[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78ASG88D6TP9Y4EQJ2MFD82":3,"skill-same:01M78ASG88D6TP9Y4EQJ2MFD82":126},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":14,"repositoryHtmlUrl":36,"repositoryHomepageUrl":6,"ownerHtmlUrl":37,"ownerBlogUrl":38,"ownerDescription":39,"updatedAt":40,"install":41,"resources":54},"01M78ASG88D6TP9Y4EQJ2MFD82","tao-train-nvdinov2",null,"NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels and produces general-purpose visual features. Use when training, exporting, or running inference for a TAO NVDINOv2 backbone. Trigger phrases include \"train NVDINOv2\", \"self-supervised ViT pretraining\", \"DINOv2 backbone\", \"visual representation learning\".","Solizardking\u002Ftrain2earn","Solizardking","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F108096810?v=4","01M786KDB10AYV042N0F9BD5B5","Clawd","01M786KDC5C3NPE4W44C5641EZ",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: tao-train-nvdinov2\ndescription: NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation\n  (teacher-student) without labels and produces general-purpose visual features. Use when training, exporting, or\n  running inference for a TAO NVDINOv2 backbone. Trigger phrases include \"train NVDINOv2\", \"self-supervised ViT pretraining\",\n  \"DINOv2 backbone\", \"visual representation learning\".\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit.\nmetadata:\n  version: \"0.1.0\"\n  author: NVIDIA Corporation\nallowed-tools: Read Bash\ntags:\n- self\n- supervised\n- learning\n---\n\n# NVDINOv2\n\nNVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels. Produces general-purpose visual features.\n\nSet train.pretrained_model_path for pretrained ViT weights.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`), read `references\u002Ftao-deploy-nvdinov2.md` first. Deploy spec templates live in this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml` prefix.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Training Requirements\n\n- **Dataset type:** image_classification\n- **Formats:** ssl\n- **Monitoring metric:** train_loss\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| inference | dataset.test_dataset.images_dir | inference_dataset | images_test.tar.gz | No |\n| train | dataset.train_dataset.images_dir | train_datasets | images_train.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for train and inference** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nS3_TRAIN = \"s3:\u002F\u002Fbucket\u002Fdata\u002Ftrain\"\nS3_EVAL = \"s3:\u002F\u002Fbucket\u002Fdata\u002Feval\"\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_gpus\": 1,\n    \"train.num_epochs\": 10,\n    \"train.checkpoint_interval\": 10,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**local AutoML validation \u002F smoke run:**\nUse this shape when the goal is to confirm Bayesian launch, metric selection,\nbest-model choice, and checkpoint persistence on local Docker. It keeps the\nrun representative while avoiding the much slower ViT-Large default.\n\n```python\n{\n    \"wandb.enable\": False,\n    \"model.backbone.teacher_type\": \"vit_s\",\n    \"model.backbone.student_type\": \"vit_s\",\n    \"model.backbone.img_size\": 224,\n    \"dataset.batch_size\": 8,\n    \"dataset.workers\": 2,\n    \"train.num_epochs\": 1,\n    \"train.checkpoint_interval\": 1,\n    \"train.num_prototypes\": 1024,\n    \"train.precision\": \"32-true\",\n    \"train.use_custom_attention\": False,\n    \"train.num_gpus\": 1,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**export (mandatory checkpoint handoff):**\n```python\n{\n    \"export.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"export.onnx_file\": \"\u002Fpath\u002Fto\u002Fresults\u002Fnvdinov2.onnx\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"inference.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"model.backbone.teacher_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.student_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.img_size\": \"\u003Csame value used for train>\",\n    \"train.use_custom_attention\": \"\u003Csame value used for train>\",\n    \"dataset.test_dataset.images_dir\": f\"{S3_EVAL}\u002Fimages_test.tar.gz\",\n}\n```\n## Eval Dataset\n\nOptional. SSL training does not use labels. Evaluation is downstream task-specific.\n\n## Important Parameters\n\n- **model.backbone.teacher_type**: Teacher ViT variant. Default vit_l (ViT-Large).