cuda-auto-tune

cuda-auto-tune is a Runtime diagnostics & profiling skill for AI agents, published by Bruce-Lee-LY in the cuda_auto_tune repository on GitHub. Skillspace rates cuda-auto-tune F (15/100) and the safety scan rated it unsafe; the rating is based on static analysis only.

NCU-driven iterative optimization workflow for CUDA/CUTLASS/Triton/CuTe DSL kernels. MANDATORY: every optimization MUST start with NCU profiling, followed by multi-dimensional analysis, then targeted code modification, then re-profiling to verify. Supports roofline, memory hierarchy, warp stalls, instruction mix, occupancy, divergence analysis. Provides implementation-specific code modifications: Native CUDA (launch config, memory patterns, async copy, Tensor Core), CUTLASS (ThreadblockShape, stages, epilogue, schedule policy, alignment), Triton (autotune params, compiler hints, tl.* API patterns), CuTe DSL (threads_per_cta, elems_per_thread, tiled_copy, copy atom, shared memory, warp/cta reduce). Use when optimizing any CUDA kernel performance.

Grade
F
Quality
15 / 100
Safety
Unsafe
GitHub stars
23

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since.

View source on GitHubBruce-Lee-LY/cuda_auto_tune

Our review

Architecture
96 / 100
Maturity
Structured: ships references or scripts
Security scan
Critical-risk patterns
License
MIT
Adoption
Adopted by 1 other repositories, in 0 collections

What the scanner flagged

  • destructive rm -rf on home/root

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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