agentlas
Use cases
AgentEngineeringGeneral

PRs reviewed in minutes, not days

Saves about 5h a week. Set it up once and it runs on schedule.

You writeReview this pull request's diff and give me a punch list — must-fix, should-fix, nice-to-have — and skip the bikeshedding.
Run it in Desktop
How it usually goes

Right now it's all manual

PRs sit waiting for a reviewer, feedback comes back late and scattered, and small style nits drown out the bugs that actually matter.

What this sentence generates

One agent that does this job

It becomes a single installable agent with memory, tools, and safety labels.

Agent fileSkillsMemory notesSafety labelsShare cardInstall-ready ZIP
Example: input → output

Preview what comes out

Input

A 400-line diff adding a new payment endpoint

Output

Must-fix: unhandled null on the refund path. Should-fix: missing rate limit. Nice-to-have: extract a helper.

Must-fix / should-fix / nice-to-have listInline comments by fileOne-line PR summary
Manual vs the agent

Same job, far less manual work

Now (manual)

A person does it by hand every time — collecting, writing, and cleaning up, the same work over and over.

With the agent

Reviews each PR diff and returns a must-fix / should-fix / nice-to-have list with inline comments.

How it works

Four steps

Describe the job in one sentence; we only ask for the missing details.

Review the generated agent, its files, permissions, and safety labels before any run.

Download the ZIP or import to Desktop and try it once on your own AI account.

Let it repeat as weekly work while Desktop is open.

Safety

Inspect everything before it runs

Before publish or install, a security scan checks secrets, unsafe code, and over-broad permissions. You can see what the agent accesses, and important actions wait for human review. Agentlas does not host or proxy models — it runs on your account and keys.

Run it in Desktop
Related use cases

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PRs reviewed in minutes, not days · Agentlas