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Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1981,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"architecture","mamba architecture","Mamba","sequences","design","orchestra","with","Model Architecture","Efficient Inference","multi","state-space","long","token","kv cache","complexity","context","model","Transformers","HuggingFace","cache","linear","state","2026-11-10 10:12:30",false,"---\nname: mamba-architecture\ndescription: State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Model Architecture, Mamba, State Space Models, SSM, Linear Complexity, Long Context, Efficient Inference, Hardware-Aware, Alternative To Transformers]\ndependencies: [mamba-ssm, torch, transformers, causal-conv1d]\n---\n\n# Mamba - Selective State Space Models\n\n## Quick start\n\nMamba is a state-space model architecture achieving O(n) linear complexity for sequence modeling.\n\n**Installation**:\n```bash\n# Install causal-conv1d (optional, for efficiency)\npip install causal-conv1d>=1.4.0\n\n# Install Mamba\npip install mamba-ssm\n# Or both together\npip install mamba-ssm[causal-conv1d]\n```\n\n**Prerequisites**: Linux, NVIDIA GPU, PyTorch 1.12+, CUDA 11.6+\n\n**Basic usage** (Mamba block):\n```python\nimport torch\nfrom mamba_ssm import Mamba\n\nbatch, length, dim = 2, 64, 16\nx = torch.randn(batch, length, dim).to(\"cuda\")\n\nmodel = Mamba(\n    d_model=dim,      # Model dimension\n    d_state=16,       # SSM state dimension\n    d_conv=4,         # Conv1d kernel size\n    expand=2          # Expansion factor\n).to(\"cuda\")\n\ny = model(x)  # O(n) complexity!\nassert y.shape == x.shape\n```\n\n## Common workflows\n\n### Workflow 1: Language model with Mamba-2\n\n**Complete LM with generation**:\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nfrom mamba_ssm.models.config_mamba import MambaConfig\nimport torch\n\n# Configure Mamba-2 LM\nconfig = MambaConfig(\n    d_model=1024,           # Hidden dimension\n    n_layer=24,             # Number of layers\n    vocab_size=50277,       # Vocabulary size\n    ssm_cfg=dict(\n        layer=\"Mamba2\",     # Use Mamba-2\n        d_state=128,        # Larger state for Mamba-2\n        headdim=64,         # Head dimension\n        ngroups=1           # Number of groups\n    )\n)\n\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\n\n# Generate text\ninput_ids = torch.randint(0, 1000, (1, 20), device=\"cuda\", dtype=torch.long)\noutput = model.generate(\n    input_ids=input_ids,\n    max_length=100,\n    temperature=0.7,\n    top_p=0.9\n)\n```\n\n### Workflow 2: Use pretrained Mamba models\n\n**Load from HuggingFace**:\n```python\nfrom transformers import AutoTokenizer\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n\n# Load pretrained model\nmodel_name = \"state-spaces\u002Fmamba-2.8b\"\ntokenizer = AutoTokenizer.from_pretrained(\"EleutherAI\u002Fgpt-neox-20b\")  # Use compatible tokenizer\nmodel = MambaLMHeadModel.from_pretrained(model_name, device=\"cuda\", dtype=torch.float16)\n\n# Generate\nprompt = \"The future of AI is\"\ninput_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\noutput_ids = model.generate(\n    input_ids=input_ids,\n    max_length=200,\n    temperature=0.7,\n    top_p=0.9,\n    repetition_penalty=1.2\n)\ngenerated_text = tokenizer.decode(output_ids[0])\nprint(generated_text)\n```\n\n**Available models**:\n- `state-spaces\u002Fmamba-130m`\n- `state-spaces\u002Fmamba-370m`\n- `state-spaces\u002Fmamba-790m`\n- `state-spaces\u002Fmamba-1.4b`\n- `state-spaces\u002Fmamba-2.8b`\n\n### Workflow 3: Mamba-1 vs Mamba-2\n\n**Mamba-1** (smaller state):\n```python\nfrom mamba_ssm import Mamba\n\nmodel = Mamba(\n    d_model=256,\n    d_state=16,      # Smaller state dimension\n    d_conv=4,\n    expand=2\n).to(\"cuda\")\n```\n\n**Mamba-2** (multi-head, larger state):\n```python\nfrom mamba_ssm import Mamba2\n\nmodel = Mamba2(\n    d_model=256,\n    d_state=128,     # Larger state dimension\n    d_conv=4,\n    expand=2,\n    headdim=64,      # Head dimension for multi-head\n    ngroups=1        # Parallel groups\n).to(\"cuda\")\n```\n\n**Key differences**:\n- **State size**: Mamba-1 (d_state=16) vs Mamba-2 (d_state=128)\n- **Architecture**: Mamba-2 has multi-head structure\n- **Normalization**: Mamba-2 uses RMSNorm\n- **Distributed**: