---
title: "Fold a duplicate image workflow into the shared build"
description: "Build the deployer image as a stage of the shared Dockerfile and delete the separate workflow that rebuilt it."
url: https://starsling.dev/ci-playbooks/techniques/drop-duplicate-image-workflow
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/drop-duplicate-image-workflow
---

# Fold a duplicate image workflow into the shared build

Build the deployer image as a stage of the shared Dockerfile and delete the separate workflow that rebuilt it.

## How to choose an approach

This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it.

- **[Spotify](https://starsling.dev/ci-playbooks/spotify#drop-duplicate-image-workflow)**: Use it when Two workflows build overlapping images or artifacts from the same sources on the same events. Check before adopting: The shared build now owns the image, so its checks must cover what the deleted workflow checked.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Fold a duplicate image workflow into the shared build.

Build the deployer image as a stage of the shared Dockerfile and delete the separate workflow that rebuilt it.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/drop-duplicate-image-workflow.md

Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence.
Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured.
Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo.
Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES.
Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch.
Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge.
````

## How Spotify used this technique

Read what Spotify changed and follow the original sources.

### Spotify: Fold a duplicate image workflow into the shared build

A separate workflow rebuilt Confidence's deployer image from its own Dockerfile on pushes to main and on pull requests, except those that only changed wasm, wasm-msg or data files. The same change moves the image into the shared Dockerfile as a stage built on the resolver's build stage, and deletes the separate workflow and its Dockerfile.

Evidence: Source PR/commit evidence

Use it when: Two workflows build overlapping images or artifacts from the same sources on the same events.

Before applying it: The shared build now owns the image, so its checks must cover what the deleted workflow checked.

[Read more in the Spotify playbook](https://starsling.dev/ci-playbooks/spotify#drop-duplicate-image-workflow)

[Primary source](https://github.com/spotify/confidence-resolver/pull/132)

## CI optimization is continuous work.

Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on.

[Get self-improving CI](https://starsling.dev/products/runners)

## Company Playbooks

### [Anthropic](https://starsling.dev/ci-playbooks/anthropic)

Run only affected tests

### [DoorDash](https://starsling.dev/ci-playbooks/doordash)

Run build and test in parallel, sharded across CI agents

### [Linear](https://starsling.dev/ci-playbooks/linear)

Benchmark the same job on faster hardware

### [Mastra](https://starsling.dev/ci-playbooks/mastra)

StarSling runners for the slowest test workflow

### [OpenAI](https://starsling.dev/ci-playbooks/openai)

Rank CI fixes by the minutes they save

### [Ramp](https://starsling.dev/ci-playbooks/ramp)

Move a CPU-bound test suite to a faster CPU

### [Shopify](https://starsling.dev/ci-playbooks/shopify)

Let an agent iterate against one CI metric

### [Spotify](https://starsling.dev/ci-playbooks/spotify)

Build arm64 on native arm64 runners

### [Stripe](https://starsling.dev/ci-playbooks/stripe)

Run only the tests a change can reach

### [Uber](https://starsling.dev/ci-playbooks/uber)

Build the merge-queue speculations most likely to be needed

## Related guides

Implementation guides for this technique.

### [Build once and reuse it across GitHub Actions jobs](https://starsling.dev/github-actions/optimizations/avoid-duplicate-compilation)

Stop rebuilding the same commit twice: drop build steps from lint and typecheck jobs, and share one build across workflows with needs and artifacts.