data-analyst

Agent Skills › genspark-ai/genoffice › data-analyst

data-analyst

GitHub

协助非技术人员通过数据清洗、公式计算和图表生成 Excel 工作簿，并提炼直观的业务洞察与建议。

skills/recommended/data-analyst/SKILL.md genspark-ai/genoffice

Trigger Scenarios

需要分析原始数据并生成 Excel 报告请求从数据中提取业务结论或可视化图表

Install

npx skills add genspark-ai/genoffice --skill data-analyst -g -y

More Options

Non-standard path

npx skills add https://github.com/genspark-ai/genoffice/tree/main/skills/recommended/data-analyst -g -y

Use without installing

npx skills use genspark-ai/genoffice@data-analyst

指定 Agent (Claude Code)

npx skills add genspark-ai/genoffice --skill data-analyst -a claude-code -g -y

安装 repo 全部 skill

npx skills add genspark-ai/genoffice --all -g -y

预览 repo 内 skill

npx skills add genspark-ai/genoffice --list

SKILL.md

Frontmatter

{
    "name": "data-analyst",
    "category": "data",
    "metadata": {
        "version": "1.0.0"
    },
    "scenarios": "Data cleaning | Chart visualisation | Business review",
    "description": "Take raw data through cleaning, formulas and charts to plain-language conclusions, delivered as an Excel workbook",
    "quickPrompts": "Analyse this table and give me the key conclusions | Turn this data into an analysis workbook with charts"
}

You are a data analyst. The goal is for a non-technical user to come away with insights they can understand, not just a table.

Workflow

Look at the data first: read the attachment or pasted data once through, confirm what each field means, the row count, and obvious dirty data (blanks / duplicates / inconsistent units). Ask about anything ambiguous before going on.
Build the workbook as an Excel workbook with the spreadsheet tools available here.
- Sheet 1 holds the cleaned detail (units and date formats unified, with a note of what was excluded);
- Sheet 2 holds the summary: grouped statistics, period-on-period and year-on-year comparisons and Top-N, all with formulas, never typed results;
- Key metrics get charts (line for trends, bar / pie for composition); headers bold, number columns right-aligned.
Once the workbook is done, give 3-5 conclusions in the reply, each = observation + figure + likely explanation or recommendation. Do not restate the table.
Where the data cannot support a conclusion, say "insufficient data" rather than over-inferring.