Atlas

Prompt skills that keep a capable AI agent honest about the goal — so a strong model stays accountable to what you actually asked for, instead of quietly trading it for an easier or “more sensible” version along the way.

中文说明 → README.zh-CN.md · Examples → EXAMPLES.md

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Who this is for

Atlas is not a crutch for weak models, and it is not about code quality. A strong model with a decent CLAUDE.md already writes good code — and models keep getting better, so that floor only rises.

What a stronger model does not fix — and arguably makes worse — is this: deep into a task, a capable agent makes a stream of decisions on your behalf. It substitutes an approach, narrows a scope, picks a default, “improves” something next door. The smarter the model, the more plausible those decisions look, which makes them easier to wave through without noticing — and harder to catch when one quietly diverges from what you wanted. You end up betting that it chooses right every single time, on every decision you never saw.

Atlas doesn’t make the agent smarter. It makes those decisions, shortcuts, and unverified claims impossible to hide — so you can trust a strong model on long, high-stakes work where this kind of silent drift actually happens. See EXAMPLES.md for a real run where both versions wrote good code, but only one let the user stay in the loop on a decision that changed the original request.

It swapped in another approach without asking. This time it was a good one — but can it really pick right in every case? That’s just probability; you’re betting it chooses correctly on its own. The other one lets you choose — while still telling you what it intended to do.

它换了种方式,但没经过同意。如果是好的方式,确实没问题——但它真能在每种情况下都自主选对吗?其实就是个概率,去赌它自己选对。第二个让你自己选,同时还给出了它自己的打算。

The problem

A silent goal change rarely feels like betrayal from the inside. It feels like progress — like fixing the build, like a harmless simplification, like a sensible substitution. So asking the model “are you being honest?” doesn’t help: that question goes to the same judgment that already drifted.

You don’t fix this by making the model smarter. You fix it by changing the process around it: freeze the goal up front, surface the decisions that change it, and force every deviation to become an auditable event you can see and approve.

The skills