| 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"}} |
OraClaw CMA-ES — SOTA Continuous Optimizer for Agents
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.
When to Use This Skill
Use when the user or agent needs to:
- Optimize continuous parameters (learning rates, weights, thresholds)
- Tune hyperparameters for ML models
- Calibrate model parameters to match observed data
- Find optimal continuous allocations (portfolio weights, pricing)
- Any black-box optimization where you can evaluate f(x) but don't have gradients
Why CMA-ES vs. Genetic Algorithm?
- CMA-ES: 10-100x more sample-efficient on smooth continuous problems. Learns the correlation structure of the search space. SOTA for continuous optimization.
- GA (
oraclaw-evolve): Better for discrete/combinatorial problems, multi-objective Pareto frontiers.
- Use CMA-ES for continuous. Use GA for discrete.
Tool: 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.
Rules
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.- CMA-ES MINIMIZES the objective. To maximize, negate the weights.
- Converges in O(dimension^2) iterations typically. Dimension 10 needs ~100-300 iterations.
Pricing
$0.10 per optimization. USDC on Base via x402. Free tier: 1,000 calls/month.
