[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADTVYNNFSFJ5JZ2CBDYEAN":3,"skill-same:01M6ADTVYNNFSFJ5JZ2CBDYEAN":101},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":66,"repositoryHtmlUrl":67,"repositoryHomepageUrl":68,"ownerHtmlUrl":69,"ownerBlogUrl":70,"ownerDescription":71,"updatedAt":72,"install":73,"resources":86},"01M6ADTVYNNFSFJ5JZ2CBDYEAN","model-architecture-mamba",null,"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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"architecture","Mamba","Model Architecture","model architecture mamba","model","complexity","sequences","orchestra","with","Efficient Inference","multi","state-space","long","token","kv cache","design","context","Transformers","HuggingFace","cache","linear","state","research","inference","2026-11-10 10:12:30",false,"---\nname: model-architecture-mamba\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.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Model Architecture, Mamba, State Space Models, SSM, Linear Complexity, Long Context, Efficient Inference, Hardware-Aware, Alternative To Transformers]\n  dependencies: [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 wiki\n\n\n",{"name":5,"license":15,"metadata":48,"description":7},{"tags":49,"author":56,"version":57,"dependencies":58},[21,20,50,51,52,53,28,54,55],"State Space Models","SSM","Linear Complexity","Long Context","Hardware-Aware","Alternative To Transformers","Orchestra Research","1.0.0",[59,60,61,62],"mamba-ssm","torch","transformers","causal-conv1d","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":74,"prompt":84,"zipAvailable":85},[75,78,81],{"tool":76,"command":77},"npx","npx skills add davila7\u002Fclaude-code-templates --skill model-architecture-mamba",{"tool":79,"command":80},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill model-architecture-mamba",{"tool":82,"command":83},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill model-architecture-mamba","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba\u002FSKILL.md",true,[87,93,97],{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Farchitecture-details.md","REFERENCE","参考文档",5456,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba\u002Freferences\u002Farchitecture-details.md",{"path":94,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fbenchmarks.md",8120,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba\u002Freferences\u002Fbenchmarks.md",{"path":98,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Ftraining-guide.md",9012,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-mamba\u002Freferences\u002Ftraining-guide.md",[102,133,159,192,226,250],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":113,"publishedAt":43},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. 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Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[139],{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[141,142,143,144,145,146,24,147,148,149,150,123,151,152,153,154,155,156,157,158],"technical debt","debt","technical","technical debt auditor","auditor","coverage","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":174,"publishedAt":43},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[165,168,169],{"categoryNo":166,"categoryName":167,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},{"categoryNo":170,"categoryName":171,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[175,176,177,178,179,180,181,119,182,183,184,185,186,187,188,122,189,190,123,191],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":196,"licenseFileUrl":197,"sourceHtmlUrl":198,"categories":199,"keywords":203,"publishedAt":43},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite 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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[200],{"categoryNo":201,"categoryName":202,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000009","前端开发",[204,205,206,207,208,40,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,119,225],"artifacts builder","web","web artifacts builder","artifacts","builder","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"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":196,"licenseFileUrl":230,"sourceHtmlUrl":231,"categories":232,"keywords":237,"publishedAt":43},"01M6AFWF33SFV9Z3SF4PEEE4BF","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[233,236],{"categoryNo":234,"categoryName":235,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":201,"categoryName":202,"taxonomyType":111,"taxonomyTypeDesc":112},[238,239,240,241,242,205,243,213,214,244,245,246,247,119,248,27,249],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":251,"name":252,"nameZh":6,"description":253,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":196,"licenseFileUrl":254,"sourceHtmlUrl":255,"categories":256,"keywords":264,"publishedAt":43},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[257,260,261],{"categoryNo":258,"categoryName":259,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},{"categoryNo":262,"categoryName":263,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000036","内容创作者",[265,266,267,268,269,270,271,272,207,273,216,151,274,275,276,277,278,187,279,123,280,281,282,283,284,130,285],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]