Numeric Format Normalization Agent Skill
对 Excel 数据进行数值格式标准化与清洗，支持大规模数据的 Parquet 转换流程，并完成关键指标的合计核对与结果文件导出。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill numeric-format-normalizationThe 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 对目标列进行数据清洗（去除空值、标准化数值格式），计算合计值，并与指定汇总 Sheet 中的合计行进行精确核对。
target_col = '目标数值列' # 示例：'建筑面积'
summary_sheet_name = 'Summary' # 示例汇总Sheet名
summary_item_col = '项目'
summary_value_col = '数值'
# 数据清洗：去除空值、强制转换为数值格式
df_cleaned = df_processed.dropna(subset=[target_col]).copy()
df_cleaned[target_col] = pd.to_numeric(df_cleaned[target_col], errors='coerce')
# 计算合计
total_calculated = df_cleaned[target_col].sum()
# 从指定 Sheet 中读取“合 计”行数值进行核对
try:
summary_sheet = pd.read_excel(file_path, sheet_name=summary_sheet_name)
expected_total = summary_sheet.loc[summary_sheet[summary_item_col] == '合 计', summary_value_col].values[0]
# 核对一致性 (处理浮点数精度问题)
if abs(total_calculated - expected_total) < 1e-6:
consistency = "一致"
difference = 0
else:
consistency = "不一致"
difference = abs(total_calculated - expected_total)
print(f"计算合计: {total_calculated}, 指定合计: {expected_total}, 一致性: {consistency}")
except Exception as e:
print(f"核对失败: {e}")
expected_total = None
consistency = "未知"
difference = None
Step2 将分析与核对结果保存为表格文件，并生成可供下载的文件链接。
output_path_xlsx = 'analysis_result.xlsx'
output_path_csv = 'analysis_result.csv'
# 构建结果表格
result_data = {
'统计项': ['总行数', f'{target_col}合计（计算值）', f'{target_col}合计（指定值）', '一致性', '差异值'],
'数值': [total_rows, total_calculated, expected_total, consistency, difference]
}
result_df = pd.DataFrame(result_data)
# 保存为多种格式
result_df.to_excel(output_path_xlsx, index=False)
result_df.to_csv(output_path_csv, index=False, encoding='utf-8-sig')
# 输出下载链接（在报告中展示）
print("分析结果已保存，可下载：")
print(f"- [{output_path_xlsx}](sandbox:/{output_path_xlsx})")
print(f"- [{output_path_csv}](sandbox:/{output_path_csv})")
Other skills for the same job
different authors, same section of the catalogue
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.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
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.
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.
How to use it
Take opensensenova/numeric-format-normalization 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.