\n- **model.backbone.student_type**: Student ViT variant. Default vit_l. Typically matches teacher.\n- **model.backbone.img_size**: Input image size. Default 518. Higher resolution produces better features but costs more memory.\n- **model.backbone.patch_size**: ViT patch size. Default 14.\n- **dataset.batch_size**: Per-GPU batch size. Default 4. SSL training is memory-intensive due to dual (teacher+student) forward passes.\n- **train.layerwise_decay**: Layer-wise learning rate decay. Important for ViT fine-tuning.\n- **train.clip_grad_norm**: Gradient clipping. Important for stable SSL training.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n| `train.num_nodes` | Number of nodes | 1 |\n\n- Strategy: `auto` (Lightning picks best strategy automatically)\n- `sync_batchnorm` is always enabled — critical for SSL training with teacher-student framework\n- Multi-GPU strongly recommended (4-8 GPUs) for meaningful SSL training\n\n**Multi-node env vars** (set by orchestrator): `WORLD_SIZE`, `NODE_RANK`, `MASTER_ADDR`, `MASTER_PORT`, `NUM_GPU_PER_NODE`.\n\n## Hardware\n\nMinimum 4 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. SSL with ViT-Large teacher+student is very memory-intensive. Requires A100 40GB+ GPUs. Multi-GPU strongly recommended.\n\n## Error Patterns\n\n**CUDA out of memory**: ViT-Large teacher+student with img_size=518 requires 40GB+ GPU memory. Reduce batch_size, img_size, or use smaller ViT variant.\n\n**Inference checkpoint has unexpected Lightning keys**: For downstream\n`inference`, pass the selected AutoML run's `student_epoch_*.pth` checkpoint,\nnot `nvdinov2_model_latest.pth`. The latest file is a training checkpoint and\nthe inference loader reports unexpected keys such as `state_dict`, optimizer\nstate, and scheduler state.\n\n**Export checkpoint has unexpected Lightning keys**: Export also consumes the\nselected `student_epoch_*.pth` checkpoint. Use the full `model_epoch_*.pth`\ncheckpoint only for resume\u002Fretrain via `train.resume_training_checkpoint_path`.\n\n**TensorRT engine passed to PyT inference**: The packaged PyT `nvdinov2 inference`\nimplementation only loads `.pth` or `.tlt` model paths. TAO Deploy\n`gen_trt_engine` builds a TensorRT engine for downstream consumers, but the PyT\ninference action does not run on that engine.\n\n**Separate distill action not available**: The current TAO PyT CLI exposes\n`export`, `inference`, `train`, and `default_specs` for NvDINOv2. Do not launch\nor advertise a standalone `nvdinov2 distill` action.\n\n**AutoML metric not found**: TAO's status KPI reports the final training scalar\nas `train_loss`. Use `train_loss` with minimize direction for AutoML selection.\nSome Lightning progress lines also render the same scalar as\n`train_loss_epoch`; treat that as a fallback alias only, not the primary\nmonitoring metric.\n\n**Slow convergence**: SSL needs many epochs. Default 10 is for quick testing; production runs typically use 100+ epochs.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nModel-specific handoff mappings:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | selected full `model_epoch_*.pth` training checkpoint from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n\n## Deployment\n\n- [tao-deploy-nvdinov2](references\u002Ftao-deploy-nvdinov2.md)\n","# NVDINOv2\n\nNVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels. Produces general-purpose visual features.\n\nSet train.pretrained_model_path for pretrained ViT weights.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`), read `references\u002Ftao-deploy-nvdinov2.md` first. Deploy spec templates live in this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml` prefix.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Training Requirements\n\n- **Dataset type:** image_classification\n- **Formats:** ssl\n- **Monitoring metric:** train_loss\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| inference | dataset.test_dataset.images_dir | inference_dataset | images_test.tar.gz | No |\n| train | dataset.train_dataset.images_dir | train_datasets | images_train.