[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADNBE54R7AZWBFSQHTB1NB":3,"skill-same:01M6ADNBE54R7AZWBFSQHTB1NB":106},{"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":28,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":68,"commitSha":69,"skillDirectory":70,"forksCount":71,"repositoryHtmlUrl":72,"repositoryHomepageUrl":73,"ownerHtmlUrl":74,"ownerBlogUrl":75,"ownerDescription":76,"updatedAt":77,"install":78,"resources":91},"01M6ADNBE54R7AZWBFSQHTB1NB","distributed-training-accelerate",null,"Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed\u002FFSDP\u002FMegatron\u002FDDP. Automatic device placement, mixed precision (FP16\u002FBF16\u002FFP8). Interactive config, single launch command. HuggingFace ecosystem standard.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-accelerate\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"distributed","training","distributed training accelerate","Distributed Training","Accelerate","api","automatic","orchestra","ecosystem","Unified API","DDP","FSDP","megatron","add","command","research","Transformers","DeepSpeed","pytorch","config","launch","support","2026-11-10 10:12:30",false,"---\nname: distributed-training-accelerate\ndescription: Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed\u002FFSDP\u002FMegatron\u002FDDP. Automatic device placement, mixed precision (FP16\u002FBF16\u002FFP8). Interactive config, single launch command. HuggingFace ecosystem standard.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Distributed Training, HuggingFace, Accelerate, DeepSpeed, FSDP, Mixed Precision, PyTorch, DDP, Unified API, Simple]\n  dependencies: [accelerate, torch, transformers]\n---\n\n# HuggingFace Accelerate - Unified Distributed Training\n\n## Quick start\n\nAccelerate simplifies distributed training to 4 lines of code.\n\n**Installation**:\n```bash\npip install accelerate\n```\n\n**Convert PyTorch script** (4 lines):\n```python\nimport torch\n+ from accelerate import Accelerator\n\n+ accelerator = Accelerator()\n\n  model = torch.nn.Transformer()\n  optimizer = torch.optim.Adam(model.parameters())\n  dataloader = torch.utils.data.DataLoader(dataset)\n\n+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\n  for batch in dataloader:\n      optimizer.zero_grad()\n      loss = model(batch)\n-     loss.backward()\n+     accelerator.backward(loss)\n      optimizer.step()\n```\n\n**Run** (single command):\n```bash\naccelerate launch train.py\n```\n\n## Common workflows\n\n### Workflow 1: From single GPU to multi-GPU\n\n**Original script**:\n```python\n# train.py\nimport torch\n\nmodel = torch.nn.Linear(10, 2).to('cuda')\noptimizer = torch.optim.Adam(model.parameters())\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=32)\n\nfor epoch in range(10):\n    for batch in dataloader:\n        batch = batch.to('cuda')\n        optimizer.zero_grad()\n        loss = model(batch).mean()\n        loss.backward()\n        optimizer.step()\n```\n\n**With Accelerate** (4 lines added):\n```python\n# train.py\nimport torch\nfrom accelerate import Accelerator  # +1\n\naccelerator = Accelerator()  # +2\n\nmodel = torch.nn.Linear(10, 2)\noptimizer = torch.optim.Adam(model.parameters())\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=32)\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)  # +3\n\nfor epoch in range(10):\n    for batch in dataloader:\n        # No .to('cuda') needed - automatic!\n        optimizer.zero_grad()\n        loss = model(batch).mean()\n        accelerator.backward(loss)  # +4\n        optimizer.step()\n```\n\n**Configure** (interactive):\n```bash\naccelerate config\n```\n\n**Questions**:\n- Which machine? (single\u002Fmulti GPU\u002FTPU\u002FCPU)\n- How many machines? (1)\n- Mixed precision? (no\u002Ffp16\u002Fbf16\u002Ffp8)\n- DeepSpeed? (no\u002Fyes)\n\n**Launch** (works on any setup):\n```bash\n# Single GPU\naccelerate launch train.py\n\n# Multi-GPU (8 GPUs)\naccelerate launch --multi_gpu --num_processes 8 train.py\n\n# Multi-node\naccelerate launch --multi_gpu --num_processes 16 \\\n  --num_machines 2 --machine_rank 0 \\\n  --main_process_ip $MASTER_ADDR \\\n  train.py\n```\n\n### Workflow 2: Mixed precision training\n\n**Enable FP16\u002FBF16**:\n```python\nfrom accelerate import Accelerator\n\n# FP16 (with gradient scaling)\naccelerator = Accelerator(mixed_precision='fp16')\n\n# BF16 (no scaling, more stable)\naccelerator = Accelerator(mixed_precision='bf16')\n\n# FP8 (H100+)\naccelerator = Accelerator(mixed_precision='fp8')\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\n# Everything else is automatic!