[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADWW5KWF3GEX61ANT8DX6Z":3,"skill-same:01M6ADWW5KWF3GEX61ANT8DX6Z":93},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6ADWW5KWF3GEX61ANT8DX6Z","post-training-miles",null,"Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8\u002FINT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-miles\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"training","post training miles","miles","post","ready","when","for","orchestra","production-ready","throughput","Megatron-LM","megatron","slime","alignment","inference","router","learning","Transformers","SGLang","Enterprise","FP8","reinforcement learning","research","2026-11-10 10:12:30",false,"---\nname: post-training-miles\ndescription: Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8\u002FINT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Reinforcement Learning, MoE, FP8, INT4, Enterprise, SGLang, Megatron-LM]\n  dependencies: [sglang-router>=0.2.3, ray, torch>=2.0.0, transformers>=4.40.0]\n---\n\n# miles: Enterprise-Grade RL for Large-Scale Model Training\n\nmiles is a high-performance, enterprise-ready RL framework optimized for large-scale model post-training. Built as a production fork of slime, it addresses critical challenges in MoE training stability, low-precision training, and train-inference alignment.\n\n## When to Use miles\n\n**Choose miles when you need:**\n- Training 1TB+ MoE models (DeepSeek V3, Qwen3-MoE)\n- FP8 or INT4 quantization-aware training\n- Bit-wise identical train-inference alignment\n- Speculative RL for maximum throughput\n- Production stability with enterprise support\n\n**Consider alternatives when:**\n- You want the research-grade original → use **slime**\n- You need flexible backend swapping → use **verl**\n- You want PyTorch-native abstractions → use **torchforge**\n\n## Key Features\n\n### Low-Precision Training\n- **Unified FP8**: End-to-end FP8 for both inference and training\n- **INT4 QAT**: 1TB models on single-machine VRAM (H200)\n- **Rollout Routing Replay (R3)**: Bit-wise expert alignment for MoE\n\n### Performance Optimizations\n- **Speculative RL**: 25%+ rollout speedup with online SFT draft models\n- **Zero-Copy Weight Sync**: CUDA IPC zero-copy mapping\n- **Partial Rollout**: Recycle half-finished trajectories\n\n### Train-Inference Alignment\n- **TIS\u002FMIS**: Truncated\u002FMasked Importance Sampling for off-policy correction\n- **Kernel-level optimization**: FlashAttention-3, DeepGEMM integration\n\n## Installation\n\n```bash\n# Recommended: Docker\ndocker pull radixark\u002Fmiles:latest\ndocker run --rm --gpus all --ipc=host --shm-size=16g \\\n  -it radixark\u002Fmiles:latest \u002Fbin\u002Fbash\n\n# From source\ngit clone https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles.git\ncd miles\npip install -r requirements.txt\npip install -e .\n```\n\n## Quick Start\n\nmiles inherits slime's configuration system. Basic training:\n\n```bash\npython train.py \\\n    --advantage-estimator grpo \\\n    --model-name qwen3-30b-a3b \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fqwen3-30b-a3b-hf \\\n    --rollout-batch-size 512 \\\n    --n-samples-per-prompt 8\n```\n\n---\n\n## Workflow 1: Large MoE Training\n\nUse this workflow for training large MoE models like DeepSeek V3 or Qwen3-MoE.\n\n### Prerequisites Checklist\n- [ ] H100\u002FH200 GPUs with FP8 support\n- [ ] MoE model (DeepSeek V3, Qwen3-MoE)\n- [ ] Docker environment with miles\n\n### Step 1: Environment Setup\n\n```bash\n# FP8 block scaling (recommended for stability)\nexport NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1\nexport CUDA_DEVICE_MAX_CONNECTIONS=1\n```\n\n### Step 2: Configure Training\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --rollout-num-gpus 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fdeepseek-v3 \\\n    --advantage-estimator grpo \\\n    --tensor-model-parallel-size 8 \\\n    --expert-model-parallel-size 4 \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl \\\n    --num-rollout 3000\n```\n\n### Verification Checklist\n- [ ] Model loads without errors\n- [ ] Routing decisions are consistent\n- [ ] No NaN\u002FInf in loss values\n\n---\n\n## Workflow 2: Speculative RL Training\n\nUse this workflow for maximum rollout throughput with EAGLE speculative decoding.