jfm-robustness
jfm-robustness is a Statistical modelling skill for AI agents, published by brycewang-stanford in the Awesome-Journal-Skills repository on GitHub. Skillspace rates jfm-robustness D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Use when results may be sensitive to liquidity-measure choice, sample filters, microstructure noise, or inference for a Journal of Financial Markets (JFM) manuscript. Builds the design-based robustness ledger; it does not invent evidence or citations.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 805
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
- 97 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 2 collections
- Scope
- Portable: works in any project. General robustness-check strategy for empirical finance manuscripts
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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