ml-system-design-review
ml-system-design-review is a System design & architecture decisions skill for AI agents, published by ML-SystemDesign in the MLSystemDesign repository on GitHub. Skillspace rates ml-system-design-review C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Use when reviewing ML system design docs, ML/AI project repos, design-doc PRs, RAG/LLM/foundation-model architectures, agentic AI workflows, or production ML readiness. Applies the ML System Design framework by Kravchenko and Babushkin to grade designs, compare docs with code, find critical gaps and low-hanging fruit, and give specific non-cringy praise.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 784
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
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Portable: works in any project. Applies a published ML system design framework to any project
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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