interrogate

Coordinate four-model adversarial reviews of code changes and synthesize a verdict.

2|Updated Apr 3, 2026

Search Tags:#LLM #multi-model #synthesis #code-review #review-automation #adversarial-review

Ready to run

npx skills add https://github.com/kscius/KS-Cursor-Orchestrator --skill interrogate-kscius

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: interrogate
Source: https://github.com/kscius/KS-Cursor-Orchestrator/tree/main/skills/interrogate
Command: npx skills add https://github.com/kscius/KS-Cursor-Orchestrator --skill interrogate-kscius

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinate adversarial, multi-model reviews of code changes by spawning four reviewers across distinct models to surface blind spots and challenge assumptions. The adversarial signal comes from model diversity, not from assigned personas, and the synthesized verdict informs decision-making without auto-applying changes.

Core Features & Use Cases

Spawn four reviewers on four different models to adversarially review code changes using the same prompt and rubric.
Synthesize a final verdict from diverse perspectives, preserving model autonomy and avoiding blind trust in a single model.
Use during high-stakes code reviews, security-sensitive patches, or design critiques to uncover edge cases and hidden risks.

Quick Start

Prompt the system with the target code changes and trigger four-model reviews to produce a synthesized verdict.

Frequently Asked Questions about interrogate

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

FAQPage Schema

How do I run multi-model code reviews to catch blind spots in security-sensitive patches?▼

Multi-model code reviews catch blind spots by spawning four reviewers across distinct models using the same prompt and rubric, synthesizing a final verdict from diverse perspectives to surface hidden risks without auto-applying changes.

What is adversarial code review and when do I need it for high-stakes changes?▼

Adversarial code review challenges assumptions by leveraging model diversity rather than assigned personas. It is needed for high-stakes reviews, security-sensitive patches, and complex design critiques where uncovering edge cases and hidden risks is critical.

How do I use multiple LLMs to review code changes without trusting a single model?▼

To review code changes without trusting a single LLM, spawn four reviewers on four different models using a consistent rubric, then synthesize their independent perspectives into a final verdict that informs your decision-making.

Does multi-model review automation auto-apply code changes after synthesizing a verdict?▼

Multi-model review automation does not auto-apply code changes. It outputs a synthesized verdict from independent model perspectives to inform your decision-making, ensuring human oversight over high-stakes or security-sensitive patches.

Can I use adversarial review for complex design critiques or is it only for code changes?▼

Adversarial review applies to complex design critiques as well as code changes. The approach uses diverse reasoning across four distinct models to uncover edge cases and hidden risks wherever diverse perspectives are valuable.

What do I need to orchestrate four-model adversarial reviews effectively?▼

To orchestrate four-model adversarial reviews effectively, you need a consistent rubric, orchestration across four distinct models, and a synthesis step to generate a final verdict from the independent model perspectives.