[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CSTM30P512QCY6RPFAQFHF":3,"skill-same:01M7CSTM30P512QCY6RPFAQFHF":96},{"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":31,"commitSha":32,"skillDirectory":33,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":36,"ownerHtmlUrl":37,"ownerBlogUrl":6,"ownerDescription":38,"updatedAt":39,"install":40,"resources":53},"01M7CSTM30P512QCY6RPFAQFHF","nv-reason-cxr",null,"Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: nv-reason-cxr\ndescription: Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.\nlicense: Apache-2.0\nallowed-tools: Bash\nmetadata:\n  author: NVIDIA MedTech Team\n  tags:\n    - MedTech\n    - CXR\n    - reasoning\n---\n\n# NV-Reason-CXR\n\n## Purpose\n- Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.\n- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.\n- Manifest I\u002FO: inputs are `chest_xray_image_or_fixture`; outputs are `result_json`.\n\n## Instructions\n- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.\n- Run `scripts\u002Frun_nv_reason_cxr.py` through the documented command below; keep outputs under a caller-provided run directory.\n- If a host agent exposes `run_script`, use `run_script(\"scripts\u002Frun_nv_reason_cxr.py\", args=[...])`; otherwise run the Bash\u002FPython command shown below.\n- Check the emitted JSON and paired verifier guidance before treating the run as evidence.\n\n## Available Scripts\n| Script | Purpose | Arguments |\n|---|---|---|\n| `scripts\u002Frun_nv_reason_cxr.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_CXR_OR_FIXTURE --out-dir OUT_DIR [--mock] [--check-setup]` |\n\n## Prerequisites\n- Runtime requirements: GPU\u002FCUDA when declared by the manifest; Python packages listed in `runtime.side_effects.pip_packages`.\n- Side effects: writes JSON outputs under the caller's `--out-dir`, may cache model assets under `~\u002F.cache\u002Fhuggingface\u002F`, and may contact `https:\u002F\u002Fhuggingface.co` or `https:\u002F\u002Fgithub.com` outside `--mock` mode.\n- Run commands from the repository root unless an existing section below says otherwise.\n\n## Limitations\n- This is a thin wrapper. Image preprocessing, model inference, and decoding are delegated to Hugging Face Transformers and the NV-Reason-CXR-3B model.\n- Output is not a diagnosis, clinical report, treatment recommendation, or triage decision. It is engineering evidence and must be reviewed by a qualified professional before any medical use.\n- The model may hallucinate findings, miss subtle abnormalities, misread support devices, or produce overconfident prose.\n- The committed fixture uses a generated synthetic PNG and deterministic mock response so CI can verify wrapper behavior without downloading model weights. Mock mode is not a substitute for model inference.\n- Not for clinical deployment, clinical interpretation, autonomous diagnosis, treatment decisions.\n\n## Troubleshooting\n| Error | Cause | Fix |\n|---|---|---|\n| Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. |\n| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |\n| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |\n\nRuns NVIDIA-Medtech [`NV-Reason-CXR-3B`](https:\u002F\u002Fgithub.com\u002FNVIDIA-Medtech\u002FNV-Reason-CXR)\nfor chest X-ray image interpretation through the documented Hugging Face\nTransformers inference path. The wrapper does not reimplement the model,\nimage preprocessing, or decoding.\n\n\n## Exact Runnable Surface\n\nFor command-shape smoke tests and JSON fixtures, use this repo-root wrapper path exactly:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE --mock --out-dir OUT_DIR\n```\n\nFor live image inference, omit `--mock` only when the user asks for live model inference. Do not invent `Medical AI Skills run`, `eval_engine\u002Frun.py`, `infer.py`, or `python -m nv_reason_cxr` commands for ordinary user runs.\n\n## Preconditions\n\nInstall the inference dependencies in the environment that will run the\nskill:\n\n```bash\npip install torch==2.7.1 torchvision==0.22.1 transformers==4.56.1 Pillow\n```\n\nThe model weights are loaded from `nvidia\u002FNV-Reason-CXR-3B` through\nTransformers. They may download to the Hugging Face cache on first use.\nSet `TRANSFORMERS_OFFLINE=1` or pass `--local-files-only` only after the\nweights are already cached.