---
title: "Classify every merged pull request by the work it contains"
description: "Tag each merged change as feature, quality, maintenance or documentation work, and watch the mix as change volume grows."
url: https://starsling.dev/ci-playbooks/techniques/classify-merged-prs
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/classify-merged-prs
---

# Classify every merged pull request by the work it contains

Tag each merged change as feature, quality, maintenance or documentation work, and watch the mix as change volume grows.

## 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#classify-merged-prs)**: Use it when Agents are raising the number of pull requests, and the team wants to see whether review, testing and rollout keep pace. Check before adopting: A classification is as good as its labels. Define the categories once and apply them to every merged change.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Classify every merged pull request by the work it contains.

Tag each merged change as feature, quality, maintenance or documentation work, and watch the mix as change volume grows.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/classify-merged-prs.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: Classify every merged pull request by the work it contains

Spotify classifies every merged pull request as features, code quality and optimization, maintenance, or documentation. Total merged changes more than doubled year over year in August, quality and optimization work rose from 27% of the mix to 31%, and maintenance and configuration fell from 31% to 25%. In each monthly incident review Spotify also asks whether AI-authored code contributed directly, and whether the increased volume of change put pressure on review, testing, rollout or observability.

Evidence: Company-reported result or observation

Spotify reports that total merged changes more than doubled year over year in August, from roughly 8,100 to 17,000. ([Source](https://engineering.atspotify.com/2026/9/ai-changed-how-spotify-builds-what-we-learned-and-fixed-about-quality-at-higher-velocity))

Use it when: Agents are raising the number of pull requests, and the team wants to see whether review, testing and rollout keep pace.

Before applying it: A classification is as good as its labels. Define the categories once and apply them to every merged change.

[Read more in the Spotify playbook](https://starsling.dev/ci-playbooks/spotify#classify-merged-prs)

[Primary source](https://engineering.atspotify.com/2026/9/ai-changed-how-spotify-builds-what-we-learned-and-fixed-about-quality-at-higher-velocity)

## 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