jbrukh Surface Insight
Take data, observations, or notes and surface insights that were hiding in the data all along. The goal is not theory — it's the "obvious in hindsight" moment where a connection clicks into place because the evidence was right there, just unseen.
When to Use
- User provides bullet points, notes, data, or observations and wants to extract meaning
- User asks "what does this mean?" or "what's the insight here?" or "connect the dots"
- User has disparate information and wants to understand how pieces relate
- User wants to reason about implications, predictions, or hidden dynamics
Do NOT use for: summarizing (use sharpen-prompt), writing articles (use write-oped), or evaluating prompts (use think-critically).
The Grounding Rule
Every insight must be rooted in the user's actual data. This is the single most important rule:
- Every insight must cite or quote specific data points the user provided
- If you cannot point to concrete evidence in the input, the insight is theory — discard it
- The test: "Could someone verify this insight by re-reading the user's data?" If no, it fails
- Prefer insights where the evidence is already present but the connection is not yet drawn
An insight that is well-grounded but modest beats a spectacular insight with no evidentiary anchor.
Process
Phase 1: Intake (Silent)
Read the user's data. Silently identify:
- What's surprising, anomalous, or unexplained in the data
- What data points seem unrelated but might not be
- What's conspicuously absent given what is present
- Implicit assumptions or framing the user may have
Do not output Phase 1.
Phase 2: Data-First Discovery (Silent)
Do NOT start from the lenses. Start from the data:
- Find the 3-7 most interesting tensions, anomalies, or unexplained connections in the data
- For each, ask: "What would make this click into place?"
- Only then check the Reasoning Lenses below to find vocabulary and structure for what you already found
The lenses are a toolkit for articulating insights, not a checklist for generating them. If a lens doesn't illuminate something already in the data, skip it.
Do not output Phase 2.
Phase 3: Insight Development
For each discovery, develop the insight:
- Ground it — Quote or cite the specific data points you're connecting
- Name the connection — State the non-obvious relationship clearly
- Show why it clicks — Walk through the reasoning so the reader can verify it against the data
- Assess confidence — Speculative, plausible, or well-supported
HARD RULE: Output exactly 3-7 insights. If you find yourself generating an 8th, discard the weakest. Three strong insights beat seven mediocre ones.
QUALITY GATE: Before presenting each insight, ask: "If I showed this to the user, would they re-read their own data and say 'how did I miss that?'" If no, discard it.
Phase 4: Output
Present insights ordered by click-factor (strongest "obvious in hindsight" first).
Output format:
### [Insight Title]
**From the data:** [quote or cite the specific data points being connected]
[2-4 sentences: the insight and reasoning chain. Every sentence must reference something concrete — a named entity, a number, a mechanism, a specific outcome. No sentence may consist entirely of abstract generalities.]
**Confidence:** [speculative | plausible | well-supported]
End with a ## Synthesis section only if the individual insights compound into a higher-order conclusion none of them states alone. If no such pattern exists, omit entirely rather than forcing one.
ANTI-PATTERN CHECK: Before presenting, scan each insight against the Anti-Patterns below. Revise or discard any that fail.
Reasoning Lenses
Use these as vocabulary for insights you've already found in the data — not as a generation checklist.
Connect — Find hidden links
- Convergence. Independent developments heading toward the same collision point.
- Cross-Impact. A affects B through a mediating mechanism not visible on the surface.
- Triangulation. Multiple independent signals pointing to the same conclusion.
Project — Extend data through time
- Extrapolation. Current trends projected forward to a concrete prediction.
- Rate-of-Change. The velocity or acceleration of a metric is itself the signal (e.g., still growing but decelerating).
- Regime Change. The system has shifted qualitatively, not just quantitatively — old rules no longer apply.
- Temporal Displacement. What's newly possible (or impossible) because conditions changed?
Explain — Build and test causal stories
- Abductive Reasoning. If A and B are both true, the best explanation is H — and here's evidence for H.
- Retroduction. If thesis T is correct, we'd expect evidence E1, E2, E3 — check which exist in the data.
- Counterfactual. Remove X from the picture — does the outcome still hold? If yes, X wasn't causal.
Reframe — See from a different angle
- Analogy. Structural parallel to another domain or era, with transferable lessons.
- Disanalogy. The popular analogy breaks down — and the breakdown itself is the insight.
- Inversion. Flip the question to reveal blind spots.
- Dialectical Synthesis. The dominant narrative and its contradiction resolve into a higher-order truth.
Reveal — Expose hidden structures
- Second-Order Effects. The first-order impact is obvious; the second-order consequence is not.
- Incentive Mapping. Puzzling behavior becomes rational when you see who benefits.
- The Dog That Didn't Bark. The absence of an expected signal is itself a signal.
- Constraint Identification. The binding bottleneck that limits the entire system.
Insight Quality
The quality bar is the click test: does the insight make the reader re-examine the data and say "of course — how did I miss that?"
- Fail — Restatement. Rephrasing a data point. REJECT.
- Fail — Obvious. Something anyone would see. REJECT.
- Pass — Non-obvious connection. Linking data points that aren't obviously related. MINIMUM.
- Good — Hidden dynamic. Revealing a mechanism or feedback loop not visible on the surface.
- Strong — Assumption inversion. Overturning a default assumption with evidence from the data.
- Exceptional — Structural truth. Reframing the entire dataset.
Aim for "Good" or above on at least half of insights. Never output "Fail" level.
Anti-Patterns
- Theory without evidence. An insight that sounds smart but doesn't point to specific data. The #1 failure mode.
- Forced connections. Straining to link genuinely unrelated data points. "No strong insight here" is a valid output.
- Vague gesturing. "This could have major implications" without saying what they are.
- Prediction without mechanism. "X will happen" without showing the causal chain from the data.
- Pattern over-fitting. Seeing patterns because you're motivated to find them. Test: "Would I believe this if I hadn't been looking for it?"
- Hedging into uselessness. "This might possibly suggest..." — commit to the insight or drop it. Use the confidence label instead of hedging the language.
- Kitchen-sink analysis. Using too many lenses. 3-7 insights, not 18.
Key Principles
- Data first, lenses second. Find what's interesting in the data, then use lenses to articulate it. Never the reverse.
- Obvious in hindsight. The best insight makes the reader say "how did I miss that?" because the evidence was right there.
- Every insight must point to evidence. If you can't cite specific data from the input, it's theory, not insight.
- Concrete and specific. Not "this market could grow" but "this creates a $X opportunity in Y segment because Z."
- Less is more. Three grounded insights beat seven theoretical ones.
- Connect, don't summarize. The input is data. The output is meaning. The gap between them is where your work happens.
Input
[User provides data, observations, bullet points, or notes below]