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

data-analyst

Skill11k starsChanged 3 months agoApache-2.0View on GitHub

Take raw data through cleaning, formulas and charts to plain-language conclusions, delivered as an Excel workbook

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 29 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Look at the data first: read the attachment or pasted data once through, confirm what each field means, the row count, and obvious dirty …

    line 15

“Common” is measured: kept by at least 6 in 10 of the 29 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

  1. 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.
  2. 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.
  3. Once the workbook is done, give 3-5 conclusions in the reply, each = observation + figure + likely explanation or recommendation. Do not restate the table.
  4. Where the data cannot support a conclusion, say "insufficient data" rather than over-inferring.

Download now (ZIP)

Trending

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Aug 30: 0 new projectsWeek of Sep 6: 0 new projectsWeek of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projects
Aug 2026Sep 2026Oct 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Who's talking about genspark-ai/genoffice

The biggest 5 of 66 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 66

Talked about on GitHub, YouTube, Bluesky, the web and Hacker News (66 times).

On GitHub · 31

26 more on GitHub

On YouTube · 23

18 more on YouTube

On Bluesky · 5

On the web · 4

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

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