| name | chart-visualization |
| description | Design, critique, or specify honest charts and data visualizations from known data and audience needs. Use for chart selection, misleading-chart audits, accessibility, dashboard visuals, or "make this data easier to understand"; do not use for raw data analysis, PPT deck generation, spreadsheet surgery, or fabricated chart data. |
Chart Visualization
Purpose
Choose or review visualizations so the chart answers the user's question honestly, accessibly, and with clear data provenance.
When to Use
Use for:
- selecting chart types for a known dataset or analysis result
- auditing charts for misleading axes, bad encodings, clutter, inaccessible color, or unclear labels
- specifying dashboard/chart requirements before implementation
- improving chart titles, annotations, scales, legends, and captions
Do not use for:
- performing the underlying numeric analysis; use
data-analysis
- generating a full slide deck; use
pptx-generator
- editing an XLSX file while preserving workbook internals; use
minimax-xlsx
- inventing sample data to make a chart look good
Workflow
- Identify the chart question: comparison, trend, distribution, composition, relationship, geospatial pattern, or ranking.
- Confirm data provenance, fields, units, denominators, time window, and transformations before choosing encodings.
- Choose the simplest chart type that answers the question.
- Specify encodings: x/y, series, grouping, sorting, scale, baseline, annotations, uncertainty, and interaction if needed.
- Audit for honesty: truncated axes, dual-axis confusion, hidden denominators, misleading aggregation, overplotting, cherry-picked ranges, and omitted uncertainty.
- Audit for usability: title states the takeaway, labels are readable, color is accessible, legend is close to data, mobile/print context is considered.
Data Authenticity Rules
- Do not fabricate values, categories, units, totals, or trend direction.
- If the data is unavailable, produce a chart specification or critique checklist, not a rendered claim.
- Label derived metrics and normalized values clearly.
- Prefer annotations that explain source-backed events; mark speculative explanations
[UNVERIFIED].
Output Contract
STATUS: SPECIFIED | REVIEWED | PARTIAL | BLOCKED
QUESTION:
- <what the chart should answer>
DATA BASIS:
- <source fields, units, filters, transformations, or missing provenance>
RECOMMENDED VISUAL:
- Chart type:
- Encodings:
- Title / labels / annotations:
- Accessibility notes:
HONESTY AUDIT:
- <axis, scale, aggregation, uncertainty, denominator, or range concerns>
IMPLEMENTATION NOTES:
- <library-agnostic guidance or project-specific constraints>
UNVERIFIED:
- <missing data/provenance or N/A>
Provenance
Clean-room AILI/OpenCode adaptation inspired by the public DeerFlow chart-visualization skill pattern. No upstream skill text, runtime paths, tools, generated assets, provider assumptions, or chart templates are copied. Source family: bytedance/deer-flow, MIT License.
