[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EAAMVDBEBHE2B47XDHVPJS":3,"skill-same:01M6EAAMVDBEBHE2B47XDHVPJS":124},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6EAAMVDBEBHE2B47XDHVPJS","huggingface-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.","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Faccelerate\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"huggingface accelerate","HuggingFace","api","Unified API","DDP","FSDP","Accelerate","megatron","simple","interactive","command","support","FP8","FP16","Mixed Precision","Distributed Training","DeepSpeed","training","pytorch","distributed","2026-11-10 10:12:30",false,"---\nname: huggingface-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.\nversion: 1.0.0\nauthor: luo-kai\nlicense: MIT\ntags: [Distributed Training, HuggingFace, Accelerate, DeepSpeed, FSDP, Mixed Precision, PyTorch, DDP, Unified API, Simple]\ndependencies: [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,"tags":49,"author":52,"license":15,"version":53,"description":7,"dependencies":54},[39,25,30,40,29,38,50,28,27,51],"PyTorch","Simple","luo-kai","1.0.0",[55,56,57],"accelerate","torch","transformers","main","4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c","ai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Faccelerate",1,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai","https:\u002F\u002Fgithub.com\u002Fluokai0","a solo builder.","2026-05-06 04:29:04",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill huggingface-accelerate",{"tool":72,"command":73},"bunx","bunx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill huggingface-accelerate",{"tool":75,"command":76},"pnpm","pnpm dlx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill huggingface-accelerate","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Faccelerate\u002FSKILL.md",true,[80,86,90,94,98,102,106,110,114,120],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"agents\u002Fdistillate-compressor.md","REFERENCE","参考文档",4951,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fagents\u002Fdistillate-compressor.md",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"agents\u002Fround-trip-reconstructor.md",2802,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fagents\u002Fround-trip-reconstructor.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fcustom-plugins.md",11781,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Faccelerate\u002Freferences\u002Fcustom-plugins.md",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fmegatron-integration.md",11252,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Faccelerate\u002Freferences\u002Fmegatron-integration.md",{"path":99,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fperformance.md",12569,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Faccelerate\u002Freferences\u002Fperformance.md",{"path":103,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":104,"sourceHtmlUrl":105},"resources\u002Fcompression-rules.md",2662,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fresources\u002Fcompression-rules.md",{"path":107,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":108,"sourceHtmlUrl":109},"resources\u002Fdistillate-format-reference.md",11880,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fresources\u002Fdistillate-format-reference.md",{"path":111,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":112,"sourceHtmlUrl":113},"resources\u002Fsplitting-strategy.md",3337,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fresources\u002Fsplitting-strategy.md",{"path":115,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":118,"sourceHtmlUrl":119},"scripts\u002Fanalyze_sources.py","SCRIPT","脚本",9912,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fscripts\u002Fanalyze_sources.py",{"path":121,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Ftests\u002Ftest_analyze_sources.py",7723,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.gemini\u002Fskills\u002Fbmad-distillator\u002Fscripts\u002Ftests\u002Ftest_analyze_sources.py",[125,152,169,197,230,264],{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":44},"01M6F2SKTE208174C7S7JJCZC7","write-contracts","Generates secure Aptos Move V2 smart contracts with Object model, Digital Asset integration, security patterns, and storage type guidance. Includes comprehensive storage decision framework for optimal data structure selection. Triggers on: 'write contract', 'create NFT collection', 'build marketplace', 'implement minting', 'generate Move module', 'create token contract', 'build DAO', 'implement staking'. Ask storage questions when: 'store', 'track', 'registry', 'mapping', 'list', 'collection'.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fwrite-contracts\u002FSKILL.md",[],[132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151],"write","contracts","write contracts","storage","secure","create","generate","collection","model","decision","ask","smart-contracts","selection","objects","structure","nft","token","aptos","critical","move",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":158,"publishedAt":44},"01M6F2RFXJ6T9MKW0A7N7GM45B","web-search","通用网络搜索技能，支持多引擎搜索（百度、必应、DuckDuckGo），无需API密钥即可获取实时信息","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fweb-search-ex-skill\u002FSKILL.md",[],[159,160,161,162,163,164,165,166,167,168],"web","search","web search","搜索","baidu","bing","duckduckgo","百度","多引擎","internet",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":178,"publishedAt":44},"01M6F2K968G1FZEZFPW2E6B222","solo-build","Execute implementation plan tasks with TDD workflow, auto-commit, and phase gates. Use when user says \"build it\", \"start building\", \"execute plan\", \"implement tasks\", \"ship it\", or references a track ID. Do NOT use for planning (use \u002Fplan) or scaffolding (use \u002Fscaffold).","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fsolo-build\u002FSKILL.md",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[179,180,181,182,183,184,185,186,160,187,188,189,132,190,191,192,193,194,195,196],"build","solo","solo build","MCP","workflow","commit","tasks","code","Bash","plan","read","auto","references","context7","query","scaffolding","ship it","project",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":209,"publishedAt":44},"01M6F2GKN39SGHSPWYB3662YC8","react-email","Use when building HTML email templates with React components, adding a visual email editor to an application using the React Email visual editor, rendering emails to HTML, or sending emails with Resend. Covers welcome emails, password resets, notifications, order confirmations, newsletters, transactional emails, and the embeddable email editor component.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Freact-email-skills\u002FSKILL.md",[203,206],{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229],"react","email","react email","github","editor","components","react components","component","welcome","https","notifications","password","node","use","html","resend","visual","emails","rendering","with",{"skillNo":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":246,"publishedAt":44},"01M6F2FBVGVDFSG8SSFJGXA40C","APITester Agent-Driven API Testing","Test API endpoints and document responses. Define tests in plain English, run them, get formatted results. Agent-driven Postman alternative.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fopenclaw-api-tester\u002FSKILL.md",[236,239,240,241],{"categoryNo":237,"categoryName":238,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":242,"categoryName":243,"taxonomyType":244,"taxonomyTypeDesc":245},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[247,26,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263],"testing","api testing","driven","APITester Agent Driven API Testing","test","run","postman","results","plain english","tester","endpoints","document","get","define","tests","english","api-tester",{"skillNo":265,"name":266,"nameZh":6,"description":267,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":268,"categories":269,"keywords":270,"publishedAt":44},"01M6F273Y3NGAPVJZ608BC0G9G","hubspot-implementation-plan","Generate a phased implementation plan from a HubSpot audit report. Creates prioritized, sequenced cleanup processes with effort estimates, dependencies, and automation feasibility. Use after running \u002Fhubspot-audit.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fhubspot-implementation-plan\u002FSKILL.md",[],[188,271,272,273,274,275,276,138,277,278,279,280,281,223,282,283,229,284],"implementation","hubspot","hubspot implementation plan","automation","feasibility","cleanup","planning","report","audit","audit report","phased implementation","dependencies","processes","hubspot audit"]