[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NARS2K4M3TSWQNVKVPFFG9":3,"skill-same:01M6NARS2K4M3TSWQNVKVPFFG9":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":14,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6NARS2K4M3TSWQNVKVPFFG9","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).","zhengliangliahu-cloud\u002Fskills","zhengliangliahu-cloud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F248268067?v=4","01M6NAQ109P2Y11XSV5D9QJFFY","Lin","01M6NAQ15R1M4E18CA2KPJHC06",1,"MIT","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F01-model-architecture\u002Fnanogpt\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"NanoGPT","Model Architecture","scratch","tiktoken","for","orchestra","WandB","From Scratch","Transformer","GPT-2","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":15,"version":50,"description":7,"dependencies":51},[20,19,28,45,46,27,47,26,48],"Educational","Andrej Karpathy","Minimalist","Training","Orchestra Research","1.0.0",[52,53,54,22,55],"torch","transformers","datasets","wandb","main","75c7c92896e878938460f0bceb23b6ab4a787286","AI-Research-SKILLs\u002F01-model-architecture\u002Fnanogpt","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud","A postgraduate student majoring in mathematics.","2026-06-03 19:04:43",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add zhengliangliahu-cloud\u002Fskills --skill nanogpt",{"tool":69,"command":70},"bunx","bunx skills add zhengliangliahu-cloud\u002Fskills --skill nanogpt",{"tool":72,"command":73},"pnpm","pnpm dlx skills add zhengliangliahu-cloud\u002Fskills --skill nanogpt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F01-model-architecture\u002Fnanogpt\u002FSKILL.md",true,[77,83,87],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Farchitecture.md","REFERENCE","参考文档",11716,"https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F01-model-architecture\u002Fnanogpt\u002Freferences\u002Farchitecture.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fdata.md",11212,"https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F01-model-architecture\u002Fnanogpt\u002Freferences\u002Fdata.md",{"path":88,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Ftraining.md",13551,"https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F01-model-architecture\u002Fnanogpt\u002Freferences\u002Ftraining.md",[92,124,158,181,206,226],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":103,"publishedAt":39},"01M6NBV8V36BCHZ1K9F7EMRJP1","ara-rigor-reviewer","Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. 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Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fresearch-manager\u002FSKILL.md",[130,133,134],{"categoryNo":131,"categoryName":132,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[140,141,142,143,144,145,146,147,148,149,150,151,113,152,153,154,155,156,157,106],"research","manager","ara research manager","writing","directory","heuristics","how","knowledge","knowledge management","never","scanning","conversation","maintain","project","end","recording","at","claims",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":168,"publishedAt":39},"01M6NBTCRK24WXZYKBD19RAV09","ara-compiler","Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fcompiler\u002FSKILL.md",[164,167],{"categoryNo":165,"categoryName":166,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},[169,170,171,172,37,173,145,147,174,113,175,176,119,177,178,179,157,106,123,180],"compiler","ara compiler","PDF","notes","logs","ingestion","artifact","input","papers","directories","layer","Knowledge Extraction",{"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":188,"publishedAt":39},"01M6NBSXGH49PE13PF1H9P7F8N","creative-thinking-for-research","Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fcreative-thinking-for-research\u002FSKILL.md",[187],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},[140,189,190,191,192,23,193,194,195,196,197,198,199,200,201,202,203,204,24,205],"creative","thinking","Creative Thinking","creative thinking for research","reasoning","science","use","ideation","frameworks","research ideation","strategies","problem","Analogical Reasoning","Problem Reformulation","novel","directions","constraint",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":213,"publishedAt":39},"01M6NBSNHZYM70ZAFXQNESYYBG","brainstorming-research-ideas","Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fbrainstorming-research-ideas\u002FSKILL.md",[212],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},[140,214,215,216,217,218,219,220,190,197,221,200,222,223,191,224,225,203,204,24],"brainstorming","ideas","brainstorming research ideas","discovery","projects","work","strategy","discover","impact","guides","Problem Discovery","Research Strategy",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":238,"publishedAt":39},"01M6NBS3Z3CJYH5225X73AZN3S","systems-paper-writing","Comprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F20-ml-paper-writing\u002Fsystems-paper-writing\u002FSKILL.md",[232,233,234,237],{"categoryNo":131,"categoryName":132,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":235,"categoryName":236,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},[143,239,240,241,242,104,243,244,245,117,246,247,248,249,177,250,24,23,251,252],"Paper Writing","systems","paper","Systems Paper Writing","guide","Blueprint","patterns","OSDI","NSDI","ASPLOS","SOSP","targeting","EuroSys","Structural Blueprint"]