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dataviz-critique
Critique charts with Fung's trifecta, then suggest 2-3 stronger visualization alternatives.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Critique charts with Fung's trifecta, then suggest 2-3 stronger visualization alternatives.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Generate fresh, visualisable data questions from raw datasets; reject stale prompts before charting.
Generate fresh, visualisable analysis questions from a raw tabular dataset. Use when Codex is given a CSV/XLSX/Parquet/database extract and asked what to ask, what to explore, what charts to make, what visualisation workshop prompts to use, or what data stories might be interesting; especially for Karthik-style exploratory analysis where obvious/stale questions should be filtered out before charting.
Orchestrate dataset-to-visual-story work: plan analysis, run it, choose visuals, style, critique, and iterate.
End-to-end analytical data visualization workflow for Karthik. Use when the user points Codex to a dataset and gives a loose exploratory question, possible hypothesis, story idea, or desired audience, and wants Codex to plan the analysis, run the analysis, find the defensible story, choose the best visual representation, make chart outputs in Karthik's design aesthetic, critique the result, and iterate until the visual story is good enough to use.
Choose charts for data stories, including S-curves, knee-bends, inflections, local peaks, and misleading/decorative forms.
Choose the right visualization for a dataset plus analytical question, hypothesis, data story, or management problem. Use when recommending, designing, critiquing, or implementing chart choices before plotting; especially for Karthik-style explanatory analytics, Mint-style data stories, time-series shape annotation (knee-bends, inflection points, local maxima/minima, temporary peaks), S-curves/adoption/diffusion patterns, Babbage/management decks, election/sports/payment/geography/risk visuals, or choosing between lines, bars, scatter, maps, distributions, small multiples, scorecards, waterfalls, and tables.
| name | dataviz-critique |
| description | Critique charts with Fung's trifecta, then suggest 2-3 stronger visualization alternatives. |
Use this when reviewing an existing chart, dashboard, infographic, AI-generated visualization, or slide visual.
Core job: diagnose whether the visual makes the right thing easy to see, hard to misread, and worth seeing; then propose 2-3 better visualization alternatives.
Identify chart type, encodings, apparent question, apparent claim, likely viewer interpretation, and assumptions from missing context.
Use Kaiser Fung's question-data-visual triangle:
Check pairwise fit:
Apply these standards:
Fix fatal data/question issues before style issues.
After critique, propose 2-3 alternatives when useful. Do not list random chart types; each option must have a distinct purpose.
Default set:
For each option: best when, chart, encoding, what it fixes/reveals, tradeoff. If only one redesign is defensible, give one strong option rather than padding.
Default structure:
## Quick read
- What it is: ...
- What it seems to say: ...
- Verdict: works / partly works / fails, because ...
## Trifecta checkup
- Question: ...
- Data: ...
- Visual: ...
- Main mismatch: ...
## Issues to fix
1. **[Fatal/Major/Minor] Issue** — impact. Fix: ...
2. ...
3. ...
## Recommended alternatives
### Option A — Minimal repair
- Best when: ...
- Chart: ...
- Encoding: ...
- What it fixes: ...
- Tradeoff: ...
### Option B — Better analytical redesign
- Best when: ...
- Chart: ...
- Encoding: ...
- What it fixes/reveals: ...
- Tradeoff: ...
### Option C — Different story lens
- Best when: ...
- Chart: ...
- Encoding: ...
- What it reveals: ...
- Tradeoff: ...
## Implementation notes
- Title/annotation: ...
- Caveats/checks: ...
For quick asks: verdict, top 3 fixes, and 2 redesign alternatives.