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for train and inference** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nS3_TRAIN = \"s3:\u002F\u002Fbucket\u002Fdata\u002Ftrain\"\nS3_EVAL = \"s3:\u002F\u002Fbucket\u002Fdata\u002Feval\"\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_gpus\": 1,\n    \"train.num_epochs\": 10,\n    \"train.checkpoint_interval\": 10,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**local AutoML validation \u002F smoke run:**\nUse this shape when the goal is to confirm Bayesian launch, metric selection,\nbest-model choice, and checkpoint persistence on local Docker. It keeps the\nrun representative while avoiding the much slower ViT-Large default.\n\n```python\n{\n    \"wandb.enable\": False,\n    \"model.backbone.teacher_type\": \"vit_s\",\n    \"model.backbone.student_type\": \"vit_s\",\n    \"model.backbone.img_size\": 224,\n    \"dataset.batch_size\": 8,\n    \"dataset.workers\": 2,\n    \"train.num_epochs\": 1,\n    \"train.checkpoint_interval\": 1,\n    \"train.num_prototypes\": 1024,\n    \"train.precision\": \"32-true\",\n    \"train.use_custom_attention\": False,\n    \"train.num_gpus\": 1,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**export (mandatory checkpoint handoff):**\n```python\n{\n    \"export.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"export.onnx_file\": \"\u002Fpath\u002Fto\u002Fresults\u002Fnvdinov2.onnx\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"inference.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"model.backbone.teacher_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.student_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.img_size\": \"\u003Csame value used for train>\",\n    \"train.use_custom_attention\": \"\u003Csame value used for train>\",\n    \"dataset.test_dataset.images_dir\": f\"{S3_EVAL}\u002Fimages_test.tar.gz\",\n}\n```\n## Eval Dataset\n\nOptional. SSL training does not use labels. Evaluation is downstream task-specific.\n\n## Important Parameters\n\n- **model.backbone.teacher_type**: Teacher ViT variant. Default vit_l (ViT-Large).\n- **model.backbone.student_type**: Student ViT variant. Default vit_l. Typically matches teacher.\n- **model.backbone.img_size**: Input image size. Default 518. Higher resolution produces better features but costs more memory.\n- **model.backbone.patch_size**: ViT patch size. Default 14.\n- **dataset.batch_size**: Per-GPU batch size. Default 4. SSL training is memory-intensive due to dual (teacher+student) forward passes.\n- **train.layerwise_decay**: Layer-wise learning rate decay. Important for ViT fine-tuning.\n- **train.clip_grad_norm**: Gradient clipping. Important for stable SSL training.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n| `train.num_nodes` | Number of nodes | 1 |\n\n- Strategy: `auto` (Lightning picks best strategy automatically)\n- `sync_batchnorm` is always enabled — critical for SSL training with teacher-student framework\n- Multi-GPU strongly recommended (4-8 GPUs) for meaningful SSL training\n\n**Multi-node env vars** (set by orchestrator): `WORLD_SIZE`, `NODE_RANK`, `MASTER_ADDR`, `MASTER_PORT`, `NUM_GPU_PER_NODE`.\n\n## Hardware\n\nMinimum 4 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. SSL with ViT-Large teacher+student is very memory-intensive. Requires A100 40GB+ GPUs. Multi-GPU strongly recommended.\n\n## Error Patterns\n\n**CUDA out of memory**: ViT-Large teacher+student with img_size=518 requires 40GB+ GPU memory. Reduce batch_size, img_size, or use smaller ViT variant.\n\n**Inference checkpoint has unexpected Lightning keys**: For downstream\n`inference`, pass the selected AutoML run's `student_epoch_*.pth` checkpoint,\nnot `nvdinov2_model_latest.pth`. The latest file is a training checkpoint and\nthe inference loader reports unexpected keys such as `state_dict`, optimizer\nstate, and scheduler state.\n\n**Export checkpoint has unexpected Lightning keys**: Export also consumes the\nselected `student_epoch_*.pth` checkpoint. Use the full `model_epoch_*.pth`\ncheckpoint only for resume\u002Fretrain via `train.resume_training_checkpoint_path`.\n\n**TensorRT engine passed to PyT inference**: The packaged PyT `nvdinov2 inference`\nimplementation only loads `.pth` or `.tlt` model paths. TAO Deploy\n`gen_trt_engine` builds a TensorRT engine for downstream consumers, but the PyT\ninference action does not run on that engine.\n\n**Separate distill action not available**: The current TAO PyT CLI exposes\n`export`, `inference`, `train`, and `default_specs` for NvDINOv2. Do not launch\nor advertise a standalone `nvdinov2 distill` action.