\n\nCUDA is expected for practical inference. CPU execution may work for small\ntests but is slow and must be requested explicitly.\n\nCheck the local environment before downloading weights or running inference:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py --check-setup\n```\n\nThe setup report checks importable dependencies, CUDA visibility, Hugging Face\ncache state, and the recommended next step.\n\nOperational environment variables:\n\n| Variable | When to use |\n|---|---|\n| `MOCK_NV_REASON_CXR` | Set to `1` for deterministic command-shape smoke tests without model inference. |\n| `NV_REASON_CXR_MODEL` | Override the Hugging Face model id only for compatibility probes. |\n| `HF_HOME` | Point at a pre-populated Hugging Face cache. |\n| `HF_TOKEN` | Authenticate model downloads when required by the local environment. |\n| `TRANSFORMERS_OFFLINE` | Set to `1` only after weights are already cached. |\n| `HF_HUB_OFFLINE` | Set to `1` only after Hugging Face assets are already cached. |\n\n## License\n\nThe upstream repository code is Apache-2.0. The model weights are released\nunder the NVIDIA OneWay Noncommercial License Agreement. Users are responsible\nfor complying with the model-weight terms before live inference.\n\n## Usage\n\nFrom Medical AI Skills repo root:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR.png \\\n  --prompt \"Find abnormalities and support devices.\" \\\n  --out-dir runs\u002Fnv_reason_cxr_case\n```\n\nUse the wrapper script directly for agent-generated commands. Do not replace\nit with `eval_engine\u002Frun.py` unless the user explicitly asks to run the eval\nharness. Do not redirect stdout with `>` in generated commands: callers and\nthe eval harness read the wrapper's stdout JSON, including\n`output.response_text`, to verify the run. The direct runnable surface is:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE \\\n  --mock \\\n  --out-dir runs\u002Fnv_reason_cxr_case\n```\n\n`PATH_TO_CXR_OR_FIXTURE` may be a PNG\u002FJPEG image or a JSON fixture. If the\nuser provides a JSON request such as\n`runs\u002F...\u002Fsynthetic_cxr_input.json`, pass that exact JSON path as the first\nargument. The script will load `generated:\u002F\u002Fsynthetic_chest_xray` fixtures,\ncreate the temporary PNG under the output directory, and emit JSON with\n`output.response_text`. Use `--mock` only for command-shape smoke tests or\nfixtures that request mock mode; omit `--mock` for live model inference.\n\nFor JPEG input:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR.jpg \\\n  --prompt \"Describe the chest X-ray findings.\" \\\n  --out-dir runs\u002Fnv_reason_cxr_case\n```\n\nFlags:\n\n- `--model-id` — Hugging Face model id, default `nvidia\u002FNV-Reason-CXR-3B`.\n- `--device auto|cuda|cpu` — default `auto`, using CUDA when available.\n- `--allow-cpu` — required for live CPU inference; CPU runs can be very slow.\n- `--torch-dtype auto|float16|bfloat16|float32` — default `auto`, using\n  bfloat16 on CUDA and float32 on CPU, matching the published BF16 model.\n- `--max-new-tokens` — generation cap, default 2048.\n- `--local-files-only` — use only locally cached Hugging Face assets.\n- `--mock` — deterministic dry-run response for CI and wiring checks.\n- `--prompt-preset findings|comprehensive|educational|structured` — optional\n  known-good prompt presets from the model card\u002Fdemo behavior.\n\nThe tested local live path uses:\n\n- `AutoModelForImageTextToText.from_pretrained(..., dtype=torch.bfloat16).eval().to(\"cuda\")`\n- `AutoProcessor.from_pretrained(..., use_fast=True)`\n- PNG\u002FJPEG image input plus one text prompt\n- `max_new_tokens=2048` by default\n\nThe script emits JSON on stdout and writes no clinical report files. It records\ninput image metadata, prompt, model id, runtime mode, response text, and known\nlimitations. If `runtime.truncated_by_max_new_tokens` is `true`, rerun with a\nhigher `--max-new-tokens` value.\n\n## Fixture Smoke Test\n\nThe committed fixture uses a generated synthetic PNG and mock mode so the\neval harness can verify the wrapper without downloading weights:\n\n```bash\npython eval_engine\u002Frun.py skills\u002Fnv-reason-cxr \\\n  --fixture skills\u002Fnv-reason-cxr\u002Ffixtures\u002Fsynthetic_cxr_input.json \\\n  --out runs\u002Fnv_reason_cxr_smoke\n```\n\n## Limits\n\nThis is research and engineering tooling only. It is not validated for\nclinical diagnosis, treatment decisions, triage, patient-facing reporting, or\nregulatory use. Model outputs can hallucinate, miss subtle findings, or\noverstate uncertainty. A qualified professional must review any use in a\nmedical workflow.