[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71FP4VZK7B64VDAXYKA038K":3,"skill-same:01M71FP4VZK7B64VDAXYKA038K":90},{"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":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":13,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M71FP4VZK7B64VDAXYKA038K","simpo-training",null,"Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO\u002FPPO.","gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6","gjbvsjjvgm-sketch","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F278511911?v=4","01M71F3JXKZQ70H2KCHHTXKZDA","01M71F3JY8A79Z3NZBW08TF4JD",0,"MIT","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Fsimpo\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"training","simpo training","simpo","when","performance","for","orchestra","LLM Alignment","Reference-Free","DPO Alternative","Preference Optimization","Efficient Training","TRL","Accelerate","free","alignment","model","Transformers","Post-Training","Optimization","2026-11-10 10:12:30",false,"---\nname: simpo-training\ndescription: Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO\u002FPPO.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [torch, transformers, datasets, trl, accelerate]\nmetadata:\n  hermes:\n    tags: [Post-Training, SimPO, Preference Optimization, Alignment, DPO Alternative, Reference-Free, LLM Alignment, Efficient Training]\n\n---\n\n# SimPO - Simple Preference Optimization\n\n## Quick start\n\nSimPO is a reference-free preference optimization method that outperforms DPO without needing a reference model.\n\n**Installation**:\n```bash\n# Create environment\nconda create -n simpo python=3.10 && conda activate simpo\n\n# Install PyTorch 2.2.2\n# Visit: https:\u002F\u002Fpytorch.org\u002Fget-started\u002Flocally\u002F\n\n# Install alignment-handbook\ngit clone https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Falignment-handbook.git\ncd alignment-handbook\npython -m pip install .\n\n# Install Flash Attention 2\npython -m pip install flash-attn --no-build-isolation\n```\n\n**Training** (Mistral 7B):\n```bash\nACCELERATE_LOG_LEVEL=info accelerate launch \\\n  --config_file accelerate_configs\u002Fdeepspeed_zero3.yaml \\\n  scripts\u002Frun_simpo.py \\\n  training_configs\u002Fmistral-7b-base-simpo.yaml\n```\n\n## Common workflows\n\n### Workflow 1: Train from base model (Mistral 7B)\n\n**Config** (`mistral-7b-base-simpo.yaml`):\n```yaml\n# Model\nmodel_name_or_path: mistralai\u002FMistral-7B-v0.1\ntorch_dtype: bfloat16\n\n# Dataset\ndataset_mixer:\n  HuggingFaceH4\u002Fultrafeedback_binarized: 1.0\ndataset_splits:\n  - train_prefs\n  - test_prefs\n\n# SimPO hyperparameters\nbeta: 2.0                  # Reward scaling (2.0-10.0)\ngamma_beta_ratio: 0.5       # Target margin (0-1)\nloss_type: sigmoid          # sigmoid or hinge\nsft_weight: 0.0             # Optional SFT regularization\n\n# Training\nlearning_rate: 5e-7         # Critical: 3e-7 to 1e-6\nnum_train_epochs: 1\nper_device_train_batch_size: 1\ngradient_accumulation_steps: 8\n\n# Output\noutput_dir: .\u002Foutputs\u002Fmistral-7b-simpo\n```\n\n**Launch training**:\n```bash\naccelerate launch --config_file accelerate_configs\u002Fdeepspeed_zero3.yaml \\\n  scripts\u002Frun_simpo.py training_configs\u002Fmistral-7b-base-simpo.yaml\n```\n\n### Workflow 2: Fine-tune instruct model (Llama 3 8B)\n\n**Config** (`llama3-8b-instruct-simpo.yaml`):\n```yaml\nmodel_name_or_path: meta-llama\u002FMeta-Llama-3-8B-Instruct\n\ndataset_mixer:\n  argilla\u002Fultrafeedback-binarized-preferences-cleaned: 1.0\n\nbeta: 2.5\ngamma_beta_ratio: 0.5\nlearning_rate: 5e-7\nsft_weight: 0.1             # Add SFT loss to preserve capabilities\n\nnum_train_epochs: 1\nper_device_train_batch_size: 2\ngradient_accumulation_steps: 4\noutput_dir: .