phase-4-prefill-optimization
phase-4-prefill-optimization is an MLOps & model serving skill for AI agents, published by Xilinx in the mlir-air repository on GitHub. Skillspace rates phase-4-prefill-optimization D (56/100) and it passed the safety scan; the rating is based on static analysis only.
Phase 4 of LLM deployment — apply the shared optimization skillset to a Phase-3-correct prefill pipeline (multi-launch merge, BO pre-loading + intermediate buffer reuse, seq-first layout). Thin orchestrator that dispatches `opt-merge-multi-launch-kernels`, `opt-buffer-object-reuse`, and `opt-layout-alignment`. Each step preserves correctness by re-running the Phase 3 gate — `make verify` (token-set vs HF bf16) is the PASS/FAIL gate; `make diagnosis` per-layer cosine is the informational lens used to localize a regression. Invoked after Phase 3 PASS.
- Grade
- D
- Quality
- 56 / 100
- Safety
- Passed
- GitHub stars
- 142
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
- 86 / 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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