What problem does it solve? Long coding sessions quietly turn assumptions into facts, and confident answers are not always correct ones. This Skill catches wrong decisions early by forcing every non-trivial decision through an adversarial fresh-context review while course-correction is still cheap, instead of discovering bugs at PR time or in production. ## Core Features & Use Cases - Five-step doubt cycle: CLAIM the decision, EXTRACT the smallest reviewable artifact plus contract, DOUBT it with an adversarial fresh-context reviewer, RECONCILE findings by precedence (contract misread, actionable, trade-off, noise), and STOP within a bounded 3-cycle loop. - Cross-model escalation: Offers a second opinion via a consensus skill or external CLIs (Gemini, Codex) with strict safety rules, including read-only sandboxes, stdin piping to avoid shell injection, and explicit per-invocation user authorization. - Use Case: Before committing a new caching layer claimed to be thread-safe, extract the diff and its contract, spawn an adversarial reviewer instructed only to find issues, classify each finding against the artifact text, and fix or document trade-offs before shipping. ## Quick Start Ask the AI to apply doubt-driven development to review the thread-safety claim in your new caching layer before committing it.