\nfor batch in dataloader:\n    with accelerator.autocast():  # Optional, done automatically\n        loss = model(batch)\n    accelerator.backward(loss)\n```\n\n### Workflow 3: DeepSpeed ZeRO integration\n\n**Enable DeepSpeed ZeRO-2**:\n```python\nfrom accelerate import Accelerator\n\naccelerator = Accelerator(\n    mixed_precision='bf16',\n    deepspeed_plugin={\n        \"zero_stage\": 2,  # ZeRO-2\n        \"offload_optimizer\": False,\n        \"gradient_accumulation_steps\": 4\n    }\n)\n\n# Same code as before!\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n```\n\n**Or via config**:\n```bash\naccelerate config\n# Select: DeepSpeed → ZeRO-2\n```\n\n**deepspeed_config.json**:\n```json\n{\n    \"fp16\": {\"enabled\": false},\n    \"bf16\": {\"enabled\": true},\n    \"zero_optimization\": {\n        \"stage\": 2,\n        \"offload_optimizer\": {\"device\": \"cpu\"},\n        \"allgather_bucket_size\": 5e8,\n        \"reduce_bucket_size\": 5e8\n    }\n}\n```\n\n**Launch**:\n```bash\naccelerate launch --config_file deepspeed_config.json train.py\n```\n\n### Workflow 4: FSDP (Fully Sharded Data Parallel)\n\n**Enable FSDP**:\n```python\nfrom accelerate import Accelerator, FullyShardedDataParallelPlugin\n\nfsdp_plugin = FullyShardedDataParallelPlugin(\n    sharding_strategy=\"FULL_SHARD\",  # ZeRO-3 equivalent\n    auto_wrap_policy=\"TRANSFORMER_AUTO_WRAP\",\n    cpu_offload=False\n)\n\naccelerator = Accelerator(\n    mixed_precision='bf16',\n    fsdp_plugin=fsdp_plugin\n)\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n```\n\n**Or via config**:\n```bash\naccelerate config\n# Select: FSDP → Full Shard → No CPU Offload\n```\n\n### Workflow 5: Gradient accumulation\n\n**Accumulate gradients**:\n```python\nfrom accelerate import Accelerator\n\naccelerator = Accelerator(gradient_accumulation_steps=4)\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\nfor batch in dataloader:\n    with accelerator.accumulate(model):  # Handles accumulation\n        optimizer.zero_grad()\n        loss = model(batch)\n        accelerator.backward(loss)\n        optimizer.step()\n```\n\n**Effective batch size**: `batch_size * num_gpus * gradient_accumulation_steps`\n\n## When to use vs alternatives\n\n**Use Accelerate when**:\n- Want simplest distributed training\n- Need single script for any hardware\n- Use HuggingFace ecosystem\n- Want flexibility (DDP\u002FDeepSpeed\u002FFSDP\u002FMegatron)\n- Need quick prototyping\n\n**Key advantages**:\n- **4 lines**: Minimal code changes\n- **Unified API**: Same code for DDP, DeepSpeed, FSDP, Megatron\n- **Automatic**: Device placement, mixed precision, sharding\n- **Interactive config**: No manual launcher setup\n- **Single launch**: Works everywhere\n\n**Use alternatives instead**:\n- **PyTorch Lightning**: Need callbacks, high-level abstractions\n- **Ray Train**: Multi-node orchestration, hyperparameter tuning\n- **DeepSpeed**: Direct API control, advanced features\n- **Raw DDP**: Maximum control, minimal abstraction\n\n## Common issues\n\n**Issue: Wrong device placement**\n\nDon't manually move to device:\n```python\n# WRONG\nbatch = batch.to('cuda')\n\n# CORRECT\n# Accelerate handles it automatically after prepare()\n```\n\n**Issue: Gradient accumulation not working**\n\nUse context manager:\n```python\n# CORRECT\nwith accelerator.accumulate(model):\n    optimizer.zero_grad()\n    accelerator.backward(loss)\n    optimizer.step()\n```\n\n**Issue: Checkpointing in distributed**\n\nUse accelerator methods:\n```python\n# Save only on main process\nif accelerator.is_main_process:\n    accelerator.save_state('checkpoint\u002F')\n\n# Load on all processes\naccelerator.load_state('checkpoint\u002F')\n```\n\n**Issue: Different results with FSDP**\n\nEnsure same random seed:\n```python\nfrom accelerate.utils import set_seed\nset_seed(42)\n```\n\n## Advanced topics\n\n**Megatron integration**: See [references\u002Fmegatron-integration.md](references\u002Fmegatron-integration.md) for tensor parallelism, pipeline parallelism, and sequence parallelism setup.