Kahneman Loop — AI skill for Claude Code

The Kahneman Loop (System 1.5)

**AI agents say "done" when it isn't.** The deploy hadn't finished, or the test the agent just ran had failed, and it tells the user it's fixed anyway. The Kahneman Loop puts what really happened in front of the agent before it replies, and hands its own failed check back once before it can say "done".

**With the Kahneman Loop, our agents made 40% fewer false claims, for about 1% more tokens and 1% more time.**

That's measured on real production traffic. On turns where a check had failed or a job wasn't done yet, replies that needed a correction fell from **25% to 15%**. The cost is averaged over every reply: the only extra work is one more model round on the 1 in 140 replies where the agent's own check failed, which comes to about 0.3 seconds per reply. It's one file with zero dependencies, and there's a drop-in Stop hook for Claude Code.

A *false claim* here is a claim that a tool result contradicts ("all tests pass" after a red test run) or that no tool result backs ("I tested it" when nothing was tested). The failure has a few names already: outcome hallucination, hallucinated success, illusory completion (see [Related work](#related-work)).

**Why "System 1.5".** Daniel Kahneman described the mind as two systems: a fast one that jumps to a conclusion from whatever is in front of it, and a slow one that can check that conclusion but rarely bothers unless something surprises it. An agent has both. What it lacks is the surprise when its own evidence says it's wrong. The Kahneman Loop is that missing piece. It does no thinking of its own: it shows the fast system the fact it was missing, and when the agent's own check failed, it calls the slow system in, once. [Backgro