An Agent Skill that changes your question instead of answering it

Ask any assistant an open-ended question — should I build this, should I take this job, how do I position this product — and you get advice that is reasonable and slightly generic. Narrow the target. Differentiate. Build an MVP. Talk to users. Test demand.

None of it is wrong. It just never moves where you are standing.

Insight Compiler is an Agent Skill I published that treats that generic answer as the starting line instead of the finish. It writes the ordinary answer down explicitly, then spends the rest of the run climbing off it.

The loop

The skill runs seven steps every time:

1. 
   Intent discovery — separates the surface question from the real goal, and names the question you haven't asked


2. 
   Ordinary-answer baseline — writes out what a normal AI would say, explicitly


3. 
   Domain lenses — looks at the problem through distant fields: military strategy, urban design, game design, religion, investment


4. 
   Discovery operators — deforms the problem: invert it, swap who acts, shift the scale, hunt for the hole nobody is looking at


5. 
   Hint bank — crosses lenses with operators into a dense list of raw material


6. 
   Strategy candidates — 2-3 directions, each traced back to the specific hints it came from


7. 
   Honest evaluation + one validation fork — ranks them without manufacturing a tie, then commits in advance to what each test outcome should change

Step 2 is the load-bearing one. An unstated ordinary answer is a ceiling the model quietly settles into; a written-down one is a floor you can push off. Step 7 is what keeps the whole thing from being a brainstorm — you end with one cheap test and a decision already made for either result.

The internal scan is wide, but the output is curated. It shows the strongest findings rather than dumping every lens it tried.

What it looks like in practice

Here is a real run. The question I gave it was my own, and it is a small one, which makes it a good demo — I published this skill and nobody was visiting the repository, so I asked what to do about it.

A plain assistant answered the question as asked: add more examples to the README, record a GIF, post it to X and Reddit, get the content ready before announcing. Solid, actionable, and it accepts every premise in the question.

The skill opened by rejecting the question:

Essential question: Not "how do I get more repo visitors," but "where does the target person first encounter enough value to feel something move in their thinking?"

Why this is non-obvious: The premise "low visits = insufficient explanation" conflates two different bottlenecks — the discovery/distribution problem and the comprehension problem.

Then, near the end, the assumption I hadn't noticed I was making:

The assumption that "GitHub repo visitor count" is the correct success metric, and that "hard to understand" is the cause of low interest, is itself questionable. For an agent skill that runs inside Claude, the person deciding whether to adopt it may never need to visit the repo at all — if they encounter a compelling output first.

Side by side:

It also graded its own three candidates honestly — flagging its most interesting one as "unverified whether target readers are actually where you post," and admitting the boring README fix was the only one executable today. The verdict: do the boring one because it is nearly free, but it is necessary, not sufficient.

The (MIT)

If you try it, I would like to know what you pointed it at.