\n\n### How Speculative RL Works\n\n1. Small draft model generates candidate tokens\n2. Target model verifies in parallel\n3. Draft model updated via online SFT to track policy\n\n### Step 1: Enable Speculative Decoding\n\nmiles supports EAGLE speculative decoding via SGLang:\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Ftarget-model \\\n    --sglang-speculative-algorithm EAGLE \\\n    --sglang-speculative-num-steps 3 \\\n    --sglang-speculative-eagle-topk 1 \\\n    --sglang-speculative-num-draft-tokens 4 \\\n    --sglang-speculative-draft-model-path \u002Fpath\u002Fto\u002Fdraft-model \\\n    --advantage-estimator grpo \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl\n```\n\n### Step 2: Enable Online MTP Training (Optional)\n\nFor online SFT of draft model during training:\n\n```bash\n--mtp-num-layers 1 \\\n--enable-mtp-training \\\n--mtp-loss-scaling-factor 0.2\n```\n\n**Note**: Online MTP training requires a torch dist checkpoint with MTP weights. Add `--mtp-num-layers 1` during checkpoint conversion from HuggingFace.\n\n### Expected Speedup\n\n- **Standard rollout**: Baseline\n- **Speculative RL**: 25-40% faster rollout\n- **With partial rollout**: Additional 10-15% throughput\n\n---\n\n## Configuration Reference\n\nmiles inherits all slime arguments. See [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete list.\n\n### Cluster Resources (from slime)\n\n```bash\n--actor-num-nodes 1\n--actor-num-gpus-per-node 8\n--rollout-num-gpus 8\n--rollout-num-gpus-per-engine 2\n--colocate\n```\n\n### Megatron Parallelism (from slime)\n\n```bash\n--tensor-model-parallel-size 8\n--pipeline-model-parallel-size 2\n--expert-model-parallel-size 4    # MoE expert parallelism\n```\n\n### Speculative Decoding (miles-specific)\n\n```bash\n--sglang-speculative-algorithm EAGLE\n--sglang-speculative-num-steps 3\n--sglang-speculative-eagle-topk 1\n--sglang-speculative-num-draft-tokens 4\n--sglang-enable-draft-weights-cpu-backup\n--sglang-speculative-draft-model-path \u002Fyour\u002Fdraft\u002Fmodel\u002Fpath\n```\n\n### Online MTP Training (miles-specific)\n\n```bash\n--mtp-num-layers 1\n--enable-mtp-training\n--mtp-loss-scaling-factor 0.2\n```\n\n---\n\n## Key Features (Conceptual)\n\nThe following features are documented in miles but specific CLI flags may vary. Consult the miles repository for latest configuration.\n\n### Unified FP8 Pipeline\n\nEnd-to-end FP8 sampling and training that eliminates quantization-induced discrepancy causing RL collapse in MoE models.\n\n### Rollout Routing Replay (R3)\n\nRecords expert routing decisions during SGLang inference and replays them during Megatron training for bit-wise expert alignment.\n\n**How R3 Works**:\n1. During SGLang inference, expert routing decisions are recorded\n2. Routing decisions stored in `sample.rollout_routed_experts`\n3. During Megatron training, routing is replayed instead of recomputed\n4. Ensures identical expert selection between train and inference\n\n### INT4 Quantization-Aware Training\n\nEnables single-machine deployment of 1TB+ models (e.g., on H200).\n\n**Memory Savings with INT4**:\n\n| Model Size | BF16 VRAM | INT4 VRAM | Reduction |\n|------------|-----------|-----------|-----------|\n| 70B | 140GB | 45GB | 3.1x |\n| 235B | 470GB | 150GB | 3.1x |\n| 671B | 1.3TB | 420GB | 3.1x |\n\n### Train-Inference Alignment\n\nmiles achieves \"exactly 0 KL divergence\" between training and inference through:\n- Flash Attention 3\n- DeepGEMM\n- Batch-invariant kernels from Thinking Machines Lab\n- `torch.compile` integration\n\n---\n\n## Sample Data Structure\n\nmiles uses the same `Sample` dataclass as slime with the `rollout_routed_experts` field for MoE routing replay:\n\n```python\n@dataclass\nclass Sample:\n    prompt: str | list[dict]\n    tokens: list[int]\n    response: str\n    reward: float | dict\n    loss_mask: list[int]\n    status: Status\n    metadata: dict\n    rollout_log_probs: list[float]\n    rollout_routed_experts: list[list[int]]  # MoE routing for R3\n```\n\nSee [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete Sample definition.