cfd-open-discovery
cfd-open-discovery is a Physics, mathematics & simulation skill for AI agents, published by csml-rpi in the AI-CFD-Scientist repository on GitHub. Skillspace rates cfd-open-discovery F (46/100) and it passed the safety scan; the rating is based on static analysis only.
Open-ended discovery loop — propose novel CFD model modifications (e.g. SA / k-omega variants, viscosity laws), implement via cfd-code-modify, run via cfd-experiment, score against baseline + reference, repeat until budget. Self-contained — embeds MetricProposer, ComparatorAuthor (text + PyVista), ComparatorVerifier, and the LLM-judge prompt verbatim. Use for topics with "open-ended", "discover", "find a novel", "beat baseline", "best model".
- Grade
- F
- Quality
- 46 / 100
- Safety
- Passed
- GitHub stars
- 37
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
- 68 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- 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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