Mamba-2 supports tensor parallelism\n\n### Workflow 4: Benchmark vs Transformers\n\n**Generation speed comparison**:\n```bash\n# Benchmark Mamba\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"state-spaces\u002Fmamba-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n\n# Benchmark Transformer\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"EleutherAI\u002Fpythia-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n```\n\n**Expected results**:\n- **Mamba**: 5× faster inference\n- **Memory**: No KV cache needed\n- **Scaling**: Linear with sequence length\n\n## When to use vs alternatives\n\n**Use Mamba when**:\n- Need long sequences (100K+ tokens)\n- Want faster inference than Transformers\n- Memory-constrained (no KV cache)\n- Building streaming applications\n- Linear scaling important\n\n**Advantages**:\n- **O(n) complexity**: Linear vs quadratic\n- **5× faster inference**: No attention overhead\n- **No KV cache**: Lower memory usage\n- **Million-token sequences**: Hardware-efficient\n- **Streaming**: Constant memory per token\n\n**Use alternatives instead**:\n- **Transformers**: Need best-in-class performance, have compute\n- **RWKV**: Want RNN+Transformer hybrid\n- **RetNet**: Need retention-based architecture\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: CUDA out of memory**\n\nReduce batch size or use gradient checkpointing:\n```python\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\nmodel.gradient_checkpointing_enable()  # Enable checkpointing\n```\n\n**Issue: Slow installation**\n\nInstall binary wheels (not source):\n```bash\npip install mamba-ssm --no-build-isolation\n```\n\n**Issue: Missing causal-conv1d**\n\nInstall separately:\n```bash\npip install causal-conv1d>=1.4.0\n```\n\n**Issue: Model not loading from HuggingFace**\n\nUse `MambaLMHeadModel.from_pretrained` (not `AutoModel`):\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nmodel = MambaLMHeadModel.from_pretrained(\"state-spaces\u002Fmamba-2.8b\")\n```\n\n## Advanced topics\n\n**Selective SSM**: See [references\u002Fselective-ssm.md](references\u002Fselective-ssm.md) for mathematical formulation, state-space equations, and how selectivity enables O(n) complexity.\n\n**Mamba-2 architecture**: See [references\u002Fmamba2-details.md](references\u002Fmamba2-details.md) for multi-head structure, tensor parallelism, and distributed training setup.\n\n**Performance optimization**: See [references\u002Fperformance.md](references\u002Fperformance.md) for hardware-aware design, CUDA kernels, and memory efficiency techniques.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA with CUDA 11.6+\n- **VRAM**:\n  - 130M model: 2GB\n  - 370M model: 4GB\n  - 790M model: 8GB\n  - 1.4B model: 14GB\n  - 2.8B model: 28GB (FP16)\n- **Inference**: 5× faster than Transformers\n- **Memory**: No KV cache (lower than Transformers)\n\n**Performance** (vs Transformers):\n- **Speed**: 5× faster inference\n- **Memory**: 50% less (no KV cache)\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (Mamba-1): https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.00752 (Dec 2023)\n- Paper (Mamba-2): https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.21060 (May 2024)\n- GitHub: https:\u002F\u002Fgithub.com\u002Fstate-spaces\u002Fmamba ⭐ 13,000+\n- Models: https:\u002F\u002Fhuggingface.co\u002Fstate-spaces\n- Docs: Repository README and wiki\n\n\n","# Mamba - Selective State Space Models\n\n## Quick start\n\nMamba is a state-space model architecture achieving O(n) linear complexity for sequence modeling.\n\n**Installation**:\n```bash\n# Install causal-conv1d (optional, for efficiency)\npip install causal-conv1d>=1.4.0\n\n# Install Mamba\npip install mamba-ssm\n# Or both together\npip install mamba-ssm[causal-conv1d]\n```\n\n**Prerequisites**: Linux, NVIDIA GPU, PyTorch 1.12+, CUDA 11.6+\n\n**Basic usage** (Mamba block):\n```python\nimport torch\nfrom mamba_ssm import Mamba\n\nbatch, length, dim = 2, 64, 16\nx = torch.randn(batch, length, dim).to(\"cuda\")\n\nmodel = Mamba(\n    d_model=dim,      # Model dimension\n    d_state=16,       # SSM state dimension\n    d_conv=4,         # Conv1d kernel size\n    expand=2          # Expansion factor\n).to(\"cuda\")\n\ny = model(x)  # O(n) complexity!