\n\n**Custom plugins**: See [references\u002Fcustom-plugins.md](references\u002Fcustom-plugins.md) for creating custom distributed plugins and advanced configuration.\n\n**Performance tuning**: See [references\u002Fperformance.md](references\u002Fperformance.md) for profiling, memory optimization, and best practices.\n\n## Hardware requirements\n\n- **CPU**: Works (slow)\n- **Single GPU**: Works\n- **Multi-GPU**: DDP (default), DeepSpeed, or FSDP\n- **Multi-node**: DDP, DeepSpeed, FSDP, Megatron\n- **TPU**: Supported\n- **Apple MPS**: Supported\n\n**Launcher requirements**:\n- **DDP**: `torch.distributed.run` (built-in)\n- **DeepSpeed**: `deepspeed` (pip install deepspeed)\n- **FSDP**: PyTorch 1.12+ (built-in)\n- **Megatron**: Custom setup\n\n## Resources\n\n- Docs: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Faccelerate\n- GitHub: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Faccelerate\n- Version: 1.11.0+\n- Tutorial: \"Accelerate your scripts\"\n- Examples: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Faccelerate\u002Ftree\u002Fmain\u002Fexamples\n- Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries\n\n\n\n","# HuggingFace Accelerate - Unified Distributed Training\n\n## Quick start\n\nAccelerate simplifies distributed training to 4 lines of code.\n\n**Installation**:\n```bash\npip install accelerate\n```\n\n**Convert PyTorch script** (4 lines):\n```python\nimport torch\n+ from accelerate import Accelerator\n\n+ accelerator = Accelerator()\n\n  model = torch.nn.Transformer()\n  optimizer = torch.optim.Adam(model.parameters())\n  dataloader = torch.utils.data.DataLoader(dataset)\n\n+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\n  for batch in dataloader:\n      optimizer.zero_grad()\n      loss = model(batch)\n-     loss.backward()\n+     accelerator.backward(loss)\n      optimizer.step()\n```\n\n**Run** (single command):\n```bash\naccelerate launch train.py\n```\n\n## Common workflows\n\n### Workflow 1: From single GPU to multi-GPU\n\n**Original script**:\n```python\n# train.py\nimport torch\n\nmodel = torch.nn.Linear(10, 2).to('cuda')\noptimizer = torch.optim.Adam(model.parameters())\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=32)\n\nfor epoch in range(10):\n    for batch in dataloader:\n        batch = batch.to('cuda')\n        optimizer.zero_grad()\n        loss = model(batch).mean()\n        loss.backward()\n        optimizer.step()\n```\n\n**With Accelerate** (4 lines added):\n```python\n# train.py\nimport torch\nfrom accelerate import Accelerator  # +1\n\naccelerator = Accelerator()  # +2\n\nmodel = torch.nn.Linear(10, 2)\noptimizer = torch.optim.Adam(model.parameters())\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=32)\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)  # +3\n\nfor epoch in range(10):\n    for batch in dataloader:\n        # No .to('cuda') needed - automatic!\n        optimizer.zero_grad()\n        loss = model(batch).mean()\n        accelerator.backward(loss)  # +4\n        optimizer.step()\n```\n\n**Configure** (interactive):\n```bash\naccelerate config\n```\n\n**Questions**:\n- Which machine? (single\u002Fmulti GPU\u002FTPU\u002FCPU)\n- How many machines? (1)\n- Mixed precision? (no\u002Ffp16\u002Fbf16\u002Ffp8)\n- DeepSpeed? (no\u002Fyes)\n\n**Launch** (works on any setup):\n```bash\n# Single GPU\naccelerate launch train.py\n\n# Multi-GPU (8 GPUs)\naccelerate launch --multi_gpu --num_processes 8 train.py\n\n# Multi-node\naccelerate launch --multi_gpu --num_processes 16 \\\n  --num_machines 2 --machine_rank 0 \\\n  --main_process_ip $MASTER_ADDR \\\n  train.py\n```\n\n### Workflow 2: Mixed precision training\n\n**Enable FP16\u002FBF16**:\n```python\nfrom accelerate import Accelerator\n\n# FP16 (with gradient scaling)\naccelerator = Accelerator(mixed_precision='fp16')\n\n# BF16 (no scaling, more stable)\naccelerator = Accelerator(mixed_precision='bf16')\n\n# FP8 (H100+)\naccelerator = Accelerator(mixed_precision='fp8')\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\n# Everything else is automatic!