = 1024\n\n# Fine-tuning\nlearning_rate = 3e-5  # Lower LR for fine-tuning\nmax_iters = 2000\nwarmup_iters = 100\n\n# Regularization\nweight_decay = 1e-1\n```\n\n### Workflow 4: Custom dataset\n\n**Train on your own text**:\n```python\n# data\u002Fcustom\u002Fprepare.py\nimport numpy as np\n\n# Load your data\nwith open('my_data.txt', 'r') as f:\n    text = f.read()\n\n# Create character mappings\nchars = sorted(list(set(text)))\nstoi = {ch: i for i, ch in enumerate(chars)}\nitos = {i: ch for i, ch in enumerate(chars)}\n\n# Tokenize\ndata = np.array([stoi[ch] for ch in text], dtype=np.uint16)\n\n# Split train\u002Fval\nn = len(data)\ntrain_data = data[:int(n*0.9)]\nval_data = data[int(n*0.9):]\n\n# Save\ntrain_data.tofile('data\u002Fcustom\u002Ftrain.bin')\nval_data.tofile('data\u002Fcustom\u002Fval.bin')\n```\n\n**Train**:\n```bash\npython data\u002Fcustom\u002Fprepare.py\npython train.py --dataset=custom\n```\n\n## When to use vs alternatives\n\n**Use nanoGPT when**:\n- Learning how GPT works\n- Experimenting with transformer variants\n- Teaching\u002Feducation purposes\n- Quick prototyping\n- Limited compute (can run on CPU)\n\n**Simplicity advantages**:\n- **~300 lines**: Entire model in `model.py`\n- **~300 lines**: Training loop in `train.py`\n- **Hackable**: Easy to modify\n- **No abstractions**: Pure PyTorch\n\n**Use alternatives instead**:\n- **HuggingFace Transformers**: Production use, many models\n- **Megatron-LM**: Large-scale distributed training\n- **LitGPT**: More architectures, production-ready\n- **PyTorch Lightning**: Need high-level framework\n\n## Common issues\n\n**Issue: CUDA out of memory**\n\nReduce batch size or context length:\n```python\nbatch_size = 1  # Reduce from 12\nblock_size = 512  # Reduce from 1024\ngradient_accumulation_steps = 40  # Increase to maintain effective batch\n```\n\n**Issue: Training too slow**\n\nEnable compilation (PyTorch 2.0+):\n```python\ncompile = True  # 2× speedup\n```\n\nUse mixed precision:\n```python\ndtype = 'bfloat16'  # Or 'float16'\n```\n\n**Issue: Poor generation quality**\n\nTrain longer:\n```python\nmax_iters = 10000  # Increase from 5000\n```\n\nLower temperature:\n```python\n# In sample.py\ntemperature = 0.7  # Lower from 1.0\ntop_k = 200       # Add top-k sampling\n```\n\n**Issue: Can't load GPT-2 weights**\n\nInstall transformers:\n```bash\npip install transformers\n```\n\nCheck model name:\n```python\ninit_from = 'gpt2'  # Valid: gpt2, gpt2-medium, gpt2-large, gpt2-xl\n```\n\n## Advanced topics\n\n**Model architecture**: See [references\u002Farchitecture.md](references\u002Farchitecture.md) for GPT block structure, multi-head attention, and MLP layers explained simply.\n\n**Training loop**: See [references\u002Ftraining.md](references\u002Ftraining.md) for learning rate schedule, gradient accumulation, and distributed data parallel setup.\n\n**Data preparation**: See [references\u002Fdata.md](references\u002Fdata.md) for tokenization strategies (character-level vs BPE) and binary format details.\n\n## Hardware requirements\n\n- **Shakespeare (char-level)**:\n  - CPU: 5 minutes\n  - GPU (T4): 1 minute\n  - VRAM: \u003C1GB\n\n- **GPT-2 (124M)**:\n  - 1× A100: ~1 week\n  - 8× A100: ~4 days\n  - VRAM: ~16GB per GPU\n\n- **GPT-2 Medium (350M)**:\n  - 8× A100: ~2 weeks\n  - VRAM: ~40GB per GPU\n\n**Performance**:\n- With `compile=True`: 2× speedup\n- With `dtype=bfloat16`: 50% memory reduction\n\n## Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002Fkarpathy\u002FnanoGPT ⭐ 48,000+\n- Video: \"Let's build GPT\" by Andrej Karpathy\n- Paper: \"Attention is All You Need\" (Vaswani et al.)\n- OpenWebText: https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FSkylion007\u002Fopenwebtext\n- Educational: Best for understanding transformers from scratch\n\n\n",{"name":5,"tags":44,"author":50,"license":15,"version":51,"category":52,"description":7,"dependencies":53},[27,19,26,45,46,25,47,48,49],"Educational","Andrej Karpathy","Minimalist","From Scratch","Training","Synthetic Sciences","1.0.0","ml-training",[54,55,56,22,57],"torch","transformers","datasets","wandb","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnanogpt","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 nanogpt",{"tool":70,"command":71},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill nanogpt",{"tool":73,"command":74},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill nanogpt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnanogpt\u002FSKILL.md",true,[78,84,88],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Farchitecture.md","REFERENCE","参考文档",11716,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnanogpt\u002Freferences\u002Farchitecture.md",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fdata.md",11212,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnanogpt\u002Freferences\u002Fdata.md",{"path":89,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ftraining.md",13551,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnanogpt\u002Freferences\u002Ftraining.md",[93,125,151,185,212,238],{"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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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",[157,160,161],{"categoryNo":158,"categoryName":159,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":162,"categoryName":163,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[167,168,169,170,171,172,173,174,175,176,114,177,178,179,180,181,182,183,140,184],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":195,"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",[191,194],{"categoryNo":192,"categoryName":193,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[196,168,197,198,199,200,201,202,203,175,113,204,205,206,207,208,209,210,23,211],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":224,"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",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":222,"categoryName":223,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000030","开发工程师",[36,225,146,226,227,228,229,112,230,113,114,116,231,232,233,234,235,236,183,237],"research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":239,"name":240,"nameZh":6,"description":241,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":242,"licenseFileUrl":6,"sourceHtmlUrl":243,"categories":244,"keywords":251,"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",[245,248,249,250],{"categoryNo":246,"categoryName":247,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":158,"categoryName":159,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":162,"categoryName":163,"taxonomyType":164,"taxonomyTypeDesc":165},[167,252,253,254,255,256,196,257,258,113,114,116,259,260,261,262,263,264,168,183],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]