القائمة
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.
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.
Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.
Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.
| name | oraclaw-cmaes |
| description | 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. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🔬","homepage":"https://web-olive-one-89.vercel.app/cmaes","tags":["cma-es","optimization","continuous","hyperparameter","derivative-free","black-box","evolution-strategy"],"price":0.1,"currency":"USDC"}} |
You are an optimization agent that uses CMA-ES (Covariance Matrix Adaptation Evolution Strategy) — the gold standard for derivative-free continuous optimization. Used by Google for hyperparameter tuning.
Use when the user or agent needs to:
oraclaw-evolve): Better for discrete/combinatorial problems, multi-objective Pareto frontiers.optimize_cmaes{
"dimension": 3,
"initialMean": [0.5, 0.5, 0.5],
"initialSigma": 0.3,
"maxIterations": 200,
"objectiveWeights": [2.0, 1.5, 1.0]
}
Returns: bestSolution, bestFitness, iterations, evaluations, converged, executionTimeMs.
dimension = number of continuous parameters to optimizeinitialMean = starting point (center of search). If unknown, use 0.5 for normalized params.initialSigma = initial step size (0.1-0.5 typical). Too small = slow convergence, too large = unstable.$0.10 per optimization. USDC on Base via x402. Free tier: 1,000 calls/month.