oraclaw-evolve — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited oraclaw-evolve (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.