\u002Foutputs\u002Fllama3-8b-simpo\n```\n\n**Launch**:\n```bash\naccelerate launch --config_file accelerate_configs\u002Fdeepspeed_zero3.yaml \\\n  scripts\u002Frun_simpo.py training_configs\u002Fllama3-8b-instruct-simpo.yaml\n```\n\n### Workflow 3: Reasoning-intensive tasks (lower LR)\n\n**For math\u002Fcode tasks**:\n```yaml\nmodel_name_or_path: deepseek-ai\u002Fdeepseek-math-7b-base\n\ndataset_mixer:\n  argilla\u002Fdistilabel-math-preference-dpo: 1.0\n\nbeta: 5.0                   # Higher for stronger signal\ngamma_beta_ratio: 0.7       # Larger margin\nlearning_rate: 3e-7         # Lower LR for reasoning\nsft_weight: 0.0\n\nnum_train_epochs: 1\nper_device_train_batch_size: 1\ngradient_accumulation_steps: 16\n```\n\n## When to use vs alternatives\n\n**Use SimPO when**:\n- Want simpler training than DPO (no reference model)\n- Have preference data (chosen\u002Frejected pairs)\n- Need better performance than DPO\n- Limited compute resources\n- Single-node training sufficient\n\n**Algorithm selection**:\n- **SimPO**: Simplest, best performance, no reference model\n- **DPO**: Need reference model baseline, more conservative\n- **PPO**: Maximum control, need reward model, complex setup\n- **GRPO**: Memory-efficient RL, no critic\n\n**Use alternatives instead**:\n- **OpenRLHF**: Multi-node distributed training, PPO\u002FGRPO\n- **TRL**: Need multiple methods in one framework\n- **DPO**: Established baseline comparison\n\n## Common issues\n\n**Issue: Loss divergence**\n\nReduce learning rate:\n```yaml\nlearning_rate: 3e-7  # Reduce from 5e-7\n```\n\nReduce beta:\n```yaml\nbeta: 1.0  # Reduce from 2.0\n```\n\n**Issue: Model forgets capabilities**\n\nAdd SFT regularization:\n```yaml\nsft_weight: 0.1  # Add SFT loss component\n```\n\n**Issue: Poor preference separation**\n\nIncrease beta and margin:\n```yaml\nbeta: 5.0            # Increase from 2.0\ngamma_beta_ratio: 0.8  # Increase from 0.5\n```\n\n**Issue: OOM during training**\n\nReduce batch size:\n```yaml\nper_device_train_batch_size: 1\ngradient_accumulation_steps: 16  # Maintain effective batch\n```\n\nEnable gradient checkpointing:\n```yaml\ngradient_checkpointing: true\n```\n\n## Advanced topics\n\n**Loss functions**: See [references\u002Floss-functions.md](references\u002Floss-functions.md) for sigmoid vs hinge loss, mathematical formulations, and when to use each.\n\n**Hyperparameter tuning**: See [references\u002Fhyperparameters.md](references\u002Fhyperparameters.md) for beta, gamma, learning rate selection guide, and model-size-specific recommendations.\n\n**Dataset preparation**: See [references\u002Fdatasets.md](references\u002Fdatasets.md) for preference data formats, quality filtering, and custom dataset creation.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA A100\u002FH100 recommended\n- **VRAM**:\n  - 7B model: 1× A100 40GB (DeepSpeed ZeRO-3)\n  - 8B model: 2× A100 40GB\n  - 70B model: 8× A100 80GB\n- **Single-node**: DeepSpeed ZeRO-3 sufficient\n- **Mixed precision**: BF16 recommended\n\n**Memory optimization**:\n- DeepSpeed ZeRO-3 (default config)\n- Gradient checkpointing\n- Flash Attention 2\n\n## Resources\n\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.14734 (NeurIPS 2024)\n- GitHub: https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002FSimPO\n- Models: https:\u002F\u002Fhuggingface.co\u002Fprinceton-nlp\n- Alignment Handbook: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Falignment-handbook\n\n\n\n","# SimPO - Simple Preference Optimization\n\n## Quick start\n\nSimPO is a reference-free preference optimization method that outperforms DPO without needing a reference model.