[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQ70TERKFRCRKYCMZ7PJZ3":3,"skill-same:01M6KQ70TERKFRCRKYCMZ7PJZ3":90},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":13,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M6KQ70TERKFRCRKYCMZ7PJZ3","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).","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fnanogpt\u002Fnanogpt\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"NanoGPT","multi","scratch","tiktoken","for","orchestra","WandB","From Scratch","Transformer","GPT-2","Model Architecture","architecture","CPU","clean","karpathy","shakespeare","model","learning","Transformers","code","training","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).\nversion: 1.0.0\nauthor: Orchestra Research\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":49,"license":14,"version":50,"description":7,"dependencies":51},[28,18,27,45,46,26,47,25,48],"Educational","Andrej Karpathy","Minimalist","Training","Orchestra Research","1.0.0",[52,53,54,21,55],"torch","transformers","datasets","wandb","main","f19b254e8975d0bfddb394686706106930d7964d","AI-Models-and-Architecture\u002Fnanogpt\u002Fnanogpt","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-12 18:13:37",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add endman100\u002Fskill-router-nexus --skill nanogpt",{"tool":68,"command":69},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill nanogpt",{"tool":71,"command":72},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill nanogpt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fnanogpt\u002Fnanogpt\u002FSKILL.md",true,[76,82,86],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Farchitecture.md","REFERENCE","参考文档",11716,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fnanogpt\u002Fnanogpt\u002Freferences\u002Farchitecture.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fdata.md",11212,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fnanogpt\u002Fnanogpt\u002Freferences\u002Fdata.md",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Ftraining.md",13551,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fnanogpt\u002Fnanogpt\u002Freferences\u002Ftraining.md",[91,115,152,175,184,208],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":95,"sourceHtmlUrl":96,"categories":97,"keywords":103,"publishedAt":39},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[104,105,106,107,108,109,110,111,112,113,114],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":119,"sourceHtmlUrl":120,"categories":121,"keywords":133,"publishedAt":39},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FSKILL.md",[122,127,130],{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":128,"categoryName":129,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":131,"categoryName":132,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000036","内容创作者",[134,135,136,137,138,139,140,141,142,143,144,145,146,107,147,148,149,22,150,151],"writing dna skill","DNA","writing","strategy","english","logic","visual","structure","chinese","reusable","use","style","distill","analysis","frame","source","at","complete",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":156,"sourceHtmlUrl":157,"categories":158,"keywords":161,"publishedAt":39},"01M6M428TTCGQ4ME3KY99VN037","stop-slop","Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[159,160],{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},{"categoryNo":131,"categoryName":132,"taxonomyType":101,"taxonomyTypeDesc":102},[162,163,164,165,166,167,168,169,170,144,171,172,173,22,174,136],"slop","stop slop","stop","drafting","when","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":179,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":39},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[177,183],"文本",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":188,"sourceHtmlUrl":189,"categories":190,"keywords":191,"publishedAt":39},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[186,192,193,194,195,196,144,197,112,149,198,199,200,201,114,202,203,204,205,166,206,207,162],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":212,"sourceHtmlUrl":213,"categories":214,"keywords":216,"publishedAt":39},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[215],{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},[217,218,219,112,111,110,136,107,183,220,162,164],"humanizer","zh","humanizer zh","翻译"]