elodin-monte-carlo
elodin-monte-carlo is a Physics, mathematics & simulation skill for AI agents, published by elodin-sys in the elodin repository on GitHub. Skillspace rates elodin-monte-carlo D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Develop and calibrate simulations against experimental truth data using elodin monte-carlo. Use when vendoring real telemetry as a reference profile, adding a truth-replay ghost entity, writing campaign specs/hooks and run scoring, reconstructing missing data channels from physics, or iterating on guidance and physics models with campaign feedback.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 535
Unproven: rated by static analysis only, not yet compared head-to-head.
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
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Category is a classifier prediction; no model has read this skill 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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