# Large File Kpi Analysis

> 根据数据量自动选择读取策略（大文件转Parquet），提取关键指标进行单位一致性验证与排序分析，并输出可下载的结果表格。

- **Type:** Skill
- **Install:** `agentstack add skill-opensensenova-sensenova-skills-kpi-metric-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [OpenSenseNova](https://agentstack.voostack.com/s/opensensenova)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [OpenSenseNova](https://github.com/OpenSenseNova)
- **Source:** https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-da-excel-workflow/capability/excel-data-analysis/kpi-metric-analysis

## Install

```sh
agentstack add skill-opensensenova-sensenova-skills-kpi-metric-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

## Skill Steps

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

Step1 提取关键指标，进行物理量/指标的单位一致性验证计算，并对核心业务指标进行降序排列。
```python
# 1. 物理量/指标单位一致性验证与计算 (保留公式结构示例)
col_numerator = 'numerator_col'  # 示例：Mx (kN·m)
col_denominator = 'denominator_col' # 示例：Wx (cm³)
col_target = 'target_col' # 示例：sigma (MPa)

if col_numerator in data.columns and col_denominator in data.columns and col_target in data.columns:
    # 单位换算示例：统一到标准单位后计算
    data['den_converted'] = data[col_denominator] * 1e-6
    data['num_converted'] = data[col_numerator] * 1e3
    data['calc_result_pa'] = data['num_converted'] / data['den_converted']
    data['calc_result_mpa'] = data['calc_result_pa'] / 1e6
    
    # 容差验证
    tolerance = 1e-6
    data['is_valid'] = abs(data['calc_result_mpa'] - data[col_target]) < tolerance
    print("单位一致性验证通过率:", data['is_valid'].mean() * 100, "%")

# 2. 提取关键指标并降序排列
group_col = 'group_col' # 示例：开发区名称
metric_col = 'metric_col' # 示例：实际到帐外资额

result_df = pd.DataFrame()
if group_col in data.columns and metric_col in data.columns:
    result_df = data[[group_col, metric_col]].copy()
    result_df = result_df.sort_values(metric_col, ascending=False).reset_index(drop=True)
```

Step2 将分析与验证结果整理为最终的数据框，保存为 Excel 文件，并生成可供下载的链接。
```python
output_path = 'analysis_result.xlsx'

# 确定最终输出的数据框
if not result_df.empty:
    result_df_final = result_df
elif 'calc_result_mpa' in data.columns:
    result_df_final = data[[col_numerator, col_denominator, col_target, 'calc_result_mpa', 'is_valid']].copy()
    result_df_final.columns = ['分子指标', '分母指标', '目标比对值', '计算结果', '是否一致']
else:
    result_df_final = data.head(100) # 默认输出前100行作为示例

# 保存为Excel文件
result_df_final.to_excel(output_path, index=False, engine='openpyxl')
print(f"分析结果已保存至: {output_path}")

# 生成下载链接
print(f"下载链接: [点击下载分析结果](./{output_path})")
```

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [OpenSenseNova](https://github.com/OpenSenseNova)
- **Source:** [OpenSenseNova/SenseNova-Skills](https://github.com/OpenSenseNova/SenseNova-Skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-opensensenova-sensenova-skills-kpi-metric-analysis
- Seller: https://agentstack.voostack.com/s/opensensenova
- Browse the marketplace: https://agentstack.voostack.com/browse

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