\n\n**Installation**:\n```bash\n# Create environment\nconda create -n simpo python=3.10 && conda activate simpo\n\n# Install PyTorch 2.2.2\n# Visit: https:\u002F\u002Fpytorch.org\u002Fget-started\u002Flocally\u002F\n\n# Install alignment-handbook\ngit clone https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Falignment-handbook.git\ncd alignment-handbook\npython -m pip install .\n\n# Install Flash Attention 2\npython -m pip install flash-attn --no-build-isolation\n```\n\n**Training** (Mistral 7B):\n```bash\nACCELERATE_LOG_LEVEL=info accelerate launch \\\n  --config_file accelerate_configs\u002Fdeepspeed_zero3.yaml \\\n  scripts\u002Frun_simpo.py \\\n  training_configs\u002Fmistral-7b-base-simpo.yaml\n```\n\n## Common workflows\n\n### Workflow 1: Train from base model (Mistral 7B)\n\n**Config** (`mistral-7b-base-simpo.yaml`):\n```yaml\n# Model\nmodel_name_or_path: mistralai\u002FMistral-7B-v0.1\ntorch_dtype: bfloat16\n\n# Dataset\ndataset_mixer:\n  HuggingFaceH4\u002Fultrafeedback_binarized: 1.0\ndataset_splits:\n  - train_prefs\n  - test_prefs\n\n# SimPO hyperparameters\nbeta: 2.0                  # Reward scaling (2.0-10.0)\ngamma_beta_ratio: 0.5       # Target margin (0-1)\nloss_type: sigmoid          # sigmoid or hinge\nsft_weight: 0.0             # Optional SFT regularization\n\n# Training\nlearning_rate: 5e-7         # Critical: 3e-7 to 1e-6\nnum_train_epochs: 1\nper_device_train_batch_size: 1\ngradient_accumulation_steps: 8\n\n# Output\noutput_dir: .\u002Foutputs\u002Fmistral-7b-simpo\n```\n\n**Launch training**:\n```bash\naccelerate launch --config_file accelerate_configs\u002Fdeepspeed_zero3.yaml \\\n  scripts\u002Frun_simpo.py training_configs\u002Fmistral-7b-base-simpo.yaml\n```\n\n### Workflow 2: Fine-tune instruct model (Llama 3 8B)\n\n**Config** (`llama3-8b-instruct-simpo.yaml`):\n```yaml\nmodel_name_or_path: meta-llama\u002FMeta-Llama-3-8B-Instruct\n\ndataset_mixer:\n  argilla\u002Fultrafeedback-binarized-preferences-cleaned: 1.0\n\nbeta: 2.5\ngamma_beta_ratio: 0.5\nlearning_rate: 5e-7\nsft_weight: 0.1             # Add SFT loss to preserve capabilities\n\nnum_train_epochs: 1\nper_device_train_batch_size: 2\ngradient_accumulation_steps: 4\noutput_dir: .\u002Foutputs\u002Fllama3-8b-simpo\n```\n\n**Launch**:\n```bash\naccelerate launch --config_file accelerate_configs\u002Fdeepspeed_zero3.yaml \\\n  scripts\u002Frun_simpo.py training_configs\u002Fllama3-8b-instruct-simpo.yaml\n```\n\n### Workflow 3: Reasoning-intensive tasks (lower LR)\n\n**For math\u002Fcode tasks**:\n```yaml\nmodel_name_or_path: deepseek-ai\u002Fdeepseek-math-7b-base\n\ndataset_mixer:\n  argilla\u002Fdistilabel-math-preference-dpo: 1.0\n\nbeta: 5.0                   # Higher for stronger signal\ngamma_beta_ratio: 0.7       # Larger margin\nlearning_rate: 3e-7         # Lower LR for reasoning\nsft_weight: 0.0\n\nnum_train_epochs: 1\nper_device_train_batch_size: 1\ngradient_accumulation_steps: 16\n```\n\n## When to use vs alternatives\n\n**Use SimPO when**:\n- Want simpler training than DPO (no reference model)\n- Have preference data (chosen\u002Frejected pairs)\n- Need better performance than DPO\n- Limited compute resources\n- Single-node training sufficient\n\n**Algorithm selection**:\n- **SimPO**: Simplest, best performance, no reference model\n- **DPO**: Need reference model baseline, more conservative\n- **PPO**: Maximum control, need reward model, complex setup\n- **GRPO**: Memory-efficient RL, no critic\n\n**Use alternatives instead**:\n- **OpenRLHF**: Multi-node distributed training, PPO\u002FGRPO\n- **TRL**: Need multiple methods in one framework\n- **DPO**: Established