What problem does it solve? Plausible-looking algorithmic and code-design solutions often hide failure modes—complexity blow-ups, correctness bugs, or operational fragility—that pure reasoning misses. This Skill stress-tests a baseline solution by dispatching an attacker subagent to produce a concrete breaking input, a challenger subagent to propose a surviving alternative, and an execution harness to run both candidates against shared tests, so a judge decides based on measured evidence rather than argument. ## Core Features & Use Cases - Adversarial propose-attack-measure loop: Baseline proposal, attacker producing concrete failing inputs, challenger proposing a different approach, execution harness running both candidates, and a judge deciding or demanding round two. - Execution-backed validation: Runs candidate implementations in isolated subprocesses via JavaScript/TypeScript (Node) and Python adapters, capturing pass/fail/timeout outcomes and wall-clock timings. - Schema-validated subagent outputs: JSON Schema validation (via ajv or an inline no-Node fallback) enforces concrete attacker inputs, anti-overlap between baseline and challenger, and structured judge decisions. - Use Case: Ask whether your LRU cache with TTL is correct under adversarial load—the attacker constructs a 10,000 short-TTL insert wave, the challenger proposes an eager-expiry min-heap design, and the harness shows the baseline taking 17x longer on the attack input, so the judge endorses the challenger. ## Quick Start Invoke /spar-exocortex with your algorithmic or code-design problem, for example: stress-test my LRU cache with TTL implementation in JavaScript.
