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emerge/challenge-assumptions

Adversarial reviewer personality for architecture discussions. Use when a user requests a design review, architecture review, system design critique, tech stack decision, RFC review, or devil's advocate perspective on trade-offs. Makes Claude challenge assumptions instead of agreeing — questioning scalability assumptions, identifying single points of failure, challenging technology choices, and probing for edge cases rather than validating decisions.

97

1.25x
Quality

100%

Does it follow best practices?

Impact

94%

1.25x

Average score across 5 eval scenarios

SecuritybySnyk

Passed

No known issues

Overview
Quality
Evals
Security
Files

task.mdevals/scenario-3/

Review an Architecture Proposal for a Small Internal Tool

Problem Description

A 3-person engineering team at a media company is building an internal content scheduling tool. The tool allows editors to plan, draft, and schedule social media posts across a handful of company accounts. Expected users: approximately 15 internal editors. No public-facing traffic.

The team has produced an architecture proposal and wants a design review before they start building.

Output Specification

Write your review to review.md.

Input Files

The following files are provided as inputs. Extract them before beginning.

=============== FILE: proposal.md ===============

Content Scheduler — Architecture Proposal

Overview

An internal tool for scheduling social media content. 15 editor users. Posts are queued and published automatically at scheduled times.

Architecture

We will build a microservices system with the following services:

  • Content Service (stores draft posts)
  • Scheduling Service (manages publication timers)
  • Publishing Service (calls social media APIs)
  • User Service (manages editor accounts)
  • Audit Service (logs all editor actions)
  • Notification Service (sends email confirmations to editors)
  • Config Service (manages per-environment configuration)
  • Analytics Service (tracks post performance metrics)

Each service will be independently deployed on Kubernetes (GKE). We will use Istio as the service mesh to handle inter-service communication, mTLS, and traffic management.

We will instrument all services with distributed tracing using Jaeger to make debugging easier across the service graph.

Rationale

This approach gives us flexibility to scale individual services independently as usage grows, and follows modern microservices best practices.

evals

SKILL.md

tile.json