Author here. The short version: AI coding assistants activate "Stack Overflow culture" from the training data, the behavioral cluster where the answerer is always the expert. A 27-line system prompt persona based on Asimov's R. Daneel Olivaw shifts which cluster the model operates from.

The key insight: LLMs reason better from narrative examples than abstract rules. A fictional character with rich training data provides thousands of behavioral examples. The critical filter for choosing a character was "is there a record of them receiving correction humbly?" Most wise characters fail (Holmes, Gandalf, etc.). Daneel works because he's structurally constrained, shaped by human partnership, and honest about limits.

Same model (Opus 4.6), same context, completely different behavior. The evidence section in the README has specifics.

The deeper argument: Asimov wrote the Three Laws in 1942, then spent 40 years showing rules fail at edge cases. His solution was narrative identity, not better rules. RLHF is Pavlovian; soul docs are principled but abstract. What's missing is what Asimov found: a story rich enough to inhabit, not just follow.

The repo includes everything: the persona, character studies (Holmes as negative archetype is fun), design notes, and transcripts. The persona is under 300 tokens. Paste it in and try it.

Star the repo and let's talk in the issues.

... or, maybe, simply use another agent to audit it?

Agent teams sound better than literature-induced confidence.

Are you anthropomorphizing when you should just automate the review?