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Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound using roofline analysis, occupancy calculations, compute/load ratio per tile, and SASS instruction inspection. Produces a decision matrix for optimization strategy selection (cp.async, warp interleaving, tiling, double-buffering, or CuAssembler hand-tuning).
analyze-kernel-bottleneck is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Analyze Kernel Bottleneck Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound by measuring baseline performance, cla. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding analyze-kernel-bottleneck to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install pjt222/agent-almanacReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Analyze Kernel Bottleneck Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound by measuring baseline performance, classifying on the roof