Multi-objective evolutionary optimization for per-metric percentile cutoffs and intersection-based config selection. TRIGGERS - ranking optimization, cutoff search, metric intersection, Optuna cutoffs, evolutionary search, percentile ranking, multi-objective ranking, config selection, survivor analysis, binding metrics, Pareto frontier cutoffs.
Quickly install evolutionary-metric-ranking AI skill to your development environment via command line
Source: terrylica/cc-skills.
Methodology for systematically zooming into high-quality configurations across multiple evaluation metrics using per-metric percentile cutoffs, intersection-based filtering, and evolutionary optimization. Domain-agnostic principles with quantitative trading case studies.
Companion skills: rangebar-eval-metrics (metric definitions) | adaptive-wfo-epoch (WFO integration) | backtesting-py-oracle (SQL validation)
Raw metric values live on incompatible scales (Kelly in [-1,1], trade count in [50, 5000], Omega in [0.8, 2.0]). Percentile ranking normalizes every metric to [0, 100], making cross-metric comparison meaningful.
Multi-objective evolutionary optimization for per-metric percentile cutoffs and intersection-based config selection. TRIGGERS - ranking optimization, cutoff search, metric intersection, Optuna cutoffs, evolutionary search, percentile ranking, multi-objective ranking, config selection, survivor analysis, binding metrics, Pareto frontier cutoffs. Source: terrylica/cc-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/terrylica/cc-skills --skill evolutionary-metric-rankingMulti-objective evolutionary optimization for per-metric percentile cutoffs and intersection-based config selection. TRIGGERS - ranking optimization, cutoff search, metric intersection, Optuna cutoffs, evolutionary search, percentile ranking, multi-objective ranking, config selection, survivor analysis, binding metrics, Pareto frontier cutoffs. Source: terrylica/cc-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/terrylica/cc-skills --skill evolutionary-metric-ranking Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/terrylica/cc-skills