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[ARTICLE · art-100554] src=perrotta.dev ↗ pub= topic=developer-tools verified=true sentiment=· neutral

★ Reviewing slop pull requests

A developer at Perrotta.dev describes a two-step workflow for reviewing AI-generated pull requests using two custom skills: /meat, which converts a diff into a reading guide, and /dual-review, which runs two review passes to validate findings. The sequence is recommended for long, plausible PRs to reduce narrative noise and unsupported findings.

read1 min views12 publishedAug 17, 2026

♠ Problem statement: review an AI-written slop pull request from a teammate.

We’ll use a clanker to review slop PRs.

First, I invoke the /meat skill, turning the diff into a reading guide, inspired by

/meat <PR-URL>

It does not look for defects. It captures the diff once, follows control and data flow, treats tests as specifications, and returns this shape:

One sentence with intent and observable result.

## Read in this order
- path:line — why this contract matters

## Flow
1. input
2. transformation
3. effect

## Contracts
## Tests as specs
## Omitted

That gives me the author’s system model (the “meat” of their PR) without the PR body’s narrative.

Second, /dual-review

1 tries to break that model:

/dual-review <PR-URL> [--post]

It runs two review passes with different lenses / perspectives, then validates every candidate against source and focused probes. Reviewer agreement changes investigation priority.

To recap:

/meat

says what the change means and where to read it./dual-review

checks whether contracts survive hostile input.My default sequence for a long, plausible PR is now:

/meat <PR-URL>
/dual-review <PR-URL> --post

One tool removes narrative noise. The other removes findings without evidence.

Inspired by an internal skill created by our CISO. ↩︎

— § —

Reply via [email](mailto:serendipity@perrotta.dev?subject=Reply to: Reviewing slop pull requests)

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