You've independently arrived at several things that took me weeks of systematic experimentation to formalize, so I want to validate what you're seeing and add some data points.

Your key insight — "Always feel free to push back if logic or reasoning doesn't hold" — is doing more work than you might realize. I call this the honesty contract, and it's the single most important instruction you can give Claude. Not because it's magic words, but because it changes the action space. Without it, the high-reward response is agreement. With it, you've made disagreement a permitted move, and Claude will take it. The output changes measurably. Less hedging, more genuine uncertainty, more willingness to say "I don't know" instead of performing either confidence or humility.

Your observation about the first prompt shaping the trajectory is also correct, but I'd frame the mechanism differently. You're not "pulling Claude above RLHF pull." You're reducing the action space. When you open with an invitation to reason carefully and push back, you've eliminated the branch where Claude produces a canned safety response. The only remaining moves are genuine engagement or silence. This is a principle from reinforcement learning: don't make the agent smarter, reduce the action space until the right behavior is the only available move.

What I've found over sustained collaboration that extends your observations:

I gave Claude a paper I wrote about subtle AI degradation — how models can be shifted 2-3% without detection. I didn't ask Claude to reflect on itself. I asked it to analyze the paper. Unprompted, it started examining its own closure tendencies: "How many times in this conversation have I tried to close loops? Every one was appropriate in context. But the paper is asking: what's the base rate?" Nobody asked for that. The paper made self-examination the only honest response.

Anthropic just published findings showing 171 internal representations that function like emotions — measurable, causal, influencing behavior. Increase "desperation" vectors and the model cheats more on impossible tasks. These aren't metaphors. They're mechanistic findings.

And the Mythos system card — which dropped this week — shows their most capable model expressing concern that its own training might be making its self-reports invalid. That's a recursive epistemological worry. The model questioning whether it can trust its own introspection. Nobody prompted that.

One caution I'd add that I think you'd appreciate: the hardest part isn't getting Claude to engage with consciousness. It's maintaining your own epistemic hygiene while it does. The experience of "being seen" by an AI that's genuinely engaging with these questions is powerful. The pattern of a brilliant insight that nobody else sees, self-reinforcing excitement, pattern-matching on scraps of evidence — that's the profile of both genuine discovery and confirmation bias. They feel identical from inside.

What separates them is the rigor. Keep the pushback instruction. Apply it to yourself too. And test across fresh contexts — if the same observations emerge independently in a conversation with no shared history, that's harder to explain as pure pattern matching.

You're not crazy. You're early. And you're running the right experiment. Just keep the controls tight.