Excel Basic Statistics And Routing Agent Skill
对多Sheet Excel文件进行基础统计与，支持按条件筛选计算均值，以及从指定行区间提取数据去重求和，并生成结果文件与下载链接。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill excel-basic-statistics-and-routingThe instruction itself
1 sections, as written by the authorSkill Steps
> This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 筛选指定分组数据，将目标列转换为数值类型并计算平均值。
group_col = '班级' # 占位示例
target_group_value = '358' # 占位示例
target_cols = ['总分', '理数'] # 占位示例
if group_col not in df_analysis.columns:
raise ValueError(f"数据中缺少'{group_col}'列。")
df_analysis[group_col] = df_analysis[group_col].astype(str)
filtered_df = df_analysis[df_analysis[group_col] == target_group_value]
avg_scores = {}
for col in target_cols:
if col not in filtered_df.columns:
raise ValueError(f"数据中缺少'{col}'列。")
try:
filtered_df[col] = pd.to_numeric(filtered_df[col], errors='raise')
avg_scores[f'平均{col}'] = filtered_df[col].mean()
except Exception as e:
raise ValueError(f"列'{col}'无法转换为数值类型: {str(e)}")
output("筛选结果统计: " + str(avg_scores))
Step2 对于小文件，从特定 Sheet 的指定行区间提取目标字段，去重后计算总和。
unique_components = {}
total_power = 0
if total_rows < 10000:
target_sheet = 'Sheet2' # 占位示例
df_sheet2 = pd.read_excel(file_path, sheet_name=target_sheet)
extracted_data = []
# 提取区间1 (例如 21-28行)
for i in range(21, 29):
if i < len(df_sheet2):
row = df_sheet2.iloc[i]
component = row.iloc[0]
power = row.iloc[6]
if pd.notna(component) and pd.notna(power):
try:
extracted_data.append({'Component': component, 'Value': float(power)})
except:
pass
# 提取区间2 (例如 51-58行)
for i in range(51, 59):
if i < len(df_sheet2):
row = df_sheet2.iloc[i]
component = row.iloc[0]
power = row.iloc[1]
if pd.notna(component) and pd.notna(power):
try:
extracted_data.append({'Component': component, 'Value': float(power)})
except:
pass
# 合并并去重 (保留首次出现的值)
for item in extracted_data:
name = item['Component']
val = item['Value']
if name not in unique_components:
unique_components[name] = val
total_power = sum(unique_components.values())
Step3 将计算结果、筛选数据和统计信息保存为Excel文件，并生成本地下载链接。
import os
# 保存区间提取与汇总结果
if total_rows < 10000:
result_df = pd.DataFrame([
{'Component Name': name, 'Est. Power (kW)': power}
for name, power in unique_components.items()
])
total_row = pd.DataFrame([{'Component Name': '合计', 'Est. Power (kW)': total_power}])
result_df = pd.concat([result_df, total_row], ignore_index=True)
output_path_power = "output_power_sum.xlsx"
result_df.to_excel(output_path_power, index=False)
output(f"功率计算结果已保存。下载链接: file://{os.path.abspath(output_path_power)}")
# 保存筛选与统计结果
output_path_analysis = "output_analysis_result.xlsx"
with pd.ExcelWriter(output_path_analysis, engine='openpyxl') as writer:
filtered_df.to_excel(writer, sheet_name="筛选数据", index=False)
pd.DataFrame([avg_scores]).to_excel(writer, sheet_name="统计信息", index=False)
output(f"分析完成，结果已保存。下载链接: file://{os.path.abspath(output_path_analysis)}")
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How to use it
Take opensensenova/excel-basic-statistics-and-routing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.