review-loop

Runs adversarial multi-agent review loops on worker subagent outputs before shipping.

Updated Aug 1, 2026
One-click install
npx skills add https://github.com/tonytino/aubreyshungry --skill review-loop-tonytino
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: review-loop
Source: https://github.com/tonytino/aubreyshungry/tree/main/.claude/skills/review-loop
Command: npx skills add https://github.com/tonytino/aubreyshungry --skill review-loop-tonytino

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? When orchestrating multi-agent work, worker subagent outputs can ship with undetected errors, safety violations, or scope creep. This Skill enforces an independent adversarial review of every worker output before it ships, with a hard cap of 2 review rounds. ## Core Features & Use Cases - Adversarial Review Loop: Dispatches a fresh Reviewer subagent with an adversarial mandate to probe worker output for dietary safety, correctness, security, repo rule compliance, copyright, test honesty, and documentation drift. - Structured Verdict Schema: Reviewers return JSON findings with severity levels (blocker/major/minor), verdicts (CONFIRMED/PLAUSIBLE/REFUTED), and an overall SHIP or CHANGES_REQUESTED decision. - Two Execution Paths: Batch work delegates to the deterministic .claude/workflows/adversarial-review.mjs workflow; single interactive tasks run the loop manually via Agent-tool calls. - Use Case: You orchestrate three worker subagents drafting meal-plan content. The Skill routes each output through a fresh adversarial Reviewer that flags any dietary-safety violation as an automatic blocker, then sends confirmed findings back to the original Worker to fix or rebut. ## Quick Start Ask the orchestrator to run the adversarial review loop on the worker subagent's output before shipping it.

Frequently Asked Questions about review-loop

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

FAQPage Schema
How do I review subagent output in a multi-agent workflow?

Dispatch the worker with an explicit spec and acceptance criteria, then spawn a fresh adversarial Reviewer subagent to probe the output. On CHANGES_REQUESTED, send the original Worker back to fix or rebut each finding, then re-review with another fresh Reviewer.

What is an adversarial code review loop for AI agents?

It is a review pattern where a fresh Reviewer subagent with an adversarial mandate tries to break a worker's output, checking correctness, security, safety rules, and scope. Findings are returned in a structured JSON verdict schema with SHIP or CHANGES_REQUESTED outcomes.

When should I use the batch workflow versus the manual review loop?

Use the .claude/workflows/adversarial-review.mjs workflow whenever there is more than one worker output, since it enforces the 2-round cap deterministically in code. Use the manual Agent-tool loop only for a single interactive task.

How many review rounds does the loop allow before escalating?

The loop is capped at 2 review rounds. After the cap, the orchestrator stops looping, makes the final ship decision, and escalates any unresolved findings to the human in the PR description.

What does the reviewer verdict schema contain?

The schema is a JSON object with a findings array (severity, area, summary, verdict, required_change per finding), an overall verdict of SHIP or CHANGES_REQUESTED, and notes. Severities are blocker, major, or minor; finding verdicts are CONFIRMED, PLAUSIBLE, or REFUTED.