\n\n---\n\n## Common Issues and Solutions\n\n### Issue: FP8 Training Collapse\n\n**Symptoms**: Loss explodes, NaN values\n\n**Solutions**:\n- Use block scaling: `export NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1`\n- Reduce learning rate: `--lr 5e-7`\n- Ensure MoE routing is consistent between train\u002Finference\n\n### Issue: Speculative Draft Drift\n\n**Symptoms**: Low acceptance rate over time\n\n**Solutions**:\n- Enable online MTP training to keep draft model aligned\n- Reduce speculative steps: `--sglang-speculative-num-steps 2`\n- Use CPU backup: `--sglang-enable-draft-weights-cpu-backup`\n\n### Issue: Train-Inference Mismatch\n\n**Symptoms**: Policy divergence, reward collapse\n\n**Solutions**:\n- Use TIS for off-policy correction: `--use-tis --tis-threshold 0.9`\n- Verify log probs match between SGLang and Megatron\n- Enable R3 for MoE models\n\n---\n\n## Supported Models\n\n| Family | Models | MoE Support |\n|--------|--------|-------------|\n| DeepSeek | R1, V3, V3.2 | Full |\n| Qwen | 2, 2.5, 3 (including MoE) | Full |\n| Llama | 3, 3.1, 3.3, 4 | Dense only |\n| Gemma | 2, 3, 3N | Dense only |\n| GLM | 4.5, 4.6, 4.7 | Dense only |\n| MiniMax | M2, M2.1 | Full |\n\n---\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles\n- **Introduction Blog**: https:\u002F\u002Flmsys.org\u002Fblog\u002F2025-11-19-miles\u002F\n- **Slime (upstream)**: https:\u002F\u002Fgithub.com\u002FTHUDM\u002Fslime\n- **SGLang**: https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang\n\n","# miles: Enterprise-Grade RL for Large-Scale Model Training\n\nmiles is a high-performance, enterprise-ready RL framework optimized for large-scale model post-training. Built as a production fork of slime, it addresses critical challenges in MoE training stability, low-precision training, and train-inference alignment.\n\n## When to Use miles\n\n**Choose miles when you need:**\n- Training 1TB+ MoE models (DeepSeek V3, Qwen3-MoE)\n- FP8 or INT4 quantization-aware training\n- Bit-wise identical train-inference alignment\n- Speculative RL for maximum throughput\n- Production stability with enterprise support\n\n**Consider alternatives when:**\n- You want the research-grade original → use **slime**\n- You need flexible backend swapping → use **verl**\n- You want PyTorch-native abstractions → use **torchforge**\n\n## Key Features\n\n### Low-Precision Training\n- **Unified FP8**: End-to-end FP8 for both inference and training\n- **INT4 QAT**: 1TB models on single-machine VRAM (H200)\n- **Rollout Routing Replay (R3)**: Bit-wise expert alignment for MoE\n\n### Performance Optimizations\n- **Speculative RL**: 25%+ rollout speedup with online SFT draft models\n- **Zero-Copy Weight Sync**: CUDA IPC zero-copy mapping\n- **Partial Rollout**: Recycle half-finished trajectories\n\n### Train-Inference Alignment\n- **TIS\u002FMIS**: Truncated\u002FMasked Importance Sampling for off-policy correction\n- **Kernel-level optimization**: FlashAttention-3, DeepGEMM integration\n\n## Installation\n\n```bash\n# Recommended: Docker\ndocker pull radixark\u002Fmiles:latest\ndocker run --rm --gpus all --ipc=host --shm-size=16g \\\n  -it radixark\u002Fmiles:latest \u002Fbin\u002Fbash\n\n# From source\ngit clone https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles.git\ncd miles\npip install -r requirements.txt\npip install -e .\n```\n\n## Quick Start\n\nmiles inherits slime's configuration system. Basic training:\n\n```bash\npython train.py \\\n    --advantage-estimator grpo \\\n    --model-name qwen3-30b-a3b \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fqwen3-30b-a3b-hf \\\n    --rollout-batch-size 512 \\\n    --n-samples-per-prompt 8\n```\n\n---\n\n## Workflow 1: Large MoE Training\n\nUse this workflow for training large MoE models like DeepSeek V3 or Qwen3-MoE.\n\n### Prerequisites Checklist\n- [ ] H100\u002FH200 GPUs with FP8 support\n- [ ] MoE model (DeepSeek V3, Qwen3-MoE)\n- [ ] Docker environment with miles\n\n### Step 1: Environment Setup\n\n```bash\n# FP8 block scaling (recommended for stability)\nexport NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1\nexport CUDA_DEVICE_MAX_CONNECTIONS=1\n```\n\n### Step 2: Configure Training\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --rollout-num-gpus 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fdeepseek-v3 \\\n    --advantage-estimator grpo \\\n    --tensor-model-parallel-size 8 \\\n    --expert-model-parallel-size 4 \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl \\\n    --num-rollout 3000\n```\n\n### Verification Checklist\n- [ ] Model loads without errors\n- [ ] Routing decisions are consistent\n- [ ] No NaN\u002FInf in loss values\n\n---\n\n## Workflow 2: Speculative RL Training\n\nUse this workflow for maximum rollout throughput with EAGLE speculative decoding.