\n","# NV-Reason-CXR\n\n## Purpose\n- Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.\n- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.\n- Manifest I\u002FO: inputs are `chest_xray_image_or_fixture`; outputs are `result_json`.\n\n## Instructions\n- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.\n- Run `scripts\u002Frun_nv_reason_cxr.py` through the documented command below; keep outputs under a caller-provided run directory.\n- If a host agent exposes `run_script`, use `run_script(\"scripts\u002Frun_nv_reason_cxr.py\", args=[...])`; otherwise run the Bash\u002FPython command shown below.\n- Check the emitted JSON and paired verifier guidance before treating the run as evidence.\n\n## Available Scripts\n| Script | Purpose | Arguments |\n|---|---|---|\n| `scripts\u002Frun_nv_reason_cxr.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_CXR_OR_FIXTURE --out-dir OUT_DIR [--mock] [--check-setup]` |\n\n## Prerequisites\n- Runtime requirements: GPU\u002FCUDA when declared by the manifest; Python packages listed in `runtime.side_effects.pip_packages`.\n- Side effects: writes JSON outputs under the caller's `--out-dir`, may cache model assets under `~\u002F.cache\u002Fhuggingface\u002F`, and may contact `https:\u002F\u002Fhuggingface.co` or `https:\u002F\u002Fgithub.com` outside `--mock` mode.\n- Run commands from the repository root unless an existing section below says otherwise.\n\n## Limitations\n- This is a thin wrapper. Image preprocessing, model inference, and decoding are delegated to Hugging Face Transformers and the NV-Reason-CXR-3B model.\n- Output is not a diagnosis, clinical report, treatment recommendation, or triage decision. It is engineering evidence and must be reviewed by a qualified professional before any medical use.\n- The model may hallucinate findings, miss subtle abnormalities, misread support devices, or produce overconfident prose.\n- The committed fixture uses a generated synthetic PNG and deterministic mock response so CI can verify wrapper behavior without downloading model weights. Mock mode is not a substitute for model inference.\n- Not for clinical deployment, clinical interpretation, autonomous diagnosis, treatment decisions.\n\n## Troubleshooting\n| Error | Cause | Fix |\n|---|---|---|\n| Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. |\n| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |\n| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |\n\nRuns NVIDIA-Medtech [`NV-Reason-CXR-3B`](https:\u002F\u002Fgithub.com\u002FNVIDIA-Medtech\u002FNV-Reason-CXR)\nfor chest X-ray image interpretation through the documented Hugging Face\nTransformers inference path. The wrapper does not reimplement the model,\nimage preprocessing, or decoding.\n\n\n## Exact Runnable Surface\n\nFor command-shape smoke tests and JSON fixtures, use this repo-root wrapper path exactly:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE --mock --out-dir OUT_DIR\n```\n\nFor live image inference, omit `--mock` only when the user asks for live model inference. Do not invent `Medical AI Skills run`, `eval_engine\u002Frun.py`, `infer.py`, or `python -m nv_reason_cxr` commands for ordinary user runs.\n\n## Preconditions\n\nInstall the inference dependencies in the environment that will run the\nskill:\n\n```bash\npip install torch==2.7.1 torchvision==0.22.1 transformers==4.56.1 Pillow\n```\n\nThe model weights are loaded from `nvidia\u002FNV-Reason-CXR-3B` through\nTransformers. They may download to the Hugging Face cache on first use.\nSet `TRANSFORMERS_OFFLINE=1` or pass `--local-files-only` only after the\nweights are already cached.\n\nCUDA is expected for practical inference. CPU execution may work for small\ntests but is slow and must be requested explicitly.\n\nCheck the local environment before downloading weights or running inference:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py --check-setup\n```\n\nThe setup report checks importable dependencies, CUDA visibility, Hugging Face\ncache state, and the recommended next step.