# Sglang Sota Humanize Loop

> Run an autonomous Humanize-governed SGLang SOTA performance loop for one LLM model: first perform a fixed fair SGLang benchmark against the requested comparison framework set, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches SGLang code, optionally uses ncu-report-skill for kernel evidence, and revalidates until SGLang matches or beats the best observed requested framework under the same workload and SLA.

## Facts
- Page: https://tashan.sh/capability/skill-bbuf-sglang-sota-humanize-loop
- tashan id: skill:BBuf/sglang-sota-humanize-loop
- Source: https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS
- Type: skill
- Category: security
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 100.0
- Freshness: 100.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- Official: no

## Install

```sh
cp -r sglang-sota-humanize-loop ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-25 by tashan (https://tashan.sh) from public evidence. Scorer s5.