\nassert y.shape == x.shape\n```\n\n## Common workflows\n\n### Workflow 1: Language model with Mamba-2\n\n**Complete LM with generation**:\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nfrom mamba_ssm.models.config_mamba import MambaConfig\nimport torch\n\n# Configure Mamba-2 LM\nconfig = MambaConfig(\n    d_model=1024,           # Hidden dimension\n    n_layer=24,             # Number of layers\n    vocab_size=50277,       # Vocabulary size\n    ssm_cfg=dict(\n        layer=\"Mamba2\",     # Use Mamba-2\n        d_state=128,        # Larger state for Mamba-2\n        headdim=64,         # Head dimension\n        ngroups=1           # Number of groups\n    )\n)\n\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\n\n# Generate text\ninput_ids = torch.randint(0, 1000, (1, 20), device=\"cuda\", dtype=torch.long)\noutput = model.generate(\n    input_ids=input_ids,\n    max_length=100,\n    temperature=0.7,\n    top_p=0.9\n)\n```\n\n### Workflow 2: Use pretrained Mamba models\n\n**Load from HuggingFace**:\n```python\nfrom transformers import AutoTokenizer\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n\n# Load pretrained model\nmodel_name = \"state-spaces\u002Fmamba-2.8b\"\ntokenizer = AutoTokenizer.from_pretrained(\"EleutherAI\u002Fgpt-neox-20b\")  # Use compatible tokenizer\nmodel = MambaLMHeadModel.from_pretrained(model_name, device=\"cuda\", dtype=torch.float16)\n\n# Generate\nprompt = \"The future of AI is\"\ninput_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\noutput_ids = model.generate(\n    input_ids=input_ids,\n    max_length=200,\n    temperature=0.7,\n    top_p=0.9,\n    repetition_penalty=1.2\n)\ngenerated_text = tokenizer.decode(output_ids[0])\nprint(generated_text)\n```\n\n**Available models**:\n- `state-spaces\u002Fmamba-130m`\n- `state-spaces\u002Fmamba-370m`\n- `state-spaces\u002Fmamba-790m`\n- `state-spaces\u002Fmamba-1.4b`\n- `state-spaces\u002Fmamba-2.8b`\n\n### Workflow 3: Mamba-1 vs Mamba-2\n\n**Mamba-1** (smaller state):\n```python\nfrom mamba_ssm import Mamba\n\nmodel = Mamba(\n    d_model=256,\n    d_state=16,      # Smaller state dimension\n    d_conv=4,\n    expand=2\n).to(\"cuda\")\n```\n\n**Mamba-2** (multi-head, larger state):\n```python\nfrom mamba_ssm import Mamba2\n\nmodel = Mamba2(\n    d_model=256,\n    d_state=128,     # Larger state dimension\n    d_conv=4,\n    expand=2,\n    headdim=64,      # Head dimension for multi-head\n    ngroups=1        # Parallel groups\n).to(\"cuda\")\n```\n\n**Key differences**:\n- **State size**: Mamba-1 (d_state=16) vs Mamba-2 (d_state=128)\n- **Architecture**: Mamba-2 has multi-head structure\n- **Normalization**: Mamba-2 uses RMSNorm\n- **Distributed**: Mamba-2 supports tensor parallelism\n\n### Workflow 4: Benchmark vs Transformers\n\n**Generation speed comparison**:\n```bash\n# Benchmark Mamba\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"state-spaces\u002Fmamba-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n\n# Benchmark Transformer\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"EleutherAI\u002Fpythia-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n```\n\n**Expected results**:\n- **Mamba**: 5× faster inference\n- **Memory**: No KV cache needed\n- **Scaling**: Linear with sequence length\n\n## When to use vs alternatives\n\n**Use Mamba when**:\n- Need long sequences (100K+ tokens)\n- Want faster inference than Transformers\n- Memory-constrained (no KV cache)\n- Building streaming applications\n- Linear scaling important\n\n**Advantages**:\n- **O(n) complexity**: Linear vs quadratic\n- **5× faster inference**: No attention overhead\n- **No KV cache**: Lower memory usage\n- **Million-token sequences**: Hardware-efficient\n- **Streaming**: Constant memory per token\n\n**Use alternatives instead**:\n- **Transformers**: Need best-in-class performance, have compute\n- **RWKV**: Want RNN+Transformer hybrid\n- **RetNet**: Need retention-based architecture\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: CUDA out of memory**\n\nReduce batch size or use gradient checkpointing:\n```python\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\nmodel.gradient_checkpointing_enable()  # Enable checkpointing\n```\n\n**Issue: Slow installation**\n\nInstall binary wheels (not source):\n```bash\npip install mamba-ssm --no-build-isolation\n```\n\n**Issue: Missing causal-conv1d**\n\nInstall separately:\n```bash\npip install causal-conv1d>=1.4.0\n```\n\n**Issue: Model not loading from HuggingFace**\n\nUse `MambaLMHeadModel.from_pretrained` (not `AutoModel`):\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nmodel = MambaLMHeadModel.from_pretrained(\"state-spaces\u002Fmamba-2.8b\")\n```\n\n## Advanced topics\n\n**Selective SSM**: See [references\u002Fselective-ssm.md](references\u002Fselective-ssm.md) for mathematical formulation, state-space equations, and how selectivity enables O(n) complexity.