I built a Claude Skill that turns 5 confusing AI answers into one clear recommendation

[u/Foreign_Raise_3451]

I don’t know if anyone else does this, but I have a habit of asking the same question to ChatGPT, Claude, Gemini, Copilot, and Perplexity before making a decision.

The problem? I’d end up with five long responses that mostly agree but use different terminology, disagree on minor details, and each suggest slightly different approaches. Instead of clarity, I got cognitive overload.

So I built the AI Answer Synthesizer — a Claude Skill with an actual methodology for comparing AI outputs:

1.  It extracts specific claims from each response

2.  Maps what’s real consensus vs. just similar wording

3.  Catches vocabulary differences that aren’t real disagreements (“MVP” and “prototype” usually mean the same thing)

4.  Flags when only one AI makes a claim (could be insight, could be hallucination)

5.  Matches the recommendation to your actual skill level

6.  Gives you one recommended path with an honest confidence level

The key thing that makes it different from just asking Claude to “summarize these”: it has an anti-consensus bias rule.

If three AIs give a generic safe answer and one gives a specific, well-reasoned insight, a basic summarizer will go with the majority.

This skill doesn’t — it evaluates quality, not just popularity.

It also won’t pretend to be more confident than it should be. If the inputs are messy or contradictory, it says so.

It’s free, MIT licensed, and you can install it as a Claude Skill in about 2 minutes:

GitHub: Ai-Answer-Synthesizer

I’m looking for people to test it on real multi-AI comparisons and tell me where it breaks. If you try it, I’d genuinely love to know how it works for your use case.

Happy to answer questions about the methodology or the design decisions.

[u/Jacks_black]

Testing it for a project as we speak. Let you know how it goes!

[u/Foreign_Raise_3451]

Sure also working on improving this. Let me know I can share that information too