[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PKQJ9G7CDWN4A79NXB3QF3":3,"skill-same:01M6PKQJ9G7CDWN4A79NXB3QF3":166},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6PKQJ9G7CDWN4A79NXB3QF3","tao-train-segformer",null,"SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction, efficient for real-time segmentation tasks. Use when training, evaluating, exporting, quantizing, or running inference for a TAO SegFormer model. Trigger phrases include \"train SegFormer\", \"semantic segmentation\", \"lightweight transformer segmenter\", \"real-time semantic segmentation\".","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tao train segformer","tao","train","use","for","Transformer","trigger","feature","time","container","docker","model","Bash","segmentation","real-time","NVIDIA","toolkit","tasks","training","architecture","2026-11-10 10:12:30",false,"---\nname: tao-train-segformer\ndescription: SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature\n  extraction, efficient for real-time segmentation tasks. Use when training, evaluating, exporting, quantizing, or running\n  inference for a TAO SegFormer model. Trigger phrases include \"train SegFormer\", \"semantic segmentation\", \"lightweight\n  transformer segmenter\", \"real-time semantic segmentation\".\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- segmentation\n---\n\n# SegFormer\n\nSegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction. Efficient for real-time segmentation tasks.\n\nSet model.backbone.pretrained_backbone_path for backbone weights.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references\u002Ftao-deploy-segformer.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. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only; otherwise default to `auto`. When `automl_policy: auto`, `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 `evaluate`, `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:** segmentation\n- **Formats:** unet\n- **Monitoring metric:** val_miou\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| evaluate | dataset.segment.root_dir | eval_dataset |  | No |\n| export | dataset.segment.root_dir | train_datasets |  | No |\n| inference | dataset.segment.root_dir | eval_dataset |  | No |\n| quantize | dataset.segment.root_dir | train_datasets |  | No |\n| quantize | dataset.segment.quant_calibration_dataset.images_dir | train_datasets |  | No |\n| train | dataset.segment.root_dir | train_datasets |  | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for every action** — 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    \"train.validation_interval\": 10,\n    \"dataset.segment.batch_size\": 4,\n    \"dataset.segment.root_dir\": f\"{S3_TRAIN}\",\n}\n```\n\n**evaluate (mandatory data sources):**\n```python\n{\n    \"evaluate.batch_size\": 4,\n    \"dataset.segment.root_dir\": f\"{S3_EVAL}\",\n}\n```\n\n**gen_trt_engine:**\n```python\n{\n    \"gen_trt_engine.tensorrt.data_type\": \"fp16\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"dataset.segment.batch_size\": 1,\n    \"dataset.segment.root_dir\": f\"{S3_EVAL}\",\n}\n```\n\n**export (mandatory data sources):**\n```python\n{\n    \"dataset.segment.root_dir\": f\"{S3_TRAIN}\",\n}\n```\n\n**quantize (mandatory data sources):**\n```python\n{\n    \"dataset.segment.root_dir\": f\"{S3_TRAIN}\",\n    \"dataset.segment.quant_calibration_dataset.images_dir\": f\"{S3_TRAIN}\",\n}\n```\n## Eval Dataset\n\nOptional. Validation data is typically part of the root_dir structure.\n\n## Important Parameters\n\n- **dataset.segment.num_classes**: Number of segmentation classes. Default 2 (binary). Must match the number of classes in your mask annotations.\n- **model.backbone.type**: Default fan_small_12_p4_hybrid. Supported includes FAN variants, SegFormer MIT variants, and others.\n- **dataset.segment.root_dir**: Root directory of the segmentation dataset.\n- **dataset.segment.img_size**: Input image size. Default 256. Increase for finer segmentation at the cost of memory.\n- **train.optim.lr**: Learning rate. Default 6e-5.\n- **model.freeze_backbone**: Whether to freeze the backbone during training. Useful for fine-tuning with limited data.\n- **dataset.segment.batch_size**: Per-GPU batch size. Default 8.\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| `train.sync_batchnorm` | Sync BN across GPUs | configurable |\n| `train.use_distributed_sampler` | Use distributed sampler | configurable |\n\n- Multi-GPU strategy: `ddp_find_unused_parameters_true`\n- No fsdp support\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 1 GPU(s), recommended 2 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. SegFormer is relatively lightweight. Default img_size=256 is memory-friendly. Increase img_size for higher resolution at the cost of memory and speed.\n\n## Error Patterns\n\n**CUDA out of memory**: Reduce batch_size or img_size. SegFormer memory scales quadratically with image size.\n\n**num_classes mismatch**: Ensure dataset.segment.num_classes matches the actual number of classes in your mask annotations.