\nfor batch in dataloader:\n    with accelerator.autocast():  # Optional, done automatically\n        loss = model(batch)\n    accelerator.backward(loss)\n```\n\n### Workflow 3: DeepSpeed ZeRO integration\n\n**Enable DeepSpeed ZeRO-2**:\n```python\nfrom accelerate import Accelerator\n\naccelerator = Accelerator(\n    mixed_precision='bf16',\n    deepspeed_plugin={\n        \"zero_stage\": 2,  # ZeRO-2\n        \"offload_optimizer\": False,\n        \"gradient_accumulation_steps\": 4\n    }\n)\n\n# Same code as before!\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n```\n\n**Or via config**:\n```bash\naccelerate config\n# Select: DeepSpeed → ZeRO-2\n```\n\n**deepspeed_config.json**:\n```json\n{\n    \"fp16\": {\"enabled\": false},\n    \"bf16\": {\"enabled\": true},\n    \"zero_optimization\": {\n        \"stage\": 2,\n        \"offload_optimizer\": {\"device\": \"cpu\"},\n        \"allgather_bucket_size\": 5e8,\n        \"reduce_bucket_size\": 5e8\n    }\n}\n```\n\n**Launch**:\n```bash\naccelerate launch --config_file deepspeed_config.json train.py\n```\n\n### Workflow 4: FSDP (Fully Sharded Data Parallel)\n\n**Enable FSDP**:\n```python\nfrom accelerate import Accelerator, FullyShardedDataParallelPlugin\n\nfsdp_plugin = FullyShardedDataParallelPlugin(\n    sharding_strategy=\"FULL_SHARD\",  # ZeRO-3 equivalent\n    auto_wrap_policy=\"TRANSFORMER_AUTO_WRAP\",\n    cpu_offload=False\n)\n\naccelerator = Accelerator(\n    mixed_precision='bf16',\n    fsdp_plugin=fsdp_plugin\n)\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n```\n\n**Or via config**:\n```bash\naccelerate config\n# Select: FSDP → Full Shard → No CPU Offload\n```\n\n### Workflow 5: Gradient accumulation\n\n**Accumulate gradients**:\n```python\nfrom accelerate import Accelerator\n\naccelerator = Accelerator(gradient_accumulation_steps=4)\n\nmodel, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\nfor batch in dataloader:\n    with accelerator.accumulate(model):  # Handles accumulation\n        optimizer.zero_grad()\n        loss = model(batch)\n        accelerator.backward(loss)\n        optimizer.step()\n```\n\n**Effective batch size**: `batch_size * num_gpus * gradient_accumulation_steps`\n\n## When to use vs alternatives\n\n**Use Accelerate when**:\n- Want simplest distributed training\n- Need single script for any hardware\n- Use HuggingFace ecosystem\n- Want flexibility (DDP\u002FDeepSpeed\u002FFSDP\u002FMegatron)\n- Need quick prototyping\n\n**Key advantages**:\n- **4 lines**: Minimal code changes\n- **Unified API**: Same code for DDP, DeepSpeed, FSDP, Megatron\n- **Automatic**: Device placement, mixed precision, sharding\n- **Interactive config**: No manual launcher setup\n- **Single launch**: Works everywhere\n\n**Use alternatives instead**:\n- **PyTorch Lightning**: Need callbacks, high-level abstractions\n- **Ray Train**: Multi-node orchestration, hyperparameter tuning\n- **DeepSpeed**: Direct API control, advanced features\n- **Raw DDP**: Maximum control, minimal abstraction\n\n## Common issues\n\n**Issue: Wrong device placement**\n\nDon't manually move to device:\n```python\n# WRONG\nbatch = batch.to('cuda')\n\n# CORRECT\n# Accelerate handles it automatically after prepare()\n```\n\n**Issue: Gradient accumulation not working**\n\nUse context manager:\n```python\n# CORRECT\nwith accelerator.accumulate(model):\n    optimizer.zero_grad()\n    accelerator.backward(loss)\n    optimizer.step()\n```\n\n**Issue: Checkpointing in distributed**\n\nUse accelerator methods:\n```python\n# Save only on main process\nif accelerator.is_main_process:\n    accelerator.save_state('checkpoint\u002F')\n\n# Load on all processes\naccelerator.load_state('checkpoint\u002F')\n```\n\n**Issue: Different results with FSDP**\n\nEnsure same random seed:\n```python\nfrom accelerate.utils import set_seed\nset_seed(42)\n```\n\n## Advanced topics\n\n**Megatron integration**: See [references\u002Fmegatron-integration.md](references\u002Fmegatron-integration.md) for tensor parallelism, pipeline parallelism, and sequence parallelism setup.\n\n**Custom plugins**: See [references\u002Fcustom-plugins.md](references\u002Fcustom-plugins.md) for creating custom distributed plugins and advanced configuration.\n\n**Performance tuning**: See [references\u002Fperformance.md](references\u002Fperformance.md) for profiling, memory optimization, and best practices.