[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQF7C1H03NYDN1T8EBK67D":3,"skill-same:01M6KQF7C1H03NYDN1T8EBK67D":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":13,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6KQF7C1H03NYDN1T8EBK67D","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.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Faccelerate\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"HuggingFace","Accelerate","huggingface accelerate","automatic","support","orchestra","ecosystem","Unified API","DDP","FSDP","megatron","add","command","api","Transformers","Distributed Training","DeepSpeed","training","pytorch","config","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: Orchestra Research\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: 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Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[160,161],{"categoryNo":125,"categoryName":126,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":131,"categoryName":132,"taxonomyType":103,"taxonomyTypeDesc":104},[163,164,165,166,167,168,169,170,171,144,172,173,174,150,175,136],"slop","stop slop","stop","drafting","when","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":43},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[178,184],"文本",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":189,"sourceHtmlUrl":190,"categories":191,"keywords":192,"publishedAt":43},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[187,193,194,195,196,197,144,198,114,149,199,200,201,202,116,203,204,205,206,167,207,208,163],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":213,"sourceHtmlUrl":214,"categories":215,"keywords":217,"publishedAt":43},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[216],{"categoryNo":125,"categoryName":126,"taxonomyType":20,"taxonomyTypeDesc":21},[218,219,220,114,113,112,136,109,184,221,163,165],"humanizer","zh","humanizer zh","翻译"]