\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\nInference mappings from TAO Core `segformer.config.json`:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| evaluate | `encryption_key` | `key` | encryption key |\n| evaluate | `evaluate.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `evaluate.trt_engine` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `results_dir` | `output_dir` | current job results directory |\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | model file inferred 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| gen_trt_engine | `encryption_key` | `key` | encryption key |\n| gen_trt_engine | `gen_trt_engine.onnx_file` | `parent_model` | model file inferred from the parent job results folder |\n| gen_trt_engine | `gen_trt_engine.trt_engine` | `create_engine_file` | output TensorRT engine path |\n| gen_trt_engine | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `inference.trt_engine` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| quantize | `encryption_key` | `key` | encryption key |\n| quantize | `quantize.model_path` | `parent_model` | model file inferred from the parent job results folder |\n| quantize | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `model.backbone.pretrained_backbone_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\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` | model file inferred 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-segformer](references\u002Ftao-deploy-segformer.md) — SegFormer deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.\n","# SegFormer\n\nSegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction. Efficient for real-time segmentation tasks.\n\nSet model.backbone.pretrained_backbone_path for backbone weights.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references\u002Ftao-deploy-segformer.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. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only; otherwise default to `auto`. When `automl_policy: auto`, `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 `evaluate`, `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:** segmentation\n- **Formats:** unet\n- **Monitoring metric:** val_miou\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| evaluate | dataset.segment.root_dir | eval_dataset |  | No |\n| export | dataset.segment.root_dir | train_datasets |  | No |\n| inference | dataset.segment.root_dir | eval_dataset |  | No |\n| quantize | dataset.segment.root_dir | train_datasets |  | No |\n| quantize | dataset.segment.quant_calibration_dataset.images_dir | train_datasets |  | No |\n| train | dataset.segment.root_dir | train_datasets |  | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for every action** — 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    \"train.validation_interval\": 10,\n    \"dataset.segment.batch_size\": 4,\n    \"dataset.segment.root_dir\": f\"{S3_TRAIN}\",\n}\n```\n\n**evaluate (mandatory data sources):**\n```python\n{\n    \"evaluate.batch_size\": 4,\n    \"dataset.segment.root_dir\": f\"{S3_EVAL}\",\n}\n```\n\n**gen_trt_engine:**\n```python\n{\n    \"gen_trt_engine.tensorrt.data_type\": \"fp16\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"dataset.segment.batch_size\": 1,\n    \"dataset.segment.root_dir\": f\"{S3_EVAL}\",\n}\n```\n\n**export (mandatory data sources):**\n```python\n{\n    \"dataset.segment.root_dir\": f\"{S3_TRAIN}\",\n}\n```\n\n**quantize (mandatory data sources):**\n```python\n{\n    \"dataset.segment.root_dir\": f\"{S3_TRAIN}\",\n    \"dataset.segment.quant_calibration_dataset.images_dir\": f\"{S3_TRAIN}\",\n}\n```\n## Eval Dataset\n\nOptional. Validation data is typically part of the root_dir structure.\n\n## Important Parameters\n\n- **dataset.segment.num_classes**: Number of segmentation classes. Default 2 (binary). Must match the number of classes in your mask annotations.\n- **model.backbone.type**: Default fan_small_12_p4_hybrid. Supported includes FAN variants, SegFormer MIT variants, and others.\n- **dataset.segment.root_dir**: Root directory of the segmentation dataset.\n- **dataset.segment.img_size**: Input image size. Default 256. Increase for finer segmentation at the cost of memory.\n- **train.optim.lr**: Learning rate. Default 6e-5.\n- **model.freeze_backbone**: Whether to freeze the backbone during training. Useful for fine-tuning with limited data.\n- **dataset.segment.batch_size**: Per-GPU batch size. Default 8.\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| `train.sync_batchnorm` | Sync BN across GPUs | configurable |\n| `train.use_distributed_sampler` | Use distributed sampler | configurable |\n\n- Multi-GPU strategy: `ddp_find_unused_parameters_true`\n- No fsdp support\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 1 GPU(s), recommended 2 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. SegFormer is relatively lightweight. Default img_size=256 is memory-friendly. Increase img_size for higher resolution at the cost of memory and speed.