\n\nOperational environment variables:\n\n| Variable | When to use |\n|---|---|\n| `MOCK_NV_REASON_CXR` | Set to `1` for deterministic command-shape smoke tests without model inference. |\n| `NV_REASON_CXR_MODEL` | Override the Hugging Face model id only for compatibility probes. |\n| `HF_HOME` | Point at a pre-populated Hugging Face cache. |\n| `HF_TOKEN` | Authenticate model downloads when required by the local environment. |\n| `TRANSFORMERS_OFFLINE` | Set to `1` only after weights are already cached. |\n| `HF_HUB_OFFLINE` | Set to `1` only after Hugging Face assets are already cached. |\n\n## License\n\nThe upstream repository code is Apache-2.0. The model weights are released\nunder the NVIDIA OneWay Noncommercial License Agreement. Users are responsible\nfor complying with the model-weight terms before live inference.\n\n## Usage\n\nFrom Medical AI Skills repo root:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR.png \\\n  --prompt \"Find abnormalities and support devices.\" \\\n  --out-dir runs\u002Fnv_reason_cxr_case\n```\n\nUse the wrapper script directly for agent-generated commands. Do not replace\nit with `eval_engine\u002Frun.py` unless the user explicitly asks to run the eval\nharness. Do not redirect stdout with `>` in generated commands: callers and\nthe eval harness read the wrapper's stdout JSON, including\n`output.response_text`, to verify the run. The direct runnable surface is:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE \\\n  --mock \\\n  --out-dir runs\u002Fnv_reason_cxr_case\n```\n\n`PATH_TO_CXR_OR_FIXTURE` may be a PNG\u002FJPEG image or a JSON fixture. If the\nuser provides a JSON request such as\n`runs\u002F...\u002Fsynthetic_cxr_input.json`, pass that exact JSON path as the first\nargument. The script will load `generated:\u002F\u002Fsynthetic_chest_xray` fixtures,\ncreate the temporary PNG under the output directory, and emit JSON with\n`output.response_text`. Use `--mock` only for command-shape smoke tests or\nfixtures that request mock mode; omit `--mock` for live model inference.\n\nFor JPEG input:\n\n```bash\npython skills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py PATH_TO_CXR.jpg \\\n  --prompt \"Describe the chest X-ray findings.\" \\\n  --out-dir runs\u002Fnv_reason_cxr_case\n```\n\nFlags:\n\n- `--model-id` — Hugging Face model id, default `nvidia\u002FNV-Reason-CXR-3B`.\n- `--device auto|cuda|cpu` — default `auto`, using CUDA when available.\n- `--allow-cpu` — required for live CPU inference; CPU runs can be very slow.\n- `--torch-dtype auto|float16|bfloat16|float32` — default `auto`, using\n  bfloat16 on CUDA and float32 on CPU, matching the published BF16 model.\n- `--max-new-tokens` — generation cap, default 2048.\n- `--local-files-only` — use only locally cached Hugging Face assets.\n- `--mock` — deterministic dry-run response for CI and wiring checks.\n- `--prompt-preset findings|comprehensive|educational|structured` — optional\n  known-good prompt presets from the model card\u002Fdemo behavior.\n\nThe tested local live path uses:\n\n- `AutoModelForImageTextToText.from_pretrained(..., dtype=torch.bfloat16).eval().to(\"cuda\")`\n- `AutoProcessor.from_pretrained(..., use_fast=True)`\n- PNG\u002FJPEG image input plus one text prompt\n- `max_new_tokens=2048` by default\n\nThe script emits JSON on stdout and writes no clinical report files. It records\ninput image metadata, prompt, model id, runtime mode, response text, and known\nlimitations. If `runtime.truncated_by_max_new_tokens` is `true`, rerun with a\nhigher `--max-new-tokens` value.\n\n## Fixture Smoke Test\n\nThe committed fixture uses a generated synthetic PNG and mock mode so the\neval harness can verify the wrapper without downloading weights:\n\n```bash\npython eval_engine\u002Frun.py skills\u002Fnv-reason-cxr \\\n  --fixture skills\u002Fnv-reason-cxr\u002Ffixtures\u002Fsynthetic_cxr_input.json \\\n  --out runs\u002Fnv_reason_cxr_smoke\n```\n\n## Limits\n\nThis is research and engineering tooling only. It is not validated for\nclinical diagnosis, treatment decisions, triage, patient-facing reporting, or\nregulatory use. Model outputs can hallucinate, miss subtle findings, or\noverstate uncertainty. A qualified professional must review any use in a\nmedical workflow.