s4h-evolution-fitness-landscape
s4h-evolution-fitness-landscape is a Structured reasoning & decision frameworks skill for AI agents, published by human-avatar in the skills-for-humanity repository on GitHub. Skillspace rates s4h-evolution-fitness-landscape D (65/100) and it passed the safety scan; the rating is based on static analysis only.
Maps the solution space as peaks and valleys to understand local optima, adaptive valleys, and path dependence. Triggers: 'are we stuck', 'local maximum', 'fitness landscape', 'why can't we get to the next level', 'path dependence', 'trapped by success', 'incremental improvement is not working', 'we need a discontinuous leap', 'adjacent possible'.
- Grade
- D
- Quality
- 65 / 100
- Safety
- Passed
- GitHub stars
- 201
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
- 94 / 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
- 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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