\n\n## Error Patterns\n\n**CUDA out of memory**: Reduce batch_size or img_size. SegFormer memory scales quadratically with image size.\n\n**num_classes mismatch**: Ensure dataset.segment.num_classes matches the actual number of classes in your mask annotations.\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\nInference mappings from TAO Core `segformer.config.json`:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| evaluate | `encryption_key` | `key` | encryption key |\n| evaluate | `evaluate.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `evaluate.trt_engine` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `results_dir` | `output_dir` | current job results directory |\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | model file inferred 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| gen_trt_engine | `encryption_key` | `key` | encryption key |\n| gen_trt_engine | `gen_trt_engine.onnx_file` | `parent_model` | model file inferred from the parent job results folder |\n| gen_trt_engine | `gen_trt_engine.trt_engine` | `create_engine_file` | output TensorRT engine path |\n| gen_trt_engine | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `inference.trt_engine` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| quantize | `encryption_key` | `key` | encryption key |\n| quantize | `quantize.model_path` | `parent_model` | model file inferred from the parent job results folder |\n| quantize | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `model.backbone.pretrained_backbone_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\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` | model file inferred 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-segformer](references\u002Ftao-deploy-segformer.md) — SegFormer deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.\n",{"name":5,"tags":44,"license":15,"metadata":45,"description":7,"allowed-tools":48,"compatibility":49},[32],{"author":46,"version":47},"NVIDIA Corporation","0.1.0","Read Bash","Requires docker + nvidia-container-toolkit.","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".agent\u002Fskills\u002Ftao-train-segformer","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 00:42:44",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-train-segformer",{"tool":63,"command":64},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-train-segformer",{"tool":66,"command":67},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-train-segformer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002FSKILL.md",true,[71,77,81,85,89,93,97,101,105,109,113,117,120,124,128,132,136,140,144,148,152,156,160],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"BENCHMARK.md","REFERENCE","参考文档",4255,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002FBENCHMARK.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"evals\u002Fevals.json",755,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fevals\u002Fevals.json",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fskill_info.yaml",1686,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fskill_info.yaml",{"path":86,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fspec_template_deploy_evaluate.yaml",762,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_deploy_evaluate.yaml",{"path":90,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fspec_template_deploy_gen_trt_engine.yaml",1402,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_deploy_gen_trt_engine.yaml",{"path":94,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fspec_template_deploy_inference.yaml",763,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_deploy_inference.yaml",{"path":98,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fspec_template_evaluate.yaml",2832,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_evaluate.yaml",{"path":102,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fspec_template_export.yaml",2927,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_export.yaml",{"path":106,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fspec_template_gen_trt_engine.yaml",3077,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_gen_trt_engine.yaml",{"path":110,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fspec_template_inference.yaml",2833,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_inference.yaml",{"path":114,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Fspec_template_quantize.yaml",2682,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_quantize.yaml",{"path":118,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":115,"sourceHtmlUrl":119},"references\u002Fspec_template_train.yaml","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Fspec_template_train.yaml",{"path":121,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":122,"sourceHtmlUrl":123},"references\u002Ftao-deploy-segformer.md",5116,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Ftao-deploy-segformer.md",{"path":125,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