重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
quarto-alt-text by posit-dev/skills
npx skills add https://github.com/posit-dev/skills --skill quarto-alt-text为该项目中的数据可视化生成无障碍替代文本。
参数
调用时,请分析图表并遵循以下指南生成替代文本:
与通常只能看到图像的替代文本场景不同,我们可以访问生成每个图表的代码。利用这一点来提取精确细节:
从绘图代码中:
从数据生成代码中:
从周围的叙述文字中:
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首先阅读 fig-cap。替代文本应补充而非重复它:
包含:
排除:
| 复杂度 | 句子数 | 何时使用 |
|---|---|---|
| 简单 | 2-3 | 单一几何对象,无分面,模式明显 |
| 标准 | 3-4 | 多个几何对象或颜色编码 |
| 复杂 | 4-5 | 分面、多个叠加层、细微的见解 |
散点图:
Scatter chart. [X var] along the x-axis, [Y var] along the y-axis.
[Shape: linear/curved/clustered]. [Specific pattern, e.g., "peaks when X is 25-50"].
[Any overlaid fits or annotations].
直方图:
Histogram of [variable]. [Shape: right-skewed/bimodal/normal/uniform].
[If transformed: "after [transformation], the distribution [result]"].
[Notable features: outliers, gaps, multiple modes].
条形图:
Bar chart. [Categories] along the x-axis, [measure] along the y-axis.
[Key comparison: which is highest/lowest, relative differences].
[Pattern: increasing/decreasing/grouped].
瓦片/栅格图:
Tile chart [or heatmap]. [Row variable] along the y-axis, [column variable] along the x-axis.
Color encodes [what value]. [Pattern: where values are high/low].
[If faceted: "N panels showing [what varies]"].
分面图:
Faceted [chart type] with [N] panels, one per [faceting variable].
[What's constant across panels]. [What changes/varies].
[Key comparison or insight across panels].
相关性热图:
Correlation [matrix/heatmap] of [what variables]. [Arrangement].
[Overall pattern: mostly positive/negative/mixed].
[Notable clusters or strong/weak pairs].
[If relevant: contrast with expected behavior, e.g., "unlike PCA, these are not orthogonal"].
前后对比图:
[N] [chart type]s arranged [vertically/in grid]. [Top/Left] shows [original].
[Bottom/Right] shows [transformed]. [Key difference/similarity].
[If overlay: "[color] curve shows [reference]"].
带叠加层的折线图:
[Line/Scatter] chart with overlaid [fits/curves]. [Axes].
[Number] of [lines/fits] shown: [list what each represents].
[Which fits well vs. poorly and why].
查找项目中的所有图表代码块:
# List all figure labels with file and line number
grep -n "#| label: fig-" *.qmd
# Find figures in a specific file
grep -n "#| label: fig-" numeric-splines.qmd
# Find a specific figure
grep -rn "#| label: fig-splines-predictor-outcome" *.qmd
代码上下文:
plotting_data |>
ggplot(aes(value)) +
geom_histogram(binwidth = 0.2) +
facet_grid(name~., scales = "free_y") +
geom_line(aes(x, y), data = norm_curve, color = "green4")
周围文字说明: “归一化不会使数据更接近正态分布”
图注: “归一化不会使数据更接近正态分布。绿色曲线表示单位正态分布的密度。”
良好的替代文本:
#| fig-alt: |
#| Faceted histogram with two panels stacked vertically. Top panel shows
#| original data with a bimodal distribution. Bottom panel shows the same
#| data after z-score normalization, retaining the bimodal shape. A green
#| normal distribution curve overlaid on the bottom panel clearly does not
#| match the data, demonstrating that normalization preserves distribution
#| shape rather than creating normality.
每周安装量
50
代码仓库
GitHub 星标数
205
首次出现
2026年3月6日
安全审计
安装于
opencode47
cursor45
gemini-cli44
github-copilot44
codex44
amp44
Generate accessible alt text for data visualizations in this project.
ARGUMENTS
When invoked, analyze the figure(s) and generate alt text following these guidelines:
Unlike typical alt text scenarios where you only see an image, we have access to the code that generates each chart. Use this to extract precise details:
From plotting code:
From data generation code:
From surrounding prose:
Read the fig-cap first. The alt text should complement, not duplicate it:
Include:
Exclude:
| Complexity | Sentences | When to use |
|---|---|---|
| Simple | 2-3 | Single geom, no facets, obvious pattern |
| Standard | 3-4 | Multiple geoms or color encoding |
| Complex | 4-5 | Faceted, multiple overlays, nuanced insight |
Scatter chart:
Scatter chart. [X var] along the x-axis, [Y var] along the y-axis.
[Shape: linear/curved/clustered]. [Specific pattern, e.g., "peaks when X is 25-50"].
[Any overlaid fits or annotations].
Histogram:
Histogram of [variable]. [Shape: right-skewed/bimodal/normal/uniform].
[If transformed: "after [transformation], the distribution [result]"].
[Notable features: outliers, gaps, multiple modes].
Bar chart:
Bar chart. [Categories] along the x-axis, [measure] along the y-axis.
[Key comparison: which is highest/lowest, relative differences].
[Pattern: increasing/decreasing/grouped].
Tile/raster chart:
Tile chart [or heatmap]. [Row variable] along the y-axis, [column variable] along the x-axis.
Color encodes [what value]. [Pattern: where values are high/low].
[If faceted: "N panels showing [what varies]"].
Faceted chart:
Faceted [chart type] with [N] panels, one per [faceting variable].
[What's constant across panels]. [What changes/varies].
[Key comparison or insight across panels].
Correlation heatmap:
Correlation [matrix/heatmap] of [what variables]. [Arrangement].
[Overall pattern: mostly positive/negative/mixed].
[Notable clusters or strong/weak pairs].
[If relevant: contrast with expected behavior, e.g., "unlike PCA, these are not orthogonal"].
Before/after comparison:
[N] [chart type]s arranged [vertically/in grid]. [Top/Left] shows [original].
[Bottom/Right] shows [transformed]. [Key difference/similarity].
[If overlay: "[color] curve shows [reference]"].
Line chart with overlays:
[Line/Scatter] chart with overlaid [fits/curves]. [Axes].
[Number] of [lines/fits] shown: [list what each represents].
[Which fits well vs. poorly and why].
To find all figure chunks in the project:
# List all figure labels with file and line number
grep -n "#| label: fig-" *.qmd
# Find figures in a specific file
grep -n "#| label: fig-" numeric-splines.qmd
# Find a specific figure
grep -rn "#| label: fig-splines-predictor-outcome" *.qmd
Code context:
plotting_data |>
ggplot(aes(value)) +
geom_histogram(binwidth = 0.2) +
facet_grid(name~., scales = "free_y") +
geom_line(aes(x, y), data = norm_curve, color = "green4")
Surrounding prose says: "Normalization doesn't make data more normal"
fig-cap: "Normalization doesn't make data more normal. The green curve indicates the density of the unit normal distribution."
Good alt text:
#| fig-alt: |
#| Faceted histogram with two panels stacked vertically. Top panel shows
#| original data with a bimodal distribution. Bottom panel shows the same
#| data after z-score normalization, retaining the bimodal shape. A green
#| normal distribution curve overlaid on the bottom panel clearly does not
#| match the data, demonstrating that normalization preserves distribution
#| shape rather than creating normality.
Weekly Installs
50
Repository
GitHub Stars
205
First Seen
Mar 6, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode47
cursor45
gemini-cli44
github-copilot44
codex44
amp44
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