\n\n### How Speculative RL Works\n\n1. Small draft model generates candidate tokens\n2. Target model verifies in parallel\n3. Draft model updated via online SFT to track policy\n\n### Step 1: Enable Speculative Decoding\n\nmiles supports EAGLE speculative decoding via SGLang:\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Ftarget-model \\\n    --sglang-speculative-algorithm EAGLE \\\n    --sglang-speculative-num-steps 3 \\\n    --sglang-speculative-eagle-topk 1 \\\n    --sglang-speculative-num-draft-tokens 4 \\\n    --sglang-speculative-draft-model-path \u002Fpath\u002Fto\u002Fdraft-model \\\n    --advantage-estimator grpo \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl\n```\n\n### Step 2: Enable Online MTP Training (Optional)\n\nFor online SFT of draft model during training:\n\n```bash\n--mtp-num-layers 1 \\\n--enable-mtp-training \\\n--mtp-loss-scaling-factor 0.2\n```\n\n**Note**: Online MTP training requires a torch dist checkpoint with MTP weights. Add `--mtp-num-layers 1` during checkpoint conversion from HuggingFace.\n\n### Expected Speedup\n\n- **Standard rollout**: Baseline\n- **Speculative RL**: 25-40% faster rollout\n- **With partial rollout**: Additional 10-15% throughput\n\n---\n\n## Configuration Reference\n\nmiles inherits all slime arguments. See [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete list.\n\n### Cluster Resources (from slime)\n\n```bash\n--actor-num-nodes 1\n--actor-num-gpus-per-node 8\n--rollout-num-gpus 8\n--rollout-num-gpus-per-engine 2\n--colocate\n```\n\n### Megatron Parallelism (from slime)\n\n```bash\n--tensor-model-parallel-size 8\n--pipeline-model-parallel-size 2\n--expert-model-parallel-size 4    # MoE expert parallelism\n```\n\n### Speculative Decoding (miles-specific)\n\n```bash\n--sglang-speculative-algorithm EAGLE\n--sglang-speculative-num-steps 3\n--sglang-speculative-eagle-topk 1\n--sglang-speculative-num-draft-tokens 4\n--sglang-enable-draft-weights-cpu-backup\n--sglang-speculative-draft-model-path \u002Fyour\u002Fdraft\u002Fmodel\u002Fpath\n```\n\n### Online MTP Training (miles-specific)\n\n```bash\n--mtp-num-layers 1\n--enable-mtp-training\n--mtp-loss-scaling-factor 0.2\n```\n\n---\n\n## Key Features (Conceptual)\n\nThe following features are documented in miles but specific CLI flags may vary. Consult the miles repository for latest configuration.\n\n### Unified FP8 Pipeline\n\nEnd-to-end FP8 sampling and training that eliminates quantization-induced discrepancy causing RL collapse in MoE models.\n\n### Rollout Routing Replay (R3)\n\nRecords expert routing decisions during SGLang inference and replays them during Megatron training for bit-wise expert alignment.\n\n**How R3 Works**:\n1. During SGLang inference, expert routing decisions are recorded\n2. Routing decisions stored in `sample.rollout_routed_experts`\n3. During Megatron training, routing is replayed instead of recomputed\n4. Ensures identical expert selection between train and inference\n\n### INT4 Quantization-Aware Training\n\nEnables single-machine deployment of 1TB+ models (e.g., on H200).\n\n**Memory Savings with INT4**:\n\n| Model Size | BF16 VRAM | INT4 VRAM | Reduction |\n|------------|-----------|-----------|-----------|\n| 70B | 140GB | 45GB | 3.1x |\n| 235B | 470GB | 150GB | 3.1x |\n| 671B | 1.3TB | 420GB | 3.1x |\n\n### Train-Inference Alignment\n\nmiles achieves \"exactly 0 KL divergence\" between training and inference through:\n- Flash Attention 3\n- DeepGEMM\n- Batch-invariant kernels from Thinking Machines Lab\n- `torch.compile` integration\n\n---\n\n## Sample Data Structure\n\nmiles uses the same `Sample` dataclass as slime with the `rollout_routed_experts` field for MoE routing replay:\n\n```python\n@dataclass\nclass Sample:\n    prompt: str | list[dict]\n    tokens: list[int]\n    response: str\n    reward: float | dict\n    loss_mask: list[int]\n    status: Status\n    metadata: dict\n    rollout_log_probs: list[float]\n    rollout_routed_experts: list[list[int]]  # MoE routing for R3\n```\n\nSee [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete Sample definition.\n\n---\n\n## Common Issues and Solutions\n\n### Issue: FP8 Training Collapse\n\n**Symptoms**: Loss explodes, NaN values\n\n**Solutions**:\n- Use block scaling: `export NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1`\n- Reduce learning rate: `--lr 5e-7`\n- Ensure MoE routing is consistent between train\u002Finference\n\n### Issue: Speculative Draft Drift\n\n**Symptoms**: Low acceptance rate over time\n\n**Solutions**:\n- Enable online MTP training to keep draft model aligned\n- Reduce speculative steps: `--sglang-speculative-num-steps 2`\n- Use CPU backup: `--sglang-enable-draft-weights-cpu-backup`\n\n### Issue: Train-Inference Mismatch\n\n**Symptoms**: Policy divergence, reward