Santa Method

Multi-Agent Adversarial Verification

The Santa Method solves the fundamental problem of agentic bias: a single AI model reviewing its own work often misses its own systematic errors and hallucinations. This skill implements a rigorous "make a list, check it twice" architecture that ensures high-stakes output is verified by two independent review agents before it ever reaches production.

What it does

• Dual Independent Review: Spawns two parallel sub-agents with zero shared context to evaluate output against a strict rubric.
• Convergence Loop: If either reviewer finds a "naughty" issue, the generator must fix the output and restart the double-blind review process.
• Structured Verdict Gate: Requires a unanimous "PASS" from both independent reviewers to proceed, eliminating subjective rubber-stamping.
• Batch Sampling: Provides optimized patterns for verifying high-volume content through stratified sampling and pattern-based fixing.

Why use this skill?

Unlike simple prompting, this skill enforces context isolation. By ensuring reviewers cannot see each other's work or the generator's internal logic, it breaks the feedback loops that lead to confident hallucinations. It is ideal for technical documentation, customer-facing copy, and production code where the cost of failure is high.