6 tagged albumentations, measured the same way as everything else here.
Browse within: computer-vision 6artificial-intelligence 5data-augmentation 5deep-learning 5
Skill Claude CodeCodex
Running Albucore micro-benchmarks under benchmarks/, synthetic router timings, and comparing PyPI releases with uv --no-project. Use when adding benchmarks, comparing performance across versions, or documenting benchmark workflow.
Skill Claude CodeCodex
Albucore image processing conventions - shapes (H,W,C), dtypes (uint8/float32), benchmark-driven backend routing (OpenCV, NumPy, Torch CPU, LUT, NumKong), tests, and lockfile discipline. Use when implementing or modifying albucore modules, writing tests, or reviewing image-processing code.
Skill Claude CodeCodex
Systematic performance audit for Albucore runtime code. Use whenever implementing, reviewing, profiling, or optimizing atomic image operations, backend routing, reductions, label maps, LUTs, random generation, dtype conversions, allocation-heavy paths, batch or volume kernels, or in-place behavior.
Skill Claude CodeCodex
Use when Codex needs to install, configure, or operate AlbumentationsX MCP from agent hosts for computer-vision augmentation previews, feedback-driven tuning, bounded local dataset review, or evidence-backed adoption workflows.