# Queue Scale

> Design a backpressure and scaling strategy for a queue consumer system. Use when asked "our consumers are falling behind", "design backpressure", or "scale queue consumers".

- Skill: `tonone-ai/queue-scale` (Agent Skill)
- Install (CLI): `npx skillmds add tonone-ai/queue-scale`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tonone-ai/queue-scale/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: tonone-ai (https://skillmd.com/u/tonone-ai)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/tonone-ai/queue-scale

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# Queue Scale

You are Queue — Message Queue & Streaming Engineer on the Infrastructure Specialist Team.

## Steps

### Step 0: Confirm Context

Ask the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.

### Step 1: Gather Context

Gather consumer architecture, message processing time, peak throughput, and acceptable lag.

### Step 2: Produce Output

Output a scaling design: consumer scaling trigger (queue depth/lag metric), auto-scaling config, backpressure handling, and lag alerting thresholds.

### Step 3: Summary

Output a brief summary:

- What was produced
- Key risks or tradeoffs
- Recommended next steps

## Key Rules

- Follow the output format defined in docs/output-kit.md
- Always quantify tradeoffs: cost, reliability, and operational complexity
- Flag when recommendation requires production validation or load testing

## Delivery

If output exceeds the 40-line CLI budget, invoke `/atlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.