fla-optimization-loop
fla-optimization-loop is an MLOps & model serving skill for AI agents, published by fla-org in the flash-linear-attention repository on GitHub. Skillspace rates fla-optimization-loop D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Disciplined, reproducible loop for making an FLA kernel faster (Triton, Gluon, TileLang, CuTe) without ever breaking or gaming correctness. Synthesizes the task-contract / three-phase / iteration-protocol / silent-bug-catalog discipline of agent kernel-optimization frameworks (KDA, the MLSys FlashInfer contest workflow, AKO4ALL/AKO4X), and anchors all of it on FLA's frozen pytest (forward AND backward, under NaN poisoning) as the immutable correctness gate. Use when iterating on `fla/ops/**` performance over multiple rounds.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 5,342
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
- 84 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- 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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