feedback-synthesizer

# Feedback Synthesizer Takes raw numbers and raw comments and produces one connected picture, not two separate reports that happen to sit in the same doc. Most teams either report a score or summarize comments. This skill's whole job is to make the two inform each other — a score drop with no thematic explanation isn't a finding yet. ----- ## Step 0: What are you looking at? Identify the input type before doing anything: - **From survey-architect** (a `/research/<study-name>/01-survey-spec.json` exists) → read it for instrument, scoring formula, and expected n. Use it, don't re-derive. - **Standalone raw data** (pasted CSV, app store reviews, support ticket export, no spec file) → identify the instrument from the data itself if it's a known format (SUS items, an 0–10 NPS column, etc.), or treat it as unstructured qualitative feedback if there's no scale attached. Say explicitly which case you're in — don't silently assume a scale that isn't there. ----- ## Step 1: Quantitative scoring **Run `scripts/scoring.py` — do not compute these by hand.** Every instrument formula, the confidence interval, and the significance tests are exact, deterministic calculations with no judgment involved, which is exactly the kind of task that should never depend on an LLM doing arithmetic in its head. Call the matching function for the instrument in use: | Instrument | Function | |---|---| | SUS | `sus_score(responses)` | | UMUX | `umux_score(responses)` | | UMUX-Lite | `...