sirvir-scale
sirvir-scale is a Local & open models skill for AI agents, published by SouthpawIN in the sirvir repository on GitHub. Skillspace rates sirvir-scale C (71/100) and it passed the safety scan; the rating is based on static analysis only.
VRAM scaling skill. Wraps `serve vram`, `serve downscale`, and the scaling ladder. Documents the 3-tier (Beefy/Modest/Thin) hardware detection, the 7-step Beefy scaling ladder, the Modest (5-step) and Thin (4-step) ladders, when to trigger downscale, and how to recover when VRAM frees up. Includes the optimization priority ladder (262K → 30 tok/s → 1M → max speed). Complements the turbofit core skill — turbofit launches models, sirvir-scale adapts them to live conditions.
- Grade
- C
- Quality
- 71 / 100
- Safety
- Passed
- GitHub stars
- 25
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
- 90 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. VRAM scaling through sirvir serve commands; product usage, not repo source.
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
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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