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name: mmskeleton description: "Guides MMSkeleton skeleton-based action-recognition, skeleton-data preparation, config-driven applications, and optional detector-backed pose workflows with explicit legacy compatibility and verification gates." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMSkeleton

Use this repository skill when a task names MMSkeleton, mmskl, ST-GCN, skeleton-based action recognition, OpenPose/NTU skeleton layouts, MMSkeleton JSON annotations, or the repository's cascade-RCNN/HRNet pose pipeline.

This is a router, not a copy of the original source tree. Start with the smallest route below, then read only the linked references needed for the task.

Verified scope and hard boundaries

• Verified core: ST-GCN graph/model construction, recognition configuration guidance, skeleton JSON/data transforms, checkpoint alias interpretation, config-driven application structure, and the repository's compiled NMS extension. A tiny CUDA ST-GCN forward passed in a CUDA-capable inspection environment.
• Optional and unverified: MMDetection-backed pose_demo, pose_demo_HD, image pose inference, and video-to-skeleton dataset building. These require a detector-compatible mmcv._ext build, detector/HRNet checkpoints, video dependencies, and GPU resources. Do not infer their readiness from the core ST-GCN smoke.
• Not bundled: original checkout files, datasets, downloaded checkpoints, video assets, or large training outputs. Use local paths supplied by the caller and validate them before running an expensive workflow.

Route by user intent

1. ST-GCN recognition, graph/model API, pretrained evaluation, training config, checkpoint aliases, or mmskl flags: read recognition.
2. Skeleton JSON, category labels, loader, normalization, masks, temporal transforms, custom feeder, or data validation: read data-preparation first, then route model/layout choices to recognition.
3. Pose demo, image/video pose inference, or raw video to skeleton JSON: read pose-estimation and run its readiness checker before downloading anything. Return produced JSON to data-preparation before recognition.
4. A new config-driven application: read the recognition CLI and configuration reference and the relevant workflow skill. MMSkeleton dispatches through a config's processor_cfg; flags are declared by argparse_cfg and bound into that config.

Installation and environment gate

The repository is a legacy Python package. Its historical documentation used Python 3.7 with PyTorch 1.2 and CUDA 9.2/10.0; those exact artifacts may be unavailable. Use an isolated, compatible Python 3.7 environment and verify the selected PyTorch CUDA build, compiler/toolkit, MMCV generation, and native extensions before trusting a workflow. Do not mutate a user's environment.

A public baseline install is explicit and isolated; adapt the CUDA/torch versions to the supported wheel/channel available on the target host:

conda create --yes --prefix /path/to/mmskeleton-legacy python=3.7 pip
conda install --yes --prefix /path/to/mmskeleton-legacy -c pytorch pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7
conda run --prefix /path/to/mmskeleton-legacy python -m pip install mmcv==1.7.2 lazy-import
# Run the editable install from the root of an MMSkeleton checkout.
cd /path/to/MMSkeleton
conda run --prefix /path/to/mmskeleton-legacy env FORCE_CUDA=1 python -m pip install -e . --no-deps

The source documentation names an older PyTorch 1.2/CUDA 9.2 or 10.0 combination; use the exact historical pair only when it is still available and compatible with the host. Align nvcc and GCC/G++ before building extensions. For a bounded package check, run the recognition smoke helper after installing the package and dependencies:

python sub-skills/recognition/scripts/run_stgcn_smoke.py --device auto

Use --device cuda for the required GPU gate. Read compatibility and troubleshooting when the historical dependency set cannot be installed. For pose-specific readiness, run the no-download checker linked from the pose sub-skill.

Shared references

• Read repository provenance before deciding whether this graph is stale for a different MMSkeleton commit.
• Read model and compatibility notes for the verified baseline, historical-version caveat, checkpoint/download boundary, and optional detector limitation.
• Read cross-cutting troubleshooting for import, CUDA/compiler, MMCV, checkpoint, config, and safety failures.
• Read router metadata only when integrating this skill into a managed repo-skills router; it is structured metadata, not a user workflow.

Safety and verification discipline

Prefer a tiny synthetic model/data smoke and JSON validation before launching full training, pretrained evaluation, checkpoint downloads, or video workers. A full recognition result requires the correct processed dataset, class count, graph layout, checkpoint, and CUDA runtime; a parser/help check is not an accuracy result. A raw-video request requires the separate optional detector gate. Preserve unresolved backend limitations in the task report instead of silently substituting CPU behavior.