metaheuristic-optimization
metaheuristic-optimization is a Physics, mathematics & simulation skill for AI agents, published by kishorkukreja in the awesome-supply-chain repository on GitHub. Skillspace rates metaheuristic-optimization D (57/100) and it passed the safety scan; the rating is based on static analysis only.
When the user wants to solve complex optimization problems using metaheuristics, apply genetic algorithms, simulated annealing, or other nature-inspired algorithms. Also use when the user mentions "genetic algorithm," "simulated annealing," "tabu search," "ant colony," "particle swarm," "evolutionary algorithms," "nature-inspired optimization," "heuristic search," or when exact optimization methods are too slow for large-scale problems. For exact methods, see optimization-modeling. For multi-objective, see multi-objective-optimization.
- Grade
- D
- Quality
- 57 / 100
- Safety
- Passed
- GitHub stars
- 45
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
- 79 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
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
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.