\n",{"name":5,"license":15,"metadata":24,"description":7,"allowed-tools":30},{"tags":25,"author":29},[26,27,28],"MedTech","CXR","reasoning","NVIDIA MedTech Team","Bash","main","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill nv-reason-cxr",{"tool":46,"command":47},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill nv-reason-cxr",{"tool":49,"command":50},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill nv-reason-cxr","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002FSKILL.md",true,[54,60,64,68,74,78,82,88,92],{"path":55,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":58,"sourceHtmlUrl":59},"BENCHMARK.md","REFERENCE","参考文档",4132,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002FBENCHMARK.md",{"path":61,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":62,"sourceHtmlUrl":63},"evals\u002Fevals.json",1269,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Fevals\u002Fevals.json",{"path":65,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":66,"sourceHtmlUrl":67},"fixtures\u002Fsynthetic_cxr_input.json",387,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Ffixtures\u002Fsynthetic_cxr_input.json",{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"scripts\u002Frun_nv_reason_cxr.py","SCRIPT","脚本",24993,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Fscripts\u002Frun_nv_reason_cxr.py",{"path":75,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":76,"sourceHtmlUrl":77},"skill_manifest.yaml",6716,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Fskill_manifest.yaml",{"path":79,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":80,"sourceHtmlUrl":81},"skill-card.md",3569,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Fskill-card.md",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"skill.oms.sig","OTHER","其他",5785,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Fskill.oms.sig",{"path":89,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":90,"sourceHtmlUrl":91},"tests\u002Ftest_nv_reason_cxr.py",4252,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Ftests\u002Ftest_nv_reason_cxr.py",{"path":93,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":94,"sourceHtmlUrl":95},"validators\u002Foutput_schema.json",2848,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fnv-reason-cxr\u002Fvalidators\u002Foutput_schema.json",[97,105,112,119,126,133],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include \"Vercel Sandbox browser\", \"microVM Chrome\", \"agent-browser in sandbox\", \"browser automation on Vercel\", or any task requiring Chrome in a Vercel Sandbox.","MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fvercel-sandbox\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":19},"01M7CTD8D2BNJY54S65M47HEJA","slack","Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include \"automate Slack app\", \"control VS Code\", \"interact with Discord app\", \"test this Electron app\", \"connect to desktop app\", or any task requiring automation of a native Electron application.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Felectron\u002FSKILL.md",[],[],{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":19},"01M7CTCBJY76688V4DCZGWFHGQ","dogfood","Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to \"dogfood\", \"QA\", \"exploratory test\", \"find issues\", \"bug hunt\", \"test this app\u002Fsite\u002Fplatform\", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fdogfood\u002FSKILL.md",[],[],{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":19},"01M7CTC4H0FFGVEAQR9PA4GA1A","agent-browser","Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to \"open a website\", \"fill out a form\", \"click a button\", \"take a screenshot\", \"scrape data from a page\", \"test this web app\", \"login to a site\", \"automate browser actions\", or any task requiring programmatic web interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fagent-browser\u002FSKILL.md",[],[],{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":139,"publishedAt":19},"01M7CTBKVFYBG78FDSABSDJTTQ","shadcn","Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn\u002Fui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for \"shadcn init\", \"create an app with --preset\", \"switch to --preset\", and for building a design system or applying a DESIGN.md or brand spec to shadcn\u002Fui.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-UI-界面组件\u002FYYC3-UI-界面组件-ui\u002Fskills\u002Fshadcn\u002FSKILL.md",[],[]]