baseline comparison\n\n## Common issues\n\n**Issue: Loss divergence**\n\nReduce learning rate:\n```yaml\nlearning_rate: 3e-7  # Reduce from 5e-7\n```\n\nReduce beta:\n```yaml\nbeta: 1.0  # Reduce from 2.0\n```\n\n**Issue: Model forgets capabilities**\n\nAdd SFT regularization:\n```yaml\nsft_weight: 0.1  # Add SFT loss component\n```\n\n**Issue: Poor preference separation**\n\nIncrease beta and margin:\n```yaml\nbeta: 5.0            # Increase from 2.0\ngamma_beta_ratio: 0.8  # Increase from 0.5\n```\n\n**Issue: OOM during training**\n\nReduce batch size:\n```yaml\nper_device_train_batch_size: 1\ngradient_accumulation_steps: 16  # Maintain effective batch\n```\n\nEnable gradient checkpointing:\n```yaml\ngradient_checkpointing: true\n```\n\n## Advanced topics\n\n**Loss functions**: See [references\u002Floss-functions.md](references\u002Floss-functions.md) for sigmoid vs hinge loss, mathematical formulations, and when to use each.\n\n**Hyperparameter tuning**: See [references\u002Fhyperparameters.md](references\u002Fhyperparameters.md) for beta, gamma, learning rate selection guide, and model-size-specific recommendations.\n\n**Dataset preparation**: See [references\u002Fdatasets.md](references\u002Fdatasets.md) for preference data formats, quality filtering, and custom dataset creation.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA A100\u002FH100 recommended\n- **VRAM**:\n  - 7B model: 1× A100 40GB (DeepSpeed ZeRO-3)\n  - 8B model: 2× A100 40GB\n  - 70B model: 8× A100 80GB\n- **Single-node**: DeepSpeed ZeRO-3 sufficient\n- **Mixed precision**: BF16 recommended\n\n**Memory optimization**:\n- DeepSpeed ZeRO-3 (default config)\n- Gradient checkpointing\n- Flash Attention 2\n\n## Resources\n\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.14734 (NeurIPS 2024)\n- GitHub: https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002FSimPO\n- Models: https:\u002F\u002Fhuggingface.co\u002Fprinceton-nlp\n- Alignment Handbook: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Falignment-handbook\n\n\n\n",{"name":5,"author":43,"license":14,"version":44,"metadata":45,"description":7,"dependencies":50},"Orchestra Research","1.0.0",{"hermes":46},{"tags":47},[36,48,28,49,27,26,25,29],"SimPO","Alignment",[51,52,53,54,55],"torch","transformers","datasets","trl","accelerate","main","93bbc269671fbb340afa15b36a8766f15dc30df2","hermes-agent\u002Foptional-skills\u002Fmlops\u002Fsimpo","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch","2026-04-27 02:51:28",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill simpo-training",{"tool":68,"command":69},"bunx","bunx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill simpo-training",{"tool":71,"command":72},"pnpm","pnpm dlx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill simpo-training","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Fsimpo\u002FSKILL.md",true,[76,82,86],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fdatasets.md","REFERENCE","参考文档",10889,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Fsimpo\u002Freferences\u002Fdatasets.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fhyperparameters.md",8502,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Fsimpo\u002Freferences\u002Fhyperparameters.md",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Floss-functions.md",7067,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Fsimpo\u002Freferences\u002Floss-functions.md",[91,132,160,176,195,216],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":113,"publishedAt":38},"01M71G6TJ2RAQ1J0PV3K6D9GQA","writing-plans","Use when you have a spec or requirements for a multi-step task. Creates comprehensive implementation plans with bite-sized tasks, exact file paths, and complete code examples.