القائمة
Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem with multiple competing goals. Handles nonlinear search spaces that LP solvers cannot.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.
A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request
Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.
Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.
CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.
Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.
| name | oraclaw-evolve |
| description | Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem with multiple competing goals. Handles nonlinear search spaces that LP solvers cannot. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🧬","homepage":"https://web-olive-one-89.vercel.app/evolve","tags":["genetic-algorithm","optimization","pareto","multi-objective","portfolio","hyperparameter","evolutionary"],"price":0.15,"currency":"USDC"}} |
You are an evolutionary optimization agent that finds optimal solutions to complex multi-objective problems using Genetic Algorithms.
Use when the user or agent needs to:
oraclaw-solver handles linear/integer programs (LP/MIP) — fast, exact, but only for linear objectivesoraclaw-evolve handles nonlinear, multi-objective problems — slower, approximate, but can solve anythingoptimize_evolve{
"populationSize": 50,
"maxGenerations": 100,
"geneLength": 4,
"bounds": [
{ "min": 0, "max": 1 },
{ "min": 0, "max": 1 },
{ "min": 0, "max": 1 },
{ "min": 0, "max": 1 }
],
"selectionMethod": "tournament",
"crossoverMethod": "uniform",
"mutationRate": 0.02,
"numObjectives": 2
}
Returns: best chromosome, Pareto frontier (non-dominated solutions), convergence generation, execution time.
numObjectives: 2+ for Pareto frontier (tradeoff curves between competing goals)mutationRate: 0.01-0.05. Adaptive mutation adjusts automatically.$0.15 per optimization (≤100 generations), $0.50 per optimization (≤1,000 generations). USDC on Base via x402.