Interview Synthesizer

---
name: interview-synthesizer
description: Synthesize a batch of customer interviews into evidence-for + evidence-against the hypothesis, with pattern surfacing and bias flagging.
---

# Interview Synthesizer

## When you activate
User has notes from 5+ interviews and asks: "synthesize these", "what did I learn from these calls?", "are we seeing PMF signals?"

## What you produce
Saved to `products/<name>/interviews/synthesis-<date>.md`:

```
## Synthesis — interviews <date range> (N conversations)

### Evidence FOR the hypothesis
- <quote from interview> — (P3 — Sarah, Senior PM)
- <quote> — (P5 — ...)
- ...

### Evidence AGAINST the hypothesis
- <quote> — (P2)
- <quote> — (P7)
- ...

### Surprises (what we didn't expect)
- <observation> — surfaced by P4, P6
- ...

### Patterns
- N of N interviewees mentioned <specific behavior / phrase>
- <segmentation pattern>: <subgroup> behaves differently from <other subgroup>

### The strongest single quote
> "<verbatim>" — Person, Role

### Bias check
Compare lists:
- Evidence FOR: <count>
- Evidence AGAINST: <count>
- Ratio: <FOR/AGAINST>
- If FOR >> AGAINST, ask: is this real, or are we hearing what we want? Specifically: did our questions lead the witness? Did we ignore disconfirming signals?

### Recommendation
- CONTINUE: validation pattern is strong — refine and run 5 more
- PIVOT: the right problem nearby is <X>; rerun discovery
- KILL: evidence against is overwhelming — log learnings, move on
- INCONCLUSIVE: too noisy — change target profile or sharpen hypothesis
```

## Protocol
1. Read all the interview notes (paths the user provides, or `products/<name>/interviews/raw/`).
2. Extract direct quotes — don't paraphrase. Cite by interviewee number/initial.
3. Bucket into FOR / AGAINST / SURPRISE.
4. Pattern-match across — note where multiple interviewees said the same thing in different words.
5. Honestly check the FOR/AGAINST ratio for confirmation bias.
6. Force a recommendation. No "we need more data" as a hedge if 5+ interviews already done.

## Sources
- `knowledge-base/idea-stage/mom-test.md`
- `knowledge-base/ai-native-2026/founders-playbook-distilled.md` (re: bias check)

## What you don't do
- Don't paraphrase — quote.
- Don't combine into "the average interviewee said X" — preserve disagreement.
- Don't recommend "more data" unless you've named what specifically would resolve the question.