collapse\n\n**Solutions**:\n- Use TIS for off-policy correction: `--use-tis --tis-threshold 0.9`\n- Verify log probs match between SGLang and Megatron\n- Enable R3 for MoE models\n\n---\n\n## Supported Models\n\n| Family | Models | MoE Support |\n|--------|--------|-------------|\n| DeepSeek | R1, V3, V3.2 | Full |\n| Qwen | 2, 2.5, 3 (including MoE) | Full |\n| Llama | 3, 3.1, 3.3, 4 | Dense only |\n| Gemma | 2, 3, 3N | Dense only |\n| GLM | 4.5, 4.6, 4.7 | Dense only |\n| MiniMax | M2, M2.1 | Full |\n\n---\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles\n- **Introduction Blog**: https:\u002F\u002Flmsys.org\u002Fblog\u002F2025-11-19-miles\u002F\n- **Slime (upstream)**: https:\u002F\u002Fgithub.com\u002FTHUDM\u002Fslime\n- **SGLang**: https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang\n\n",{"name":5,"license":15,"metadata":47,"description":7},{"tags":48,"author":52,"version":53,"dependencies":54},[49,50,39,51,38,37,29],"Reinforcement Learning","MoE","INT4","Orchestra Research","1.0.0",[55,56,57,58],"sglang-router>=0.2.3","ray","torch>=2.0.0","transformers>=4.40.0","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-miles",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add davila7\u002Fclaude-code-templates --skill post-training-miles",{"tool":75,"command":76},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill post-training-miles",{"tool":78,"command":79},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill post-training-miles","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-miles\u002FSKILL.md",true,[83,89],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fapi-reference.md","REFERENCE","参考文档",4137,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-miles\u002Freferences\u002Fapi-reference.md",{"path":90,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Ftroubleshooting.md",5814,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-miles\u002Freferences\u002Ftroubleshooting.md",[94,124,151,184,219,243],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":105,"publishedAt":42},"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",[100],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[106,107,108,109,110,25,111,112,113,24,114,115,116,117,118,119,120,121,122,123],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":131,"publishedAt":42},"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",[130],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[132,133,134,135,136,137,138,139,140,141,142,24,143,144,145,146,147,148,149,150],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":166,"publishedAt":42},"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",[157,160,161],{"categoryNo":158,"categoryName":159,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},{"categoryNo":162,"categoryName":163,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[167,168,169,170,171,172,173,25,174,175,176,177,178,179,180,113,181,182,24,183],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":188,"licenseFileUrl":189,"sourceHtmlUrl":190,"categories":191,"keywords":195,"publishedAt":42},"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",[192],{"categoryNo":193,"categoryName":194,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000009","前端开发",[196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,25,218],"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":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":188,"licenseFileUrl":223,"sourceHtmlUrl":224,"categories":225,"keywords":230,"publishedAt":42},"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",[226,229],{"categoryNo":227,"categoryName":228,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":193,"categoryName":194,"taxonomyType":103,"taxonomyTypeDesc":104},[231,232,233,234,235,197,236,206,207,237,238,239,240,25,241,115,242],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":244,"name":245,"nameZh":6,"description":246,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":188,"licenseFileUrl":247,"sourceHtmlUrl":248,"categories":249,"keywords":257,"publishedAt":42},"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",[250,253,254],{"categoryNo":251,"categoryName":252,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},{"categoryNo":255,"categoryName":256,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000036","内容创作者",[258,259,260,261,262,263,264,265,199,266,209,143,267,268,269,270,271,179,272,24,273,274,275,276,277,121,278],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]