cuda-kernel-profiling
cuda-kernel-profiling is a Runtime diagnostics & profiling skill for AI agents, published by Llaminar in the llaminar repository on GitHub. Skillspace rates cuda-kernel-profiling D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Profile and tune Llaminar V2 CUDA kernels using LLAMINAR_PROFILING (per-kernel + per-stage timing), Nsight Systems (nsys), and Nsight Compute (ncu), then validate with the GEMM perf-test harness, the benchmark subcommand, and parity tests. Use when asked to find the slowest CUDA kernel, diagnose occupancy / register pressure / memory-bound stalls, A/B two kernel variants, or close a prefill/decode throughput gap while keeping PyTorch parity.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- 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, so go by its current files.
Our review
- Architecture
- 96 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- AGPL-3.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
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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