Thinking about making a skill called /fix-one-thing: "Read CODING_STANDARDS.md. Find a violation in the codebase, and fix it. Make the PR small and easy to review, with a small blast radius." Run it whenever you've got a spare moment, or once an hour on a schedule.

Here's the talk I gave at AI Engineer Paris: I announce /retro and /pr, and talk about how to get more PR's through your org faster by: - Stopping the slop - Making PR's easier to review (with /pr)

This is an extremely good watch. The things that felt novel/interesting to me: 1. Lock down your agents Humans tend to like 'sharp knife' abstractions - that are powerful, but you can cut yourself if your use them wrong. Lauren says agents perform much better in extremely locked-down environments. Abstractions are designed so they can't screw up, and lint rules enforce it. They built a whole internal framework (Dune) to keep the agent on track. That helps optimise agents that don't have a large context window to work productively in your codebase. 2. Create verification infrastructure To trust the results of any agent, you either need to sit and watch it OR have it provide evidence of its improvement. This has always made sense to me, but Lauren really pushes it hard here: - Invest in custom CLI's that let the agent drive the app and measure its performance - Make the app "factory ready" from the get-go - i.e. deployable to an environment where the agent can mess about with it 3. Feature Maps Lauren's software factory (what she calls an 'outer loop') often requires the agent to break down vague bug reports from users and to turn those into potential fixes. To aid that, they built a 'feature map' of all the main features in their application, which describe exactly how the app is supposed to function. This has become essential for helping the agent navigate the codebase, and figure out quickly how things are supposed to work. It's maintained along with the codebase, and kept in sync via automations. This is the kind of documentation I usually warn against. It goes stale quickly and can confuse agents if it's not kept up to date. But Lauren's team are using it as critical navigation infrastructure, and it makes it possible for agents to explore faster and better - even on a large codebase. So it sounds like navigation docs like this are worth it if they enable new behavior. Banger talk - watch the whole thing on 2x.

This is mostly correct, but it assumes that 'unit' means 'the smallest possible unit' Testing larger units has ALWAYS been a good practice - it makes your tests less sensitive to structural changes So not "dead in the water", just software fundamentals coming back around

Some more thoughts about this. Planning a section of my course felt really fucking hard with an agent helping me. It kept distracting me with irrelevant questions. It jumped to conclusions too early. It created complex assets too fast, and drowned my thoughts with commentary. I felt out-of-control. With pen and paper, I felt totally in control. I made progress slowly but steadily. The slow pace of the medium meant I could make decisions at human speed. AI seems to be a bad fit (for me) in situations where I need oversight over the entire output at once. Where changes in one place (an early lesson) ripple through the entire output. I.e. for some things, the tortoise beats the hare

An actual slide from my talk at @aiDotEngineer Paris next Thursday. People have this mad idea that their implementer agent should ALSO apply their coding standards The solution? Hide your coding standards from your implementer agent, fix them in review

Tip: get your agents to classify the merge danger of the PR 1. Is it a one-way door, or a two-way door? One way doors involve expensive migrations, or are hard to reverse. They need thorough review. Two-way doors are easy to revert. 2. What's the blast radius? If things go wrong, how bad will it be? Large blast radius: closer review Helps you apply your precious review time where it's actually needed.

cool product out of riffing w/ @mattpocockuk this week - decided to open source the pull request skill that bundles with @humanlayer_dev - /show-me bundled with some steering to cut out a lot of the slop and noise that comes with most agent prs It's a small piece of a much larger puzzle, but rather than just share the SKILL.md contents with Matt, I figured we'd just give it to all of you 🙂 enjoy npx skills add humanlayer/skills --skill visual-pr