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fwriting-plans\u002FSKILL.md",[97,102,105,110],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000036","内容创作者",[114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,23,131,21],"writing","writing plans","plans","design","review","workflow","documentation","tasks","development","code","planning","code-review","subagent","superpowers","paths","test-driven-development","you","test-driven",{"skillNo":133,"name":129,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":145,"publishedAt":38},"01M71G6R81013R3T7W5VP56GCB","Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle with test-first approach.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Ftest-driven-development\u002FSKILL.md",[137,140,143,144],{"categoryNo":138,"categoryName":139,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":141,"categoryName":142,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},[146,147,122,148,149,114,150,123,151,126,127,152,153,154,155,156,157,158,159,21],"test","test driven development","driven","testing","debugging","refactor","red-green-refactor","cycle","green","systematic","bugfix","implementing","systematic-debugging","test-first",{"skillNo":161,"name":158,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":166,"publishedAt":38},"01M71G6N0Y546JZEDF25E2PMCE","Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fsystematic-debugging\u002FSKILL.md",[165],{"categoryNo":141,"categoryName":142,"taxonomyType":100,"taxonomyTypeDesc":101},[167,150,155,114,146,122,126,168,127,169,170,171,116,172,129,173,131,174,175,21],"systematic debugging","use","bug","root-cause","problem-solving","problem","fixes","phase","root cause",{"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":6,"sourceHtmlUrl":180,"categories":181,"keywords":188,"publishedAt":38},"01M71G6J0DXEGY48GH4KVW2SJH","subagent-driven-development","Use when executing implementation plans with independent tasks. Dispatches fresh delegate_task per task with two-stage review (spec compliance then code quality).","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fsubagent-driven-development\u002FSKILL.md",[182,183,184,185],{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":186,"categoryName":187,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000037","律师与法务",[122,189,126,148,114,118,119,190,121,123,191,125,192,193,127,116,194,129,131,21],"subagent driven development","code quality","delegation","parallel","delegate","fresh",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":203,"publishedAt":38},"01M71G6EFFBQX6634E6XHB09Y1","requesting-code-review","Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Frequesting-code-review\u002FSKILL.md",[201,202],{"categoryNo":138,"categoryName":139,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},[204,118,123,205,114,206,125,207,208,209,210,126,211,212,213,127,129,214,215,131],"code review","requesting code review","commit","verification","security scan","pipeline","loop","reviewer","fix","auto","static","auto-fix",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":220,"categories":221,"keywords":222,"publishedAt":38},"01M71G6CM8ANSWVFXCN9AAD58Y","plan","Plan mode for Hermes — inspect context, write a markdown plan into the active workspace's `.hermes\u002Fplans\u002F` directory, and do not execute the work.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fplan\u002FSKILL.md",[],[218,114,223,119,122,124,224,126,225,226,227,228,229,230,116,231,232,233,23,234],"directory","markdown","workspace","work","write","implementation","do","active","plan mode","inspect","mode","plan-mode"]