santa-method

Runs two independent review agents in a convergence loop to iteratively verify output quality.

Updated Nov 19, 2025
One-click install
npx skills add https://github.com/Sake-Team/SmartSake --skill santa-method-sake-team
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: santa-method
Source: https://github.com/Sake-Team/SmartSake/tree/main/backup/skills/santa-method
Command: npx skills add https://github.com/Sake-Team/SmartSake --skill santa-method-sake-team

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires generator, reviewer_b, reviewer_c, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of ensuring high-quality, compliant output by implementing a multi-agent adversarial verification system. It helps prevent publication of flawed content that may contain factual errors, inconsistencies, or violate compliance standards.

Core Features & Use Cases

  • Adversarial Review: Uses two independent agents to review the generated content, ensuring unbiased quality control.
  • Convergence Loop: Continues the review process iteratively until both agents agree on the output's quality.
  • Use Case: Ideal for publishing critical content like reports, technical documentation, and customer-facing communication.

Quick Start

Execute the santa-method to review the output produced by your generator before deployment.

Frequently Asked Questions about santa-method

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is multi-agent adversarial review for content verification?

Multi-agent adversarial review is a quality assurance process where two independent agents evaluate generated output against a rubric. It uses a convergence loop for iterative corrections to prevent publishing flawed or non-compliant content.

How do I verify technical documentation quality before publication?

To verify technical documentation quality, you can use an automated multi-agent verification system. Provide the generated output and an evaluation rubric to trigger two independent adversarial reviewers that iterate until both agree on the content's quality.

Do I need to provide a rubric for automated output verification?

Yes, you need to provide a specific rubric for evaluation. The multi-agent adversarial verification system requires both the generated output to be reviewed and a rubric to guide the independent agents during their iterative review and correction loop.

What is the best way to prevent factual errors in customer-facing communication?

The best way to prevent factual errors is using a multi-agent adversarial verification system. It employs two independent agents and a convergence loop to iteratively review and correct content, ensuring high-quality and compliant customer-facing output.

When should I not use an iterative adversarial review process?

You should not use an iterative adversarial review process when you lack an evaluation rubric or the initial generated output to be reviewed. The convergence loop requires both inputs to function correctly for content integrity verification.