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Name the relevant recipe file or YAML section.\n2. List the builder functions or config keys involved.\n3. Include a minimal YAML or command example when the question asks how to\n   configure something.\n4. End with a local validation command or tiny CPU-compatible test.\n\nFor conceptual recipe questions, answer from this skill without inspecting the\nrepository or loading other AutoModel skills unless the user asks you to edit\nfiles. Keep the response focused on recipe YAML, builders, CLI routing, tests,\nand local validation.\n\nUse these compact answer patterns for common questions:\n\n- New finetuning recipe variant: start from the closest file under\n  `nemo_automodel\u002Frecipes\u002F`, update the model, dataset or dataloader,\n  optimizer, loss, LR scheduler, step scheduler, and checkpoint builders,\n  register a CLI route only if adding a command or domain alias, add example\n  YAML under `examples\u002F`, then add a tiny CPU-compatible unit test and run\n  `automodel finetune llm -c \u003Cconfig.yaml>`.\n- `_target_` fields: describe `_target_` as the fully qualified Python callable,\n  explain that sibling keys become keyword arguments, show optimizer and dataset\n  examples, and mention nested CLI overrides such as `--optimizer.lr`.\n- Validation and checkpointing: name `step_scheduler.val_check_interval`,\n  `step_scheduler.checkpoint_interval`, `validation_dataset`,\n  `restore_from.path`, and consolidated safetensors; include the minimal YAML\n  snippet from this skill.\n\nFor validation and checkpointing, always name:\n\n- `step_scheduler.val_check_interval` for validation cadence.\n- `step_scheduler.checkpoint_interval` for save cadence.\n- `validation_dataset` as the validation dataloader source.\n- `restore_from.path` for resume.\n- Consolidated safetensors as the default checkpoint format for HF ecosystem\n  compatibility.\n\n## Routing Boundary\n\nUse this skill for recipe construction and execution-flow questions: YAML\nstructure, `_target_` callables, builder functions, validation datasets,\ncheckpoint configuration, CLI route registration, and recipe-specific tests.\n\nDo not use this skill for standalone distributed strategy selection, cluster\nlauncher configuration, or model architecture onboarding unless the user is\nasking how those choices appear inside an AutoModel recipe YAML.\n\n## Recipe Architecture\n\n### Execution Flow\n\n```\nCLI (automodel finetune llm -c config.yaml)\n  -> app.py parses command + domain + config\n    -> recipe script (e.g. train_ft.py) main(config_path)\n      -> Recipe class .setup() builds all components\n        -> .run_train_validation_loop() executes training\n```\n\n### Recipe Class\n\nRecipes inherit from `BaseRecipe` and implement two methods:\n\n- `setup()` -- builds model, optimizer, dataloader, loss, LR scheduler, step scheduler, and checkpoint config via builder functions.\n- `run_train_validation_loop()` -- executes the training and validation loop.\n\n### Builder Pattern\n\nAll components are constructed through dedicated builder functions:\n\n- `build_model()` -- instantiates the model from config\n- `build_optimizer()` -- creates optimizer (AdamW, etc.)\n- `build_dataloader()` -- sets up train and validation dataloaders\n- `build_loss_fn()` -- creates the loss function\n- `build_lr_scheduler()` -- creates the learning rate scheduler\n- `build_step_scheduler()` -- creates the step scheduler controlling training progression\n- `build_checkpoint_config()` -- configures checkpointing\n\n### Infrastructure Application Order\n\nComponents are applied in this strict order after building:\n\n1. PEFT (LoRA, etc.)