\n\n**Mamba-2 architecture**: See [references\u002Fmamba2-details.md](references\u002Fmamba2-details.md) for multi-head structure, tensor parallelism, and distributed training setup.\n\n**Performance optimization**: See [references\u002Fperformance.md](references\u002Fperformance.md) for hardware-aware design, CUDA kernels, and memory efficiency techniques.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA with CUDA 11.6+\n- **VRAM**:\n  - 130M model: 2GB\n  - 370M model: 4GB\n  - 790M model: 8GB\n  - 1.4B model: 14GB\n  - 2.8B model: 28GB (FP16)\n- **Inference**: 5× faster than Transformers\n- **Memory**: No KV cache (lower than Transformers)\n\n**Performance** (vs Transformers):\n- **Speed**: 5× faster inference\n- **Memory**: 50% less (no KV cache)\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (Mamba-1): https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.00752 (Dec 2023)\n- Paper (Mamba-2): https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.21060 (May 2024)\n- GitHub: https:\u002F\u002Fgithub.com\u002Fstate-spaces\u002Fmamba ⭐ 13,000+\n- Models: https:\u002F\u002Fhuggingface.co\u002Fstate-spaces\n- Docs: Repository README and 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of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[106],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[112,113,114,115,116,117,118,119,120,121,122,123,40,124,125,126,127,128,129,130,131,132,133,28,134,135,136],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","component","routing","state management","shadcn","file","ui","Tools","Tailwind","use","html","single-file","simple","ui components","for","jsx","suite",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":148,"publishedAt":41},"01M6FEE9VTK1W4GY21AKREJXRG","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[144,147],{"categoryNo":145,"categoryName":146,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},[149,150,151,117,152,153,113,154,155,156,126,157,158,159,160,25,134],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","applications","behavior","local",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":165,"sourceHtmlUrl":166,"categories":167,"keywords":168,"publishedAt":41},"01M6FE20F7ZTQBFWKF4MPJ2SBX","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[169,170,171,172,173,174,175,176,129,177,178,179,180,181,182,183,25,184,185,186,134,187],"analysis","neuropixels analysis","spike","metrics","data","open","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load","when",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":192,"sourceHtmlUrl":193,"categories":194,"keywords":198,"publishedAt":41},"01M6FDP0PDK6F1GGX395C7625Z","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[195],{"categoryNo":196,"categoryName":197,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000023","Skill与智能体开发",[199,200,201,202,203,204,205,206,207,208,209,210,211,212,25,134,213,187,214],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":219,"sourceHtmlUrl":220,"categories":221,"keywords":223,"publishedAt":41},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[222],{"categoryNo":196,"categoryName":197,"taxonomyType":109,"taxonomyTypeDesc":110},[217,224,225,226,227,228,229,156,230,231,129,232,233,234,235,134,236],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":241,"sourceHtmlUrl":242,"categories":243,"keywords":250,"publishedAt":41},"01M6FDHPXE10J3XGTCH5HBHS1M","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[244,247],{"categoryNo":245,"categoryName":246,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":248,"categoryName":249,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000016","图像与多媒体",[251,252,253,200,254,127,255,256,205,129,257,212,258,259,260,134,261,187],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]