":126,"sourceHtmlUrl":127},"references\u002Ftao-deploy-segformer.skill_info.yaml",2266,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Freferences\u002Ftao-deploy-segformer.skill_info.yaml",{"path":129,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":130,"sourceHtmlUrl":131},"schemas\u002Fevaluate.schema.json",49327,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Fevaluate.schema.json",{"path":133,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":134,"sourceHtmlUrl":135},"schemas\u002Fexport.schema.json",50335,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Fexport.schema.json",{"path":137,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":138,"sourceHtmlUrl":139},"schemas\u002Fgen_trt_engine.schema.json",54594,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Fgen_trt_engine.schema.json",{"path":141,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":142,"sourceHtmlUrl":143},"schemas\u002Finference.schema.json",49329,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Finference.schema.json",{"path":145,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":146,"sourceHtmlUrl":147},"schemas\u002Fmanifest.json",21746,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Fmanifest.json",{"path":149,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":150,"sourceHtmlUrl":151},"schemas\u002Fquantize.schema.json",46528,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Fquantize.schema.json",{"path":153,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":154,"sourceHtmlUrl":155},"schemas\u002Ftrain.schema.json",46522,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fschemas\u002Ftrain.schema.json",{"path":157,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":158,"sourceHtmlUrl":159},"skill-card.md",3777,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fskill-card.md",{"path":161,"resourceType":162,"resourceTypeDesc":163,"sizeBytes":164,"sourceHtmlUrl":165},"skill.oms.sig","OTHER","其他",9357,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Ftao-train-segformer\u002Fskill.oms.sig",[167,193,217,243,261,295],{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":182,"publishedAt":39},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[174,179],{"categoryNo":175,"categoryName":176,"taxonomyType":177,"taxonomyTypeDesc":178},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":180,"categoryName":181,"taxonomyType":177,"taxonomyTypeDesc":178},"01JBSEEDCAT000000000000023","Skill与智能体开发",[183,184,185,186,187,22,188,189,190,191,192],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":201,"publishedAt":39},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[199,200],{"categoryNo":175,"categoryName":176,"taxonomyType":177,"taxonomyTypeDesc":178},{"categoryNo":180,"categoryName":181,"taxonomyType":177,"taxonomyTypeDesc":178},[202,203,185,204,205,206,207,208,22,209,210,211,212,213,188,189,214,215,23,216],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":6,"sourceHtmlUrl":221,"categories":222,"keywords":227,"publishedAt":39},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[223,226],{"categoryNo":224,"categoryName":225,"taxonomyType":177,"taxonomyTypeDesc":178},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":180,"categoryName":181,"taxonomyType":177,"taxonomyTypeDesc":178},[228,229,230,231,232,233,234,235,22,236,237,238,239,240,241,23,242],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"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":171,"licenseFileUrl":6,"sourceHtmlUrl":247,"categories":248,"keywords":251,"publishedAt":39},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[249,250],{"categoryNo":175,"categoryName":176,"taxonomyType":177,"taxonomyTypeDesc":178},{"categoryNo":180,"categoryName":181,"taxonomyType":177,"taxonomyTypeDesc":178},[252,185,253,204,254,255,256,257,187,22,258,212,188,189,259,260],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":6,"sourceHtmlUrl":265,"categories":266,"keywords":276,"publishedAt":39},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[267,270,271],{"categoryNo":268,"categoryName":269,"taxonomyType":177,"taxonomyTypeDesc":178},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":224,"categoryName":225,"taxonomyType":177,"taxonomyTypeDesc":178},{"categoryNo":272,"categoryName":273,"taxonomyType":274,"taxonomyTypeDesc":275},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[277,278,279,280,281,282,283,284,285,36,286,287,288,289,290,291,292,293,189,294],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":296,"name":297,"nameZh":6,"description":298,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":6,"sourceHtmlUrl":299,"categories":300,"keywords":303,"publishedAt":39},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[301,302],{"categoryNo":224,"categoryName":225,"taxonomyType":177,"taxonomyTypeDesc":178},{"categoryNo":272,"categoryName":273,"taxonomyType":274,"taxonomyTypeDesc":275},[277,279,304,36,288,22,305,306,213,293,307,189,214,308,23,309,310],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]