\n2. FP8 quantization\n3. QAT (quantization-aware training)\n4. Checkpoint load \u002F restore\n5. Parameter freezing\n6. Sharding (FSDP2, Megatron-FSDP, DDP)\n7. Device placement\n8. `torch.compile`\n9. Context parallelism hooks\n\n## YAML Config Anatomy\n\nA complete recipe config follows this structure:\n\n```yaml\nstep_scheduler:\n  max_steps: 1000\n  num_epochs: 1\n  grad_accumulation_steps: 4\n  val_check_interval: 100\n  checkpoint_interval: 500\n  log_interval: 10\n\ndist_env:\n  master_addr: localhost\n  master_port: 29500\n\nrng:\n  seed: 42\n\nmodel:\n  _target_: nemo_automodel.models.llm.NemotronHForCausalLM\n  name_or_path: meta-llama\u002FLlama-3.2-1B\n  # additional model kwargs passed to the constructor\n\ncompile:\n  enabled: false\n  backend: inductor\n\nclip_grad_norm:\n  max_norm: 1.0\n\ndistributed:\n  strategy: fsdp2       # fsdp2 | megatron_fsdp | ddp\n  dp_size: auto\n  tp_size: 1\n  cp_size: 1\n\nloss_fn:\n  _target_: torch.nn.CrossEntropyLoss\n\ndataset:\n  _target_: nemo_automodel.datasets.squad.SquadDataset\n  tokenizer_name_or_path: meta-llama\u002FLlama-3.2-1B\n  max_seq_length: 2048\n\nvalidation_dataset:\n  _target_: nemo_automodel.datasets.squad.SquadDataset\n  split: validation\n\npacked_sequence:\n  enabled: false\n\ndataloader:\n  batch_size: 4\n  num_workers: 4\n  pin_memory: true\n\noptimizer:\n  _target_: torch.optim.AdamW\n  lr: 2.0e-5\n  weight_decay: 0.01\n\nlr_scheduler:\n  _target_: nemo_automodel.schedulers.CosineAnnealingWarmup\n  warmup_steps: 50\n  min_lr: 1.0e-6\n```\n\n### The `_target_` Pattern\n\nThe `_target_` key specifies a fully qualified Python callable. All remaining keys in that section are passed as keyword arguments:\n\n```yaml\noptimizer:\n  _target_: torch.optim.AdamW   # callable\n  lr: 2.0e-5                    # kwarg\n  weight_decay: 0.01            # kwarg\n```\n\nThis is equivalent to: `torch.optim.AdamW(lr=2e-5, weight_decay=0.01)`.\n\n### CLI Overrides\n\nAny config value can be overridden from the command line:\n\n```bash\nautomodel finetune llm -c config.yaml \\\n  --optimizer.lr 1e-4 \\\n  --step_scheduler.max_steps 500 \\\n  --distributed.tp_size 2\n```\n\n## Examples\n\nValidation and checkpointing:\n\n```yaml\nstep_scheduler:\n  val_check_interval: 100\n  checkpoint_interval: 500\n\nvalidation_dataset:\n  _target_: nemo_automodel.datasets.squad.SquadDataset\n  split: validation\n\nrestore_from:\n  path: \u002Fcheckpoints\u002Fstep-500\n```\n\n## Domain-Specific Notes\n\n### LLM\n\n- `nemo_automodel\u002Frecipes\u002Fllm\u002Ftrain_ft.py` handles both finetuning and pretraining. The distinction is in the config (dataset, learning rate, etc.).\n- `nemo_automodel\u002Frecipes\u002Fllm\u002Fkd.py` implements knowledge distillation with a teacher and student model.\n- `nemo_automodel\u002Frecipes\u002Fllm\u002Fbenchmark.py` runs throughput and latency benchmarks.\n\n### VLM\n\n- Uses `NeMoAutoModelForImageTextToText` instead of causal LM classes.\n- Config includes a `processor` section instead of a standalone tokenizer.\n- Recipe lives in `nemo_automodel\u002Frecipes\u002Fvlm\u002Ffinetune.py`.\n\n### Diffusion\n\n- Uses `NeMoAutoDiffusionPipeline`.\n- Requires a `parallel_scheme` dict in config to define parallelism.\n- Only supports DDP and FSDP2 strategies (no Megatron-FSDP).\n- Recipe lives in `nemo_automodel\u002Frecipes\u002Fdiffusion\u002Ftrain.py`.\n\n### Retrieval\n\n- Two encoder patterns:\n  - **Bi-encoder** (`nemo_automodel\u002Frecipes\u002Fretrieval\u002Ftrain_bi_encoder.py`): separate query and document encoders, contrastive loss.\n  - **Cross-encoder** (`nemo_automodel\u002Frecipes\u002Fretrieval\u002Ftrain_cross_encoder.py`): joint encoding, classification head.\n- Hard negative mining: `nemo_automodel\u002Frecipes\u002Fretrieval\u002Fmine_hard_negatives.py`.\n\n## Training Loop Details\n\nThe training loop follows this structure per epoch:\n\n```\nfor epoch in range(num_epochs):\n    for batch_idx in range(batches_per_epoch):\n        # --- gradient accumulation inner loop ---\n        for micro_batch in micro_batches:\n            if pipeline_parallel:\n                schedule.step(micro_batch)    # PP schedule\n            else:\n                loss = model(micro_batch)     # direct forward\n                loss.backward()\n\n        # --- optimizer step ---\n        scale_grads_and_clip_grad_norm(model, max_norm)\n        optimizer.step()\n        lr_scheduler.step()\n        optimizer.zero_grad()\n\n        # --- logging ---\n        MetricsSample(step, epoch, loss, grad_norm, lr, mem, tps, mfu)\n\n        # --- validation (at configured intervals) ---\n        if step % val_check_interval == 0:\n            run_validation()\n\n        # --- checkpoint (at configured intervals) ---\n        if step % checkpoint_interval == 0:\n            save_checkpoint()\n```\n\n### StepScheduler\n\nControls all training progression: total epochs, total steps, gradient accumulation steps, validation interval, checkpoint interval, and logging interval.\n\n### Gradient Clipping\n\nApplied via `scale_grads_and_clip_grad_norm()` after the backward pass and before the optimizer step. Controlled by `clip_grad_norm.max_norm` in config.\n\n### Context Parallelism\n\nWhen `cp_size > 1`, batches are split across the context-parallel group using `make_cp_batch_and_ctx()`. This must happen before the forward pass.\n\n### MetricsSample\n\nEach training step produces a `MetricsSample` with fields:\n\n- `step` -- global step count\n- `epoch` -- current epoch\n- `loss` -- training loss\n- `grad_norm` -- gradient norm after clipping\n- `lr` -- current learning rate\n- `mem` -- GPU memory usage\n- `tps` -- tokens per second\n- `mfu` -- model FLOPS utilization\n\n## Validation & Checkpointing\n\n### Validation\n\n- Runs at intervals defined by `step_scheduler.val_check_interval`.\n- Uses the validation dataloader built from `validation_dataset` config.\n- Model is set to eval mode; gradients are disabled.\n\n### Checkpointing\n\n- Default format: consolidated safetensors for easy deployment on HF ecosystem (always prefer this over DCP).\n- Checkpoint interval controlled by `step_scheduler.checkpoint_interval`.\n- Resume training via the `restore_from` config key pointing to a checkpoint directory.\n\n```yaml\nrestore_from:\n  path: \u002Fcheckpoints\u002Fstep-500\n```\n\n## Pitfalls\n\n| Problem | Cause | Fix |\n|---|---|---|\n| Silent config errors | Typo in `_target_` value | The class path must be a valid, importable Python callable. Double-check the module path and class name. |\n| Training crashes at first step | `global_batch_size` not divisible by `local_batch_size * dp_size * grad_accumulation_steps` | Ensure the batch size math is consistent across all dimensions. |\n| New recipe not accessible via CLI | Missing CLI command alias registration | Register the new route in the CLI app so `automodel \u003Ccommand> \u003Cdomain>` resolves correctly. |\n| Shape mismatch at forward pass | Dataset collate function output does not match model input signature | Verify that the collate function returns tensors with the keys and shapes the model expects. |\n| OOM during validation | Validation batch size too large or gradients not disabled | Wrap validation in `torch.no_grad()` and consider a smaller validation batch size. |\n| Checkpoint restore fails | Mismatched model architecture between checkpoint and config | Ensure the model config matches the checkpoint exactly (layer count, hidden dim, vocab size). |\n","# NeMo AutoModel Recipe Development\n\n## Instructions\n\nFor recipe questions, answer with the smallest complete path to action:\n\n1. Name the relevant recipe file or YAML section.\n2. List the builder functions or config keys involved.\n3. Include a minimal YAML or command example when the question asks how to\n   configure something.\n4. End with a local validation command or tiny CPU-compatible test.\n\nFor conceptual recipe questions, answer from this skill without inspecting the\nrepository or loading other AutoModel skills unless the user asks you to edit\nfiles. Keep the response focused on recipe YAML, builders, CLI routing, tests,\nand local validation.\n\nUse these compact answer patterns for common questions:\n\n- New finetuning recipe variant: start from the closest file under\n  `nemo_automodel\u002Frecipes\u002F`, update the model, dataset or dataloader,\n  optimizer, loss, LR scheduler, step scheduler, and checkpoint builders,\n  register a CLI route only if adding a command or domain alias, add example\n  YAML under `examples\u002F`, then add a tiny CPU-compatible unit test and run\n  `automodel finetune llm -c \u003Cconfig.yaml>`.\n- `_target_` fields: describe `_target_` as the fully qualified Python callable,\n  explain that sibling keys become keyword arguments, show optimizer and dataset\n  examples, and mention nested CLI overrides such as `--optimizer.lr`.\n- Validation and checkpointing: name `step_scheduler.val_check_interval`,\n  `step_scheduler.checkpoint_interval`, `validation_dataset`,\n  `restore_from.path`, and consolidated safetensors; include the minimal YAML\n  snippet from this skill.\n\nFor validation and checkpointing, always name:\n\n- `step_scheduler.val_check_interval` for validation cadence.\n- `step_scheduler.checkpoint_interval` for save cadence.\n- `validation_dataset` as the validation dataloader source.\n- `restore_from.path` for resume.\n- Consolidated safetensors as the default checkpoint format for HF ecosystem\n  compatibility.\n\n## Routing Boundary\n\nUse this skill for recipe construction and execution-flow questions: YAML\nstructure, `_target_` callables, builder functions, validation datasets,\ncheckpoint configuration, CLI route registration, and recipe-specific tests.\n\nDo not use this skill for standalone distributed strategy selection, cluster\nlauncher configuration, or model architecture onboarding unless the user is\nasking how those choices appear inside an AutoModel recipe YAML.\n\n## Recipe Architecture\n\n### Execution Flow\n\n```\nCLI (automodel finetune llm -c config.yaml)\n  -> app.py parses command + domain + config\n    -> recipe script (e.g. train_ft.py) main(config_path)\n      -> Recipe class .setup() builds all components\n        -> .run_train_validation_loop() executes training\n```\n\n### Recipe Class\n\nRecipes inherit from `BaseRecipe` and implement two methods:\n\n- `setup()` -- builds model, optimizer, dataloader, loss, LR scheduler, step scheduler, and checkpoint config via builder functions.\n- `run_train_validation_loop()` -- executes the training and validation loop.\n\n### Builder Pattern\n\nAll components are constructed through dedicated builder functions:\n\n- `build_model()` -- instantiates the model from config\n- `build_optimizer()` -- creates optimizer (AdamW, etc.)\n- `build_dataloader()` -- sets up train and validation dataloaders\n- `build_loss_fn()` -- creates the loss function\n- `build_lr_scheduler()` -- creates the learning rate scheduler\n- `build_step_scheduler()` -- creates the step scheduler controlling training progression\n- `build_checkpoint_config()` -- configures checkpointing\n\n### Infrastructure Application Order\n\nComponents are applied in this strict order after building:\n\n1. PEFT (LoRA, etc.)\n2. FP8 quantization\n3. QAT (quantization-aware training)\n4. Checkpoint load \u002F restore\n5. Parameter freezing\n6. Sharding (FSDP2, Megatron-FSDP, DDP)\n7. Device placement\n8. `torch.compile`\n9. Context parallelism hooks\n\n## YAML Config Anatomy\n\nA complete recipe config follows this structure:\n\n```yaml\nstep_scheduler:\n  max_steps: 1000\n  num_epochs: 1\n  grad_accumulation_steps: 4\n  val_check_interval: 100\n  checkpoint_interval: 500\n  log_interval: 10\n\ndist_env:\n  master_addr: localhost\n  master_port: 29500\n\nrng:\n  seed: 42\n\nmodel:\n  _target_: nemo_automodel.models.llm.NemotronHForCausalLM\n  name_or_path: meta-llama\u002FLlama-3.2-1B\n  # additional model kwargs passed to the constructor\n\ncompile:\n  enabled: false\n  backend: inductor\n\nclip_grad_norm:\n  max_norm: 1.0\n\ndistributed:\n  strategy: fsdp2       # fsdp2 | megatron_fsdp | ddp\n  dp_size: auto\n  tp_size: 1\n  cp_size: 1\n\nloss_fn:\n  _target_: torch.nn.CrossEntropyLoss\n\ndataset:\n  _target_: nemo_automodel.datasets.squad.SquadDataset\n  tokenizer_name_or_path: meta-llama\u002FLlama-3.2-1B\n  max_seq_length: 2048\n\nvalidation_dataset:\n  _target_: nemo_automodel.datasets.squad.SquadDataset\n  split: validation\n\npacked_sequence:\n  enabled: false\n\ndataloader:\n  batch_size: 4\n  num_workers: 4\n  pin_memory: true\n\noptimizer:\n  _target_: torch.optim.AdamW\n  lr: 2.0e-5\n  weight_decay: 0.01\n\nlr_scheduler:\n  _target_: nemo_automodel.schedulers.CosineAnnealingWarmup\n  warmup_steps: 50\n  min_lr: 1.0e-6\n```\n\n### The `_target_` Pattern\n\nThe `_target_` key specifies a fully qualified Python callable. All remaining keys in that section are passed as keyword arguments:\n\n```yaml\noptimizer:\n  _target_: torch.optim.AdamW   # callable\n  lr: 2.0e-5                    # kwarg\n  weight_decay: 0.01            # kwarg\n```\n\nThis is equivalent to: `torch.optim.AdamW(lr=2e-5, weight_decay=0.01)`.\n\n### CLI Overrides\n\nAny config value can be overridden from the command line:\n\n```bash\nautomodel finetune llm -c config.yaml \\\n  --optimizer.lr 1e-4 \\\n  --step_scheduler.max_steps 500 \\\n  --distributed.tp_size 2\n```\n\n## Examples\n\nValidation and checkpointing:\n\n```yaml\nstep_scheduler:\n  val_check_interval: 100\n  checkpoint_interval: 500\n\nvalidation_dataset:\n  _target_: nemo_automodel.datasets.squad.SquadDataset\n  split: validation\n\nrestore_from:\n  path: \u002Fcheckpoints\u002Fstep-500\n```\n\n## Domain-Specific Notes\n\n### LLM\n\n- `nemo_automodel\u002Frecipes\u002Fllm\u002Ftrain_ft.py` handles both finetuning and pretraining. The distinction is in the config (dataset, learning rate, etc.).\n- `nemo_automodel\u002Frecipes\u002Fllm\u002Fkd.py` implements knowledge distillation with a teacher and student model.\n- `nemo_automodel\u002Frecipes\u002Fllm\u002Fbenchmark.py` runs throughput and latency benchmarks.\n\n### VLM\n\n- Uses `NeMoAutoModelForImageTextToText` instead of causal LM classes.\n- Config includes a `processor` section instead of a standalone tokenizer.\n- Recipe lives in `nemo_automodel\u002Frecipes\u002Fvlm\u002Ffinetune.py`.\n\n### Diffusion\n\n- Uses `NeMoAutoDiffusionPipeline`.\n- Requires a `parallel_scheme` dict in config to define parallelism.\n- Only supports DDP and FSDP2 strategies (no Megatron-FSDP).\n- Recipe lives in `nemo_automodel\u002Frecipes\u002Fdiffusion\u002Ftrain.py`.\n\n### Retrieval\n\n- Two encoder patterns:\n  - **Bi-encoder** (`nemo_automodel\u002Frecipes\u002Fretrieval\u002Ftrain_bi_encoder.py`): separate query and document encoders, contrastive loss.\n  - **Cross-encoder** (`nemo_automodel\u002Frecipes\u002Fretrieval\u002Ftrain_cross_encoder.py`): joint encoding, classification head.\n- Hard negative mining: `nemo_automodel\u002Frecipes\u002Fretrieval\u002Fmine_hard_negatives.py`.\n\n## Training Loop Details\n\nThe training loop follows this structure per epoch:\n\n```\nfor epoch in range(num_epochs):\n    for batch_idx in range(batches_per_epoch):\n        # --- gradient accumulation inner loop ---\n        for micro_batch in micro_batches:\n            if pipeline_parallel:\n                schedule.step(micro_batch)    # PP schedule\n            else:\n                loss = model(micro_batch)     # direct forward\n                loss.backward()\n\n        # --- optimizer step ---\n        scale_grads_and_clip_grad_norm(model, max_norm)\n        optimizer.step()\n        lr_scheduler.step()\n        optimizer.zero_grad()\n\n        # --- logging ---\n        MetricsSample(step, epoch, loss, grad_norm, lr, mem, tps, mfu)\n\n        # --- validation (at configured intervals) ---\n        if step % val_check_interval == 0:\n            run_validation()\n\n        # --- checkpoint (at configured intervals) ---\n        if step % checkpoint_interval == 0:\n            save_checkpoint()\n```\n\n### StepScheduler\n\nControls all training progression: total epochs, total steps, gradient accumulation steps, validation interval, checkpoint interval, and logging interval.\n\n### Gradient Clipping\n\nApplied via `scale_grads_and_clip_grad_norm()` after the backward pass and before the optimizer step. Controlled by `clip_grad_norm.max_norm` in config.\n\n### Context Parallelism\n\nWhen `cp_size > 1`, batches are split across the context-parallel group using `make_cp_batch_and_ctx()`. This must happen before the forward pass.\n\n### MetricsSample\n\nEach training step produces a `MetricsSample` with fields:\n\n- `step` -- global step count\n- `epoch` -- current epoch\n- `loss` -- training loss\n- `grad_norm` -- gradient norm after clipping\n- `lr` -- current learning rate\n- `mem` -- GPU memory usage\n- `tps` -- tokens per second\n- `mfu` -- model FLOPS utilization\n\n## Validation & Checkpointing\n\n### Validation\n\n- Runs at intervals defined by `step_scheduler.val_check_interval`.\n- Uses the validation dataloader built from `validation_dataset` config.\n- Model is set to eval mode; gradients are disabled.\n\n### Checkpointing\n\n- Default format: consolidated safetensors for easy deployment on HF ecosystem (always prefer this over DCP).\n- Checkpoint interval controlled by `step_scheduler.checkpoint_interval`.\n- Resume training via the `restore_from` config key pointing to a checkpoint directory.\n\n```yaml\nrestore_from:\n  path: \u002Fcheckpoints\u002Fstep-500\n```\n\n## Pitfalls\n\n| Problem | Cause | Fix |\n|---|---|---|\n| Silent config errors | Typo in `_target_` value | The class path must be a valid, importable Python callable. Double-check the module path and class name. |\n| Training crashes at first step | `global_batch_size` not divisible by `local_batch_size * dp_size * grad_accumulation_steps` | Ensure the batch size math is consistent across all dimensions. |\n| New recipe not accessible via CLI | Missing CLI command alias registration | Register the new route in the CLI app so `automodel \u003Ccommand> \u003Cdomain>` resolves correctly. |\n| Shape mismatch at forward pass | Dataset collate function output does not match model input signature | Verify that the collate function returns tensors with the keys and shapes the model expects. |\n| OOM during validation | Validation batch size too large or gradients not disabled | Wrap validation in `torch.no_grad()` and consider a smaller validation batch size. |\n| Checkpoint restore fails | Mismatched model architecture between checkpoint and config | Ensure the model config matches the checkpoint exactly (layer count, hidden dim, vocab size). |\n",{"name":5,"license":15,"metadata":24,"description":7,"when_to_use":29},{"tags":25,"author":28},[26,27],"nemo-automodel","recipe-development","NVIDIA","Creating or modifying training, SFT, or eval recipes, adding new YAML config fields, debugging recipe construction or trainer issues, or understanding the recipe execution flow.","main","99825ad3f3bcc6597ddcc7294a14e6e922f2b67b","skills\u002Fnemo-automodel-recipe-development",0,"https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills","https:\u002F\u002Fgithub.com\u002Fjukeyman","https:\u002F\u002FRJBUSINESSSOLUTIONS.ORG","Credit and Finance Expert | Developer\r\n\r\nI'm Rick Jefferson, a seasoned credit and finance expert with over a decade of experience in financial literacy,","2026-07-13 07:03:29",{"source":8,"skillName":5,"commands":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add jukeyman\u002Fjukeyman-skills --skill nemo-automodel-recipe-development",{"tool":45,"command":46},"bunx","bunx skills add jukeyman\u002Fjukeyman-skills --skill nemo-automodel-recipe-development",{"tool":48,"command":49},"pnpm","pnpm dlx skills add jukeyman\u002Fjukeyman-skills --skill nemo-automodel-recipe-development","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fnemo-automodel-recipe-development\u002FSKILL.md",true,[53,59,63,67],{"path":54,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":57,"sourceHtmlUrl":58},"BENCHMARK.md","REFERENCE","参考文档",4109,"https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fnemo-automodel-recipe-development\u002FBENCHMARK.md",{"path":60,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":61,"sourceHtmlUrl":62},"evals\u002Fevals.json",3529,"https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fnemo-automodel-recipe-development\u002Fevals\u002Fevals.json",{"path":64,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":65,"sourceHtmlUrl":66},"skill-card.md",3739,"https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fnemo-automodel-recipe-development\u002Fskill-card.md",{"path":68,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":71,"sourceHtmlUrl":72},"skill.oms.sig","OTHER","其他",4613,"https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fnemo-automodel-recipe-development\u002Fskill.oms.sig",[74,82,89,96,103,111],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":19},"01M79ANS2SM329NEP5ZCX106RR","zustand-store-creator","Create zustand store creator operations. Auto-activating skill for Frontend Development.\nTriggers on: zustand store creator, zustand store creator\nPart of the Frontend Development skill category. Use when working with zustand store creator functionality. Trigger with phrases like \"zustand store creator\", \"zustand creator\", \"zustand\".","MIT","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fzustand-store-creator\u002FSKILL.md",[],[],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M79AMZKP4YDEZ7EHG1Y7PP74","zero-trust-config-helper","Configure with zero trust config helper operations. Auto-activating skill for Security Advanced.\nTriggers on: zero trust config helper, zero trust config helper\nPart of the Security Advanced skill category. Use when configuring systems or services. Trigger with phrases like \"zero trust config helper\", \"zero helper\", \"zero\".","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fzero-trust-config-helper\u002FSKILL.md",[],[],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":19},"01M79AMJSBCEJQNBTYNAHYN5HM","zapier-integration-helper","Assist with zapier integration helper operations. Auto-activating skill for Business Automation.\nTriggers on: zapier integration helper, zapier integration helper\nPart of the Business Automation skill category. Use when working with APIs or building integrations. Trigger with phrases like \"zapier integration helper\", \"zapier helper\", \"zapier\".","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fzapier-integration-helper\u002FSKILL.md",[],[],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":19},"01M79AHJZGVMARS13VFQYRR1MP","yaml-config-validator","Validate yaml config validator operations. Auto-activating skill for DevOps Basics.\nTriggers on: yaml config validator, yaml config validator\nPart of the DevOps Basics skill category. Use when configuring systems or services. Trigger with phrases like \"yaml config validator\", \"yaml validator\", \"yaml\".","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fyaml-config-validator\u002FSKILL.md",[],[],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":107,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":19},"01M79AHGDZRN0020RDQSQDPAM8","xvary-stock-research","Thesis-driven equity analysis from public SEC EDGAR and market data; \u002Fanalyze, \u002Fscore, \u002Fcompare workflows with bundled Python tools (Claude Code, Cursor, Codex).","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fxvary-stock-research\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fxvary-stock-research\u002FSKILL.md",[],[],{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":19},"01M79AHCYJ9FV9RMHQ24HDRN33","xss-vulnerability-scanner","Scan xss vulnerability scanner operations. Auto-activating skill for Security Fundamentals.\nTriggers on: xss vulnerability scanner, xss vulnerability scanner\nPart of the Security Fundamentals skill category. Use when working with xss vulnerability scanner functionality. Trigger with phrases like \"xss vulnerability scanner\", \"xss scanner\", \"xss\".","https:\u002F\u002Fgithub.com\u002Fjukeyman\u002Fjukeyman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fxss-vulnerability-scanner\u002FSKILL.md",[],[]]