Insight Compiler: Stopping Generic AI Advice
I've been using an LLM agent skill called Insight Compiler that treats the generic answer as the starting line rather than the finish. Instead of just answering the prompt, it explicitly identifies the "ordinary" response and then systematically attempts to climb away from it.
The Logic Flow
The workflow follows a strict 7-step sequence to avoid the common pitfalls of prompt engineering:
1. Intent Discovery: Separates the surface question from the actual goal.
2. Ordinary-Answer Baseline: Explicitly writes out what a standard AI would say. This is the most critical step; by making the ceiling visible, the model can push past it.
3. Domain Lenses: Analyzes the problem through unrelated fields (e.g., urban design, military strategy).
4. Discovery Operators: Inverts the problem or shifts the scale to find overlooked gaps.
5. Hint Bank: Synthesizes lenses and operators into raw material.
6. Strategy Candidates: Develops 2-3 distinct directions based on the hints.
7. Honest Evaluation: Ranks the candidates and defines a specific validation test for each.
Real-World Performance
I tested this against a standard assistant regarding a GitHub repo with zero traffic.
A standard LLM suggested the usual: improve the README, record a GIF, and post on X or Reddit. It accepted my premise that "low visits = poor explanation."
Insight Compiler rejected the premise entirely. It identified the "essential question" as where the target user first encounters value, rather than how to increase a visitor count. It pointed out that for a Claude-based agent skill, the user might never need to visit the repo if the output itself is compelling.
Comparison of outcomes:
- Ordinary Answer: Focuses on README improvements, adding demos, and growing visitor counts.
- Insight Compiler: Focuses on defining the target user, producing one high-impact example, and distributing the output as the primary content.
The result wasn't just a brainstorm; it ended with a cold evaluation. It flagged the "interesting" strategy as unverified and admitted the "boring" README fix was the only immediate action—necessary, but not sufficient for success.
For anyone building an AI workflow, this is a great example of how to use a multi-step agentic process to move from "correct but useless" to "provocative and actionable."
https://github.com/snc2work/insight-compiler/blob/main/examples/nobody-visits-my-repository.md