\n\n**AutoML metric not found**: TAO's status KPI reports the final training scalar\nas `train_loss`. Use `train_loss` with minimize direction for AutoML selection.\nSome Lightning progress lines also render the same scalar as\n`train_loss_epoch`; treat that as a fallback alias only, not the primary\nmonitoring metric.\n\n**Slow convergence**: SSL needs many epochs. Default 10 is for quick testing; production runs typically use 100+ epochs.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nModel-specific handoff mappings:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | selected full `model_epoch_*.pth` training checkpoint from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n\n## Deployment\n\n- [tao-deploy-nvdinov2](references\u002Ftao-deploy-nvdinov2.md)\n",{"name":5,"tags":24,"license":15,"metadata":28,"description":7,"allowed-tools":31,"compatibility":32},[25,26,27],"self","supervised","learning",{"author":29,"version":30},"NVIDIA Corporation","0.1.0","Read Bash","Requires docker + nvidia-container-toolkit.","main","86cc27ffd16af6cb85948f1155578e6f3f61cc4c","skillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn","https:\u002F\u002Fgithub.com\u002FSolizardking","https:\u002F\u002Fmusebook.trade","Got a mile high and never came down","2026-10-08 11:52:53",{"source":8,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add Solizardking\u002Ftrain2earn --skill tao-train-nvdinov2",{"tool":47,"command":48},"bunx","bunx skills add Solizardking\u002Ftrain2earn --skill tao-train-nvdinov2",{"tool":50,"command":51},"pnpm","pnpm dlx skills add Solizardking\u002Ftrain2earn --skill tao-train-nvdinov2","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002FSKILL.md",true,[55,61,65,69,73,77,81,85,88,92,96,100,104,108,112,116,120],{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"BENCHMARK.md","REFERENCE","参考文档",4064,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002FBENCHMARK.md",{"path":62,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":63,"sourceHtmlUrl":64},"evals\u002Fevals.json",796,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fevals\u002Fevals.json",{"path":66,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":67,"sourceHtmlUrl":68},"references\u002Fskill_info.yaml",2734,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fskill_info.yaml",{"path":70,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fspec_template_deploy_gen_trt_engine.yaml",514,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_deploy_gen_trt_engine.yaml",{"path":74,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":75,"sourceHtmlUrl":76},"references\u002Fspec_template_distill.yaml",2113,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_distill.yaml",{"path":78,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fspec_template_export.yaml",2314,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_export.yaml",{"path":82,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fspec_template_inference.yaml",2264,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_inference.yaml",{"path":86,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":75,"sourceHtmlUrl":87},"references\u002Fspec_template_train.yaml","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_train.yaml",{"path":89,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ftao-deploy-nvdinov2.md",3852,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Ftao-deploy-nvdinov2.md",{"path":93,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Ftao-deploy-nvdinov2.skill_info.yaml",1605,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Ftao-deploy-nvdinov2.skill_info.yaml",{"path":97,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":98,"sourceHtmlUrl":99},"schemas\u002Fdistill.schema.json",40774,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fschemas\u002Fdistill.schema.json",{"path":101,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":102,"sourceHtmlUrl":103},"schemas\u002Fexport.schema.json",43536,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fschemas\u002Fexport.schema.json",{"path":105,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":106,"sourceHtmlUrl":107},"schemas\u002Finference.schema.json",43698,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fschemas\u002Finference.schema.json",{"path":109,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":110,"sourceHtmlUrl":111},"schemas\u002Fmanifest.json",12582,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