---
title: "Schedule agents to open small cleanup pull requests for review"
description: "Encode golden principles, then let background agents scan, grade and open targeted refactors."
url: https://starsling.dev/ci-playbooks/techniques/scheduled-cleanup-prs
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/scheduled-cleanup-prs
---

# Schedule agents to open small cleanup pull requests for review

Encode golden principles, then let background agents scan, grade and open targeted refactors.

## 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.

- **[OpenAI](https://starsling.dev/ci-playbooks/openai#scheduled-cleanup-prs)**: Use it when Cleanup work piles up until it needs a dedicated day. Check before adopting: Automerge only changes small enough to review in a minute and covered by required checks.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Schedule agents to open small cleanup pull requests for review.

Encode golden principles, then let background agents scan, grade and open targeted refactors.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/scheduled-cleanup-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 OpenAI used this technique

Read what OpenAI changed and follow the original sources.

### OpenAI: Schedule agents to open small cleanup pull requests for review

OpenAI's agent-first team encodes golden principles in the repository, such as preferring shared utility packages and validating data at its boundaries. On a regular cadence, background agents scan for deviations, update quality grades and open targeted refactoring pull requests, and most of them can be reviewed in under a minute and automerged. This replaced a weekly manual cleanup.

Evidence: Company-reported result or observation

Our team used to spend every Friday (20% of the week) cleaning up “AI slop.” ([Source](https://openai.com/index/harness-engineering/))

Use it when: Cleanup work piles up until it needs a dedicated day.

Before applying it: Automerge only changes small enough to review in a minute and covered by required checks.

[Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#scheduled-cleanup-prs)

[Primary source](https://openai.com/index/harness-engineering/)

## 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