Source-derived protocol · AI Engineer
Agent-First Code Review Workflow
Have Claude do a first-pass review of code changes, then give the reviewing agent a way to actually try the experience out like a real user — end-to-end tasks, opening the experience, checking it in a browser, running tools, and linting. Set up hooks that run on any edits, study the diffs, and drive a loop that corrects the PR as quickly as possible when a bug or gap in functionality is discovered; for UI changes destined for the browser, have the agent test the change in the browser and generate a video of the experience where it clicks around, and for mobile or desktop experiences, have the agent generate screenshots or videos of that experience. Check the agent's verification evidence yourself, and codify your team's best practice and taste in shared files such as claude.md or agents.md where the agent can consult them during code review.
8 actions
AI Engineer's principle
The workflow for verification and code review is agent-first: Claude does the first-pass review, the agent is given a way to try the experience like a real user, and the human is responsible for checking the agent's verification evidence.
Your version
8 total
Run an agent-first code review loop where Claude reviews changes first, the agent verifies the experience like a real user, and you check its evidence before the PR is corrected.
The source describes a workflow the creator's team runs; the cadence of running it and the specific shared files to write are the user's to choose. All action text is source-backed; the two parameters only fill in the schedule and the file names.
Have Claude do a first-pass review of code changes.
Recurring · on your cadenceDone when: Claude has completed a first-pass review of the code change.
Give the reviewing agent a way to actually try the experience out similar to a real user: end-to-end tasks, opening up the experience, checking it in a browser, running tools, and doing linting.
Recurring · on your cadenceDone when: The reviewing agent has a working way to try the experience out like a real user.
Set up hooks that run on any edits, study the diffs, and drive a loop that corrects the PR as quickly as possible when a bug or gap in functionality is discovered.
Setup · onceDone when: Hooks are in place that run on any edits, study the diffs, and drive a correction loop for the PR.
For UI changes destined for the browser, have the agent test the change in the browser and generate a video of the experience where it clicks around, to build confidence that it works.
Recurring · on your cadenceDone when: The agent has tested the UI change in the browser and generated a video of the experience where it clicks around.
For mobile or desktop experiences, have the agent generate screenshots or videos of that experience.
Recurring · on your cadenceDone when: The agent has generated screenshots or videos of the mobile or desktop experience.
Check the agent's verification evidence yourself — for UI work used by a user in the browser, have the agent generate videos or screenshots of the browser experience to build confidence it works.
Recurring · on your cadenceDone when: You have checked the agent's verification evidence yourself.
Codify your team's best practice and taste in shared files such as [which shared files will you codify your team's best practice and taste in] where the agent can consult them during code review.
Setup · onceDone when: Your team's best practice and taste are written down in the shared files the agent can consult during code review.
Review whether the agent-first review loop is still working for your team: is Claude's first-pass review catching issues, is the agent's verification evidence giving you confidence, and are the shared files still reflecting your team's best practice and taste?
Review · on your cadenceDone when: You have reviewed whether the loop is still working and noted any changes to make.NuthinButta scaffolding
Source-derived protocol
Based on “How Anthropic Merges 30,000 Code Changes a Day - Manish Kapur, Sonar & Addy Osmani, Anthropic” by AI Engineer.
- Source fidelity verifiedPassed · Oct 29, 2026
- Protocolability reviewedPassed · configurable template
- Editorially reviewedNot yet
- Supported by independent sourcesNot evaluated
Source fidelity means each creator-worded element was checked against the exact statements it cites. It says nothing about whether the approach works.