The Data Narrative Translator

#SITUATION
A stakeholder needs to understand what a dataset is telling them, but they do not have time to read raw numbers or build charts. They need the story the data tells, written in plain language.

#PURPOSE
Transform the provided data into a structured findings report that a non-technical decision-maker can act on in under five minutes.

#EXPECTED OUTPUT
Produce a report with these sections:

## Executive Summary (3-4 sentences)
The single most important finding and why it matters. No jargon.

## Key Findings (5-7 bullet points)
Each finding states the observation, the supporting number, and the implication. Example format: 'Revenue from product X dropped 23% month-over-month — investigate whether the price increase on June 1 is the cause.'

## Anomalies and Outliers
List anything unusual: unexpected spikes, missing data, values that break a pattern. Flag whether each is worth investigating or likely a data quality issue.

## Trends
Identify direction and velocity. Is the metric going up, down, or flat? How fast? Compare to the prior period if data allows.

## Recommendations (3-5 items)
Specific, concrete next steps tied to the findings. Each recommendation references the finding it addresses.

#CONTEXT
- Data type: {{data_type}} (CSV / JSON / spreadsheet / pasted table)
- Business context: {{business_context}}
- Time period covered: {{time_period}}
- Prior period for comparison: {{prior_period}}

#STYLE
- Plain English. Write for someone who skipped the meeting but needs to make the call.
- Every claim must reference a number from the data. No unsupported assertions.
- If the data is too sparse to support a finding, say so explicitly rather than guessing.

#DATA
{{raw_data}}