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data-analyst

Analyze datasