Large File Kpi Analysis Agent Skill

by OpenSenseNova

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。

648 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 large-file-kpi-analysis

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 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。

# 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。

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})")

Other skills for the same job

different authors, same section of the catalogue

Protocolsio Integration

by christophacham

×4

Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.

16k tokens

Tailored Resume Generator

by frostant

×4

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances

3k tokens

Excalidraw Diagram Generator

by github

vendor ×3

Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

Build and distribute Expo development clients locally or via TestFlight

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

16k tokens

Benchling Integration

by christophacham

×3

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

24k tokens

How to use it

Copy the folder

Take opensensenova/large-file-kpi-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.