\n\n## Hardware requirements\n\n- **CPU**: Works (slow)\n- **Single GPU**: Works\n- **Multi-GPU**: DDP (default), DeepSpeed, or FSDP\n- **Multi-node**: DDP, DeepSpeed, FSDP, Megatron\n- **TPU**: Supported\n- **Apple MPS**: Supported\n\n**Launcher requirements**:\n- **DDP**: `torch.distributed.run` (built-in)\n- **DeepSpeed**: `deepspeed` (pip install deepspeed)\n- **FSDP**: PyTorch 1.12+ (built-in)\n- **Megatron**: Custom setup\n\n## Resources\n\n- Docs: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Faccelerate\n- GitHub: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Faccelerate\n- Version: 1.11.0+\n- Tutorial: \"Accelerate your scripts\"\n- Examples: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Faccelerate\u002Ftree\u002Fmain\u002Fexamples\n- Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries\n\n\n\n",{"name":5,"license":15,"metadata":56,"description":7},{"tags":57,"author":62,"version":63,"dependencies":64},[32,58,33,46,40,59,60,39,38,61],"HuggingFace","Mixed Precision","PyTorch","Simple","Orchestra Research","1.0.0",[65,66,67],"accelerate","torch","transformers","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-accelerate",3911,"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","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":79,"prompt":89,"zipAvailable":90},[80,83,86],{"tool":81,"command":82},"npx","npx skills add davila7\u002Fclaude-code-templates --skill distributed-training-accelerate",{"tool":84,"command":85},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill distributed-training-accelerate",{"tool":87,"command":88},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill distributed-training-accelerate","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-accelerate\u002FSKILL.md",true,[92,98,102],{"path":93,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fcustom-plugins.md","REFERENCE","参考文档",11781,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-accelerate\u002Freferences\u002Fcustom-plugins.md",{"path":99,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fmegatron-integration.md",11252,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-accelerate\u002Freferences\u002Fmegatron-integration.md",{"path":103,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fperformance.md",12569,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-accelerate\u002Freferences\u002Fperformance.md",[107,137,164,193,228,252],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":116,"publishedAt":51},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[113],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":144,"publishedAt":51},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. 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",[143],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},[145,146,147,148,149,150,151,152,153,154,155,126,156,157,158,159,160,161,162,163],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":175,"publishedAt":51},"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",[170,171,172],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":173,"categoryName":174,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[176,177,178,179,180,181,182,122,183,184,185,186,187,188,189,125,190,191,126,192],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":198,"sourceHtmlUrl":199,"categories":200,"keywords":204,"publishedAt":51},"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",[201],{"categoryNo":202,"categoryName":203,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,122,227],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":232,"sourceHtmlUrl":233,"categories":234,"keywords":239,"publishedAt":51},"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",[235,238],{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":202,"categoryName":203,"taxonomyType":21,"taxonomyTypeDesc":22},[240,241,242,243,244,206,245,215,216,246,247,248,249,122,250,128,251],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":256,"sourceHtmlUrl":257,"categories":258,"keywords":266,"publishedAt":51},"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",[259,262,263],{"categoryNo":260,"categoryName":261,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":264,"categoryName":265,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[267,268,269,270,271,272,273,274,208,275,218,156,276,277,278,279,280,188,281,126,282,283,284,285,286,134,287],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]