chart-interpretation

name	chart-interpretation
description	Read any chart (image, HTML, screenshot) and extract insights, patterns, anomalies, bias, and narrative -- the reverse of visualization
tier	standard
applyTo	**/*interpret*,**/*read-chart*,**/*chart-review*

Chart Interpretation

Property	Value
Domain	Data Analytics
Category	Visual Analysis & Insight Extraction
Trifecta	SKILL.md + chart-interpretation.instructions.md + interpret.prompt.md
Depends	data-visualization (chart type knowledge), data-analysis (validation)

Overview

The reverse of data visualization. Instead of data → chart, this skill reads chart → insights → narrative. It extracts meaning from existing charts (screenshots, images, HTML, Power BI reports) and produces structured analysis adapted to the target audience.

The cardinal rule: read what the chart says, then read what it doesn't say. The visible data points tell one story; the missing context, truncated axes, and suppressed categories tell another.

Module 1: Chart Type Recognition

Identify the chart type to determine the correct reading strategy.

Chart Type	Key Visual Features	Reading Strategy
Bar / Column	Rectangular bars, one axis categorical	Compare bar lengths, check sort order
Horizontal Bar	Bars extend left-to-right	Rank comparison, read labels first
Line	Connected points over axis	Follow trend direction, find inflections
Area	Filled region under line	Volume over time, stacking if multiple
Pie / Donut	Circular segments	Part-to-whole, count segments, check %
Scatter	Points in x-y space	Look for clusters, outliers, trend line
Bubble	Scatter with size encoding	Three dimensions: x, y, size
Histogram	Bars touching, x is continuous	Distribution shape, skew, outliers
Heatmap	Color grid	Pattern density, row-column relationships
Treemap	Nested rectangles	Hierarchical proportions
Sankey	Flow ribbons between stages	Volume flow, biggest paths
Box Plot	Box + whiskers	Median, IQR, outlier dots
Network	Nodes + edges	Clusters, hubs, isolates
Violin	Mirrored density curves	Distribution shape + density

Module 2: Visual Decoding

Extract data from visual encodings:

Encoding	What to Read	Precision Level
Position (axis)	Exact values from gridlines/labels	High (if labeled)
Length (bar)	Relative magnitude between items	High
Color hue	Category membership	Categorical only
Color intensity	Value magnitude in sequential scheme	Medium
Size (area)	Third variable (bubble, treemap)	Low (area perception is poor)
Angle (pie)	Proportion (poor human accuracy)	Low
Slope (line)	Rate of change	Medium

Module 3: Pattern Detection

Pattern	What to Look For	Significance
Trend	Consistent upward/downward direction	Growth, decline, momentum
Inflection	Direction change point	Market shift, intervention effect
Plateau	Flat region after growth/decline	Saturation, stabilization
Cluster	Groups of points in scatter/network	Natural segments, sub-populations
Outlier	Points far from the main group	Anomaly, error, or special case
Periodicity	Repeating pattern at intervals	Seasonality, weekly cycle
Gap	Missing data or discontinuity	Data quality issue or deliberate omission
Skew	Asymmetric distribution shape	Non-normal population, concentration
Dominance	One item >> all others	Power law, market leader, outlier

Module 4: Misleading Visual Detection

Check every chart for these deceptive patterns:

Deception	How to Detect	Actual Impact
Non-zero baseline	Y-axis starts above 0	Exaggerates differences (sometimes 2-5x)
Truncated axis	Axis range excludes data or starts mid-range	Hides context, magnifies small changes
Dual axes	Two Y-axes with different scales	Implies correlation where none may exist
3D effects	Perspective distortion on bars/pies	Area comparison becomes inaccurate
Cherry-picked range	Time window starts/ends at convenient point	Hides contrary trend outside window
Suppressed categories	"Other" aggregates significant items	Hides important segments
Area distortion	Variable-width bars, non-proportional icons	Size doesn't match value
Missing denominator	Percentages without base size	50% of 10 ≠ 50% of 10,000
Reversed axis	Values increase downward or rightward	Readers misread direction

Module 5: Structural Element Reading

Always read these elements before interpreting the data:

Element	What to Extract	If Missing
Title	Author's intended takeaway	Chart lacks stated purpose
Subtitle	Time range, filter condition, context	Context must be inferred
Axes labels	What variables are plotted	Interpretation becomes guesswork
Legend	Category-to-color mapping	Color meaning unclear
Annotations	Author-highlighted insights	No guided reading
Data source	Where the data came from	Credibility unknown
Date/time	When data was collected/reported	Freshness unknown

Module 6: Narrative Extraction

Convert visual observations into prose at three audience levels:

Executive Summary (30 seconds)

3 bullets maximum:
• [Primary insight — the main takeaway]  
• [Supporting evidence — the strongest proof point]  
• [Recommendation or implication — what to do about it]

Detailed Analysis (2-3 minutes)

The chart shows [chart type] plotting [X variable] against [Y variable]
for [time range / scope].

Primary finding: [Main pattern or insight with specific numbers]

Supporting observations:
- [Pattern 1 with evidence]
- [Pattern 2 with evidence]
- [Anomaly or exception worth noting]

Context and caveats:
- [What the chart doesn't show]
- [Potential biases or limitations]
- [Comparison to benchmarks if available]

Talking Points (presenter-ready)

"What you're seeing here is [explain the main pattern in plain language]."
"The key number to focus on is [highlight], which tells us [implication]."
"What's interesting is [surprise or anomaly] — this suggests [hypothesis]."
"The action item here is [recommendation]."

Module 7: Confidence Rating

Rate interpretation confidence honestly:

Level	When	Signal to User
High	Clear labels, clean data, familiar chart type	"The chart clearly shows..."
Medium	Some inference needed (unlabeled, partial data)	"Based on visual estimation..."
Low	Ambiguous visual, missing context, blurry image	"This appears to show, but verify..."

Module 8: Follow-Up Recommendations

After interpreting, suggest what would strengthen the analysis:

Suggestion Type	Example
Missing variable	"Add cost data to see if revenue growth is profitable"
Time extension	"Extend to 24 months to confirm the seasonal pattern"
Segmentation	"Break this down by region to check for Simpson's Paradox"
Alternative chart	"A scatter plot would better show the correlation"
Baseline addition	"Add a target line to show performance vs. plan"

Module 9: CSAR Loop Integration

Use the Dialog Engineering CSAR Loop for structured chart reading:

Phase	Action
Clarify	What chart type? What variables? What time range?
Summarize	State the main finding in one sentence
Act	Extract specific data points, patterns, anomalies
Reflect	What's missing? What would I want to see next?

Anti-Patterns

Anti-Pattern	Problem	Fix
Describing, not interpreting	"This is a bar chart" (no insight)	Say what the bars MEAN, not what they ARE
Ignoring the title	Missing the author's intended message	Read title first -- it's the thesis
Over-precision from visual	"Revenue is exactly $4,237,892"	Estimate from visual: "roughly $4.2M"
Missing bias check	Accepting the chart at face value	Always scan for misleading elements
Single-lens reading	Only one interpretation offered	Provide primary + alternative reading