TAO FastFoundationStereo Real-Time Stereo Depth

The skill limits declared tools to Read and Bash and requires Docker, NVIDIA Container Toolkit, read-only data mounts, and user-supplied checkpoints. It discloses data, output, and checkpoint paths and describes the training, evaluation, export, and deployment chain. Deductions apply because Bash and Docker permit substantial external computation, file writes, and GPU-resource effects, while the documentation lacks explicit stepwise user confirmation, strong isolation, sensitive-data handling, dependency-integrity checks, and rollback procedures. The license and NVIDIA provenance improve traceability but do not replace safety controls.

The documentation gives concrete guidance on model widths, data formats, checkpoint resolution, KPI differences, common errors, and silent failures, and it specifies AutoML versus direct-training routing. Deductions apply because static review cannot reproduce the key paths; the container, TAO Core, tao-skill-bank, TensorRT, and referenced templates are not fully available in the supplied evidence, and some failures still shift diagnosis to the user. Under static calibration, the score is capped at 10.

Trigger phrases, supported actions, raw bp2 deployment and fine-tuning boundaries, dataset classes, inference input formats, and checkpoint prerequisites are described. Deductions apply because the skill requires an NVIDIA GPU, Docker, NVIDIA Container Toolkit, a commercial bp2 checkpoint, and reachable data paths. Chinese-language interaction and mainland-China network reachability are not documented, while examples depend on S3 and container infrastructure, so environment-fit evidence is incomplete.

Metadata provides a name, version 0.1.0, author, license, compatibility requirement, and tags; the documentation is layered across prerequisites, workflow, parameters, error patterns, and deployment references. Deductions apply because the benchmark report identifies missing recommended Instructions and Examples sections, a nonconforming author format, and unexpected folder depth. No clear changelog, maintenance owner, support lifecycle, or update path is provided, and several key details depend on referenced files not fully shown in the evidence.

The skill covers training, evaluation, inference, export, TensorRT engine generation, and deployment, with directly reusable YAML/Python parameter blocks, verification metrics, and a seven-action workflow. Deductions apply because nothing was executed for this review; output quality and the roughly 10× latency claim were not independently verified, and users must still provide the container, GPU, dataset, bp2 checkpoint, and correct paths and configurations. Under static calibration, the score is capped at 7.

The evidence includes fixed revision metadata, parameter templates, error-pattern documentation, an evaluation task, and an NVSkills-Eval report covering security, correctness, discoverability, and static findings. Deductions apply because the benchmark has only one task and no negative tasks, its reported results do not replace independent reproduction here, and there is no multi-source corroboration; key model behavior and performance claims remain primarily documentation assertions. Under static calibration, the score is capped at 5.