interrogate

Spawns multiple LLM reviewers to adversarially review code changes and synthesizes a verdict.

Updated Mar 27, 2026
One-click install
npx skills add https://github.com/gmackie/agent-skills --skill interrogate-gmackie
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: interrogate
Source: https://github.com/gmackie/agent-skills/tree/main/skills/interrogate
Command: npx skills add https://github.com/gmackie/agent-skills --skill interrogate-gmackie

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Single-reviewer code reviews miss blind spots and inflate nitpicks. This Skill runs the same diff through several independent LLM reviewers, then applies lead-reviewer judgment to separate real issues from noise so you know what actually needs fixing. ## Core Features & Use Cases - Multi-Model Adversarial Review: Spawns one reviewer per configured model, each applying the same rubric and code-quality lens to the same diff. - Consensus Synthesis: Deduplicates findings, highlights issues raised by 2+ models as high-confidence signal, and maps where models agree or diverge. - Lead Judgment Filtering: Categorizes every finding as act on, consider, noted, or dismissed, with rationale, so the verdict is actionable rather than exhaustive. - Use Case: Before merging a feature branch, ask for an adversarial review of git diff main...HEAD; receive a structured verdict listing the handful of issues that would block a real PR plus the dismissed nitpicks and why. ## Quick Start Ask the agent to interrogate the changes on your current feature branch and produce a synthesized adversarial review verdict.

Frequently Asked Questions about interrogate

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

FAQPage Schema
How do I run a multi-model adversarial code review?▼

Point the skill at specific files, a diff, or a feature branch and it runs git diff against the base branch. It spawns one reviewer per configured model with the same prompt and rubric, then synthesizes their findings into a single verdict.

What does the interrogate skill review in a diff?▼

Reviewers apply a rubric covering correctness, root causes versus symptoms, structural integrity, verification, complexity budget, and security, plus a code-quality lens targeting structural simplification and spaghetti growth. Each finding includes severity, evidence, and an optional suggestion.

How are conflicting findings from different models handled?▼

Findings raised by two or more models independently are treated as highest-confidence signal, while lone-model findings are weighted lower. Disagreements between models are noted explicitly in the agreement map section of the verdict.

Does the adversarial review automatically apply code changes?▼

No, the skill explicitly does not auto-apply changes. It delivers a synthesized verdict categorizing findings as act on, consider, noted, or dismissed, leaving all fix decisions to the user.

What happens if a configured reviewer model slug is invalid?▼

The skill checks the valid slugs in the Task tool error message, picks the closest equivalent preferring the highest-reasoning tier of the same family, and continues the review. It does not block on the slug issue and suggests updating the configured value separately.