kernel-optimization
kernel-optimization is an MLOps & model serving skill for AI agents, published by cklxx in the arle repository on GitHub. Skillspace rates kernel-optimization F (50/100) and it passed the safety scan; the rating is based on static analysis only.
Use this skill when ckl asks to optimize an ARLE kernel, operator, attention path, GEMM, decode/prefill path, quantization op, scheduler hot path, TTFT/ITL/tok-s metric, memory footprint, or any "optimize this operator" / "tune the kernel" / "make this faster" request. It enforces formula-predict -> measured binding constraint -> matched single-variable A/B -> interaction A/B when needed -> explicit tradeoff -> license-or-kill, and keeps the industry catalog scoped to ARLE CUDA/TileLang/MLX/runtime work.
- Grade
- F
- Quality
- 50 / 100
- Safety
- Passed
- GitHub stars
- 20
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 63 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
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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