metrics-analyzer

name	metrics-analyzer
description	Analyze product data from a CSV file or pasted data. Finds trends, anomalies, segments, and actionable insights. Ask questions about your data in plain English.
argument-hint	["path to CSV or paste data"]
allowed-tools	Read, Write, Bash

Metrics Analyzer

Turn raw product data into insights. No dashboards — just clear answers to "what happened, why, and what should we do about it."

Process

1. Load the data. Read the CSV file or parse pasted data. Identify columns, data types, date ranges, and row count.
2. Understand structure. Detect date columns, metric columns (numeric), and dimension columns (categorical). Identify the grain (daily, weekly, per-user, per-transaction).
3. Compute summary statistics. For each key metric: mean, median, min, max, standard deviation, and recent trend direction.
4. Detect trends. Compare recent period vs. prior period. Calculate week-over-week and month-over-month changes. Flag anything moving more than 10% as notable.
5. Flag anomalies. Identify data points more than 2 standard deviations from the mean. Check for sudden drops, spikes, or flatlines.
6. Segment analysis. If dimension columns exist, break metrics by segment. Find which segments are driving overall changes.
7. Generate insights. Synthesize findings into 3 actionable insights ranked by business impact.

Output Format

Data Summary

• Rows: [N] | Columns: [N] | Date range: [start] to [end]
• Grain: [daily/weekly/per-user/etc.]
• Key metrics found: [list]
• Dimensions found: [list]

Key Metrics Overview

Metric	Current	Prior Period	Change	Trend
[metric]	[value]	[value]	[+/-X%]	[up/down/stable]

Trends

• [Metric 1]: [Direction] [magnitude] over [time period]. [One sentence context.]
• [Metric 2]: [Direction] [magnitude] over [time period]. [One sentence context.]

Anomalies

• [Date/period]: [What happened] — [metric] was [value] vs. expected [value]. Possible cause: [hypothesis].

Segment Breakdown

Segment	[Metric]	vs. Average	Notable
[segment]	[value]	[+/-X%]	[yes/no + why]

Top 3 Insights

1. [Insight title]: [What the data shows] — [Why it matters] — [Suggested action]
2. [Insight title]: [What the data shows] — [Why it matters] — [Suggested action]
3. [Insight title]: [What the data shows] — [Why it matters] — [Suggested action]

Recommended Next Analysis

• [What to dig into next and why]

Rules

• Always start by showing the data summary so the user can confirm the data loaded correctly.
• Use Bash with Python (pandas) for any computation. Do not eyeball numbers — calculate them.
• Trends must include magnitude ("up 12%"), not just direction ("going up").
• Every anomaly must include a hypothesis for the cause, even if speculative. Label speculative causes clearly.
• Insights must be actionable. "Revenue is up" is an observation. "Revenue is up 15% driven by Segment X — double down on acquisition there" is an insight.
• If the data is messy (missing values, inconsistent formats), clean it and report what you did.
• If the data is too small for meaningful analysis (under 30 data points), say so explicitly.
• Do not generate charts — describe what a chart would show in words. The user can visualize later.
• If the user asks a specific question about the data, answer that question FIRST, then provide the broader analysis.
• Save the analysis to a file in the current working directory as metrics-analysis-[date].md.