fschemas\u002Fmanifest.json",{"path":113,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":114,"sourceHtmlUrl":115},"schemas\u002Ftrain.schema.json",40770,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fschemas\u002Ftrain.schema.json",{"path":117,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":118,"sourceHtmlUrl":119},"skill-card.md",3819,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fskill-card.md",{"path":121,"resourceType":122,"resourceTypeDesc":123,"sizeBytes":124,"sourceHtmlUrl":125},"skill.oms.sig","OTHER","其他",7813,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fskill.oms.sig",[127,135,143,151,158,166],{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":19},"01M78B94RSCAKMH1QS79VE6DG9","testing","For testing with Light Protocol programs and clients on localnet, devnet, and mainnet validation.","MIT","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Ftesting\u002FSKILL.md",[],[],{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":139,"sourceHtmlUrl":140,"categories":141,"keywords":142,"publishedAt":19},"01M78B81BKY8RX12HETPG8YKWK","qedgen","Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions \"qedgen\", \"lean proof\", \"formal proof\", \"verify my code\", \"prove correctness\", \"formal verification\", or wants mathematical guarantees about their implementation.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-formal-verification\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-formal-verification\u002FSKILL.md",[],[],{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":147,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":19},"01M78B7Q7C5K54E01DN83A60KG","solana-dev","Use when user asks to \"build a Solana dapp\", \"write an Anchor program\", \"create a token\", \"debug Solana errors\", \"set up wallet connection\", \"test my Solana program\", \"deploy to devnet\", or \"explain Solana concepts\" (rent, accounts, PDAs, CPIs, etc.). Also use for quick on-chain lookups via public RPC + curl — \"what's the balance of \u003Cwallet>\", \"look up transaction \u003Csig>\", \"token balance for \u003Caccount>\", \"check this address on mainnet\u002Fdevnet\". End-to-end Solana development playbook covering wallet connection, Anchor\u002FPinocchio programs, Codama client generation, LiteSVM\u002FMollusk\u002FSurfpool testing, security checklists, and JSON-RPC curl lookups against public clusters. Integrates with the Solana MCP server for live documentation search. Prefers framework-kit (@solana\u002Fclient + @solana\u002Freact-hooks) for UI, wallet-standard-first connection (incl. ConnectorKit), @solana\u002Fkit for client\u002FRPC code, and @solana\u002Fweb3-compat for legacy boundaries.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-dev\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-dev\u002FSKILL.md",[],[],{"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":131,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":19},"01M78B7HXD6N6GWKSVRQ8F6Q31","solana-common-errors","Diagnose and fix common Solana development errors — GLIBC mismatches, Anchor version conflicts, cargo build-sbf \u002F platform-tools failures, LiteSVM issues, edition2024 crate pins, RPC airdrops, and Anchor 0.29→0.32 migrations. Use when builds fail, Anchor CLI won't install, or CI breaks on Debian\u002FUbuntu.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fsolana-common-errors\u002FSKILL.md",[],[],{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":162,"sourceHtmlUrl":163,"categories":164,"keywords":165,"publishedAt":19},"01M78B6T7YSYWQ0N4DP40QDXEZ","skill-creator","Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fskill-creator\u002Flicense.txt","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fskill-creator\u002FSKILL.md",[],[],{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":19},"01M78B1VFT9X5AND7AE1HZPZGF","phantom-wallet-mcp","Execute wallet operations through the Phantom MCP server. Use when the user wants to interact with their Phantom wallet directly — get addresses, transfer SOL or SPL tokens, buy\u002Fswap tokens, sign transactions, and sign messages across Solana, Ethereum, Bitcoin, and Sui. Requires the @phantom\u002Fmcp-server to be configured as an MCP server.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Ftrain2earn\u002Fblob\u002Fmain\u002Fskillhub\u002Fskills\u002Fphantom-wallet-mcp\u002FSKILL.md",[],[]]