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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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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.
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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.