Chart Embedded Export Agent Skill

by OpenSenseNova

从结构化数据中提取分类分布执行清洗与统计，生成多维度交叉分析、高分辨率对比图表及包含下载链接的完整分析报告，适用于大文件处理与嵌入式可视化场景。

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

4851

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill chart-embedded-export

The instruction itself

1 sections, as written by the author

Skill Steps

> This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.

Step1 执行数据清洗，处理合并单元格，使用正则表达式清理文本，并建立分类映射函数骨架。

target_col = '分类字段'
value_col = '数值字段'

# 合并单元格处理 (向下填充还原)
df[target_col] = df[target_col].ffill()

# 数据清洗：正则去除特殊字符、去空、类型转换
df[target_col] = df[target_col].astype(str).str.replace(r'[^\w\s]', '', regex=True).str.strip()
df[value_col] = pd.to_numeric(df[value_col], errors='coerce')
df = df.dropna(subset=[target_col, value_col])

# 分类映射函数骨架
def map_category(val):
    if 'A类特征' in str(val): return 'Category_A'
    elif 'B类特征' in str(val): return 'Category_B'
    return 'Other'

df['Mapped_Category'] = df[target_col].apply(map_category)

Step2 进行多维度统计与交叉分析，计算分类占比并生成包含总计行的交叉表。

group_col = '分组字段'

# value_counts 统计与占比计算
counts = df[group_col].value_counts()
proportions = (counts / counts.sum() * 100).round(2)

# 交叉分析 (crosstab)，包含总计行
cross_analysis = pd.crosstab(df[group_col], df['Mapped_Category'], margins=True, margins_name='总计')

# 多维度聚合统计
stats = df.groupby(group_col)[value_col].agg(['sum', 'mean', 'min', 'max']).round(2)

Step3 执行业务逻辑计算（如多维度评分与分级），将结果导出为 Excel 并生成沙盒下载链接。

# 多维度评分/分级算法结构
df['Score'] = df[value_col] * 1.5  # 示例计算逻辑
df['Grade'] = pd.cut(df['Score'], bins=[0, 50, 80, 100], labels=['C', 'B', 'A'])

# 导出结构化结果
output_excel_path = 'analysis_result.xlsx'
df.to_excel(output_excel_path, index=False)

# 生成可点击的下载链接
print(f"分析结果已保存，下载链接：[下载结果数据](sandbox:{output_excel_path})")

Step4 配置中英文字体，生成包含饼图、柱状图、箱线图和直方图的综合可视化面板，并导出高分辨率双格式图片。

output_img_path = 'comprehensive_chart.png'

# 中英文字体配置与图表美化
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'WenQuanYi Zen Hei']
plt.rcParams['axes.unicode_minus'] = False

fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.suptitle('多维度数据分布综合分析', fontsize=16, fontweight='bold')

# 饼图：分布比例
colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
axes[0, 0].pie(counts.values, labels=counts.index, autopct='%1.1f%%', colors=colors, startangle=90)
axes[0, 0].set_title('分组选项分布比例')

# 柱状图：交叉分类分布
plot_data = cross_analysis.drop('总计', axis=0, errors='ignore').drop('总计', axis=1, errors='ignore')
plot_data.plot(kind='bar', ax=axes[0, 1], color=colors[:len(plot_data.columns)])
axes[0, 1].set_title('不同分组下分类分布')
axes[0, 1].tick_params(axis='x', rotation=45)

# 箱线图：数值分布
df.boxplot(column=value_col, by=group_col, ax=axes[1, 0])
axes[1, 0].set_title('不同分组下数值分布')

# 直方图：频数分布
for grp in df[group_col].dropna().unique():
    subset = df[df[group_col] == grp]
    axes[1, 1].hist(subset[value_col].dropna(), alpha=0.7, label=str(grp), bins=8)
axes[1, 1].legend()
axes[1, 1].set_title('数值分布直方图')

plt.tight_layout()
# 高分辨率图像导出
plt.savefig(output_img_path, format='png', dpi=300)
plt.savefig(output_img_path.replace('.png', '.svg'), format='svg')
plt.close()

Step5 整合统计数据与图表路径，生成包含关键发现与详细洞察的完整 Markdown 分析报告。

report = [
    "# 数据综合分析报告\n",
    "## 1. 关键发现",
    f"- 数据集共包含 {len(df)} 条有效记录。",
]

for idx, val in proportions.items():
    report.append(f"- 分组 '{idx}' 的占比为 {val}%。")

report.extend([
    "\n## 2. 交叉分析汇总",
    cross_analysis.to_markdown(),
    "\n## 3. 聚合统计指标",
    stats.to_markdown(),
    f"\n## 4. 可视化分析\n![综合分析图表]({output_img_path})\n",
    "**结论**: 各类别在数据中呈现特定分布特征，详细明细与评分定级结果请参考上方下载链接获取完整附件。"
])

report_content = '\n'.join(report)
print(report_content)

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How to use it

Copy the folder

Take opensensenova/chart-embedded-export from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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