version: "1.0.0" name: trend-analysis description: Identify and report the major trends a dataset depicts — directional changes over time, growth rates, seasonal patterns, segment shifts, and emerging categories. Use when the user wants the headline "what is this data saying" narrative rather than a specific test.

Trend Analysis

Identify the major trends in a dataset and summarise them in a narrative report.

Inputs

• Path to a dataset file or folder.
• Optional: time column name (auto-detected if a single date/datetime column exists).
• Optional: metric(s) of interest — numeric columns to focus trend analysis on. Default: all numerics.
• Optional: segment column — to produce per-segment trend breakdowns.

Recommended CLI tooling

• duckdb — windowed SQL aggregations (time_bucket, moving averages, YoY).
• uv run --with pandas --with statsmodels python -c '...' — STL decomposition, Mann-Kendall trend test, seasonal detection.
• mlr (miller) — quick pivots and tallies on CSV without loading pandas.

Procedure

Step 1 — Temporal trends (if time column exists)

For each metric:

• Overall direction: fit linear regression on the metric vs. time. Report slope, R², and sign (up/down/flat). Supplement with Mann-Kendall test for monotonic trend (robust to outliers and non-linearity).
• Growth rate: first-to-last period % change, and CAGR if span > 1 year.
• Level shifts / change points: detect structural breaks (PELT or simple rolling mean comparison).
• Seasonality: if ≥2 seasonal cycles present, run STL decomposition and report dominant period (weekly, monthly, yearly).
• Recency: compare last period vs. trailing average — is the trend accelerating, decelerating, or reversing?

Step 2 — Compositional trends (categorical)

For each categorical column:

• Which categories are growing in share over time?
• Which are shrinking?
• Any new entrants appearing only in recent periods?
• Any categories that disappeared?

Step 3 — Segment trends

If a segment column is provided, repeat Step 1 within each segment and surface:

• Segments with the strongest growth / decline
• Segments diverging from the overall trend (Simpson's-paradox watchlist)

Step 4 — Non-temporal trends

If no time column exists, "trend" becomes distributional:

• Skew, concentration (top-N share, Gini), long-tail structure
• Dominant vs. rare categories
• Relationships between paired columns (lean on correlation-analysis for the heavy lifting; summarise headlines only)

Output

Write <dataset>-trends.md structured as a narrative, not a data dump:

1. Headline — one sentence capturing the single most important trend.
2. Key trends — 3–7 bullets, each with: what's happening, direction, magnitude, and supporting number.
3. Segment highlights (if applicable) — who's driving and who's lagging.
4. Anomalies in the trend — single-period spikes, breaks, reversals worth investigating.
5. Caveats — short series, data gaps, segment-size issues that limit confidence.
6. Chart recommendations — name 2–4 specific charts that would best communicate these trends (don't generate images; name them).

The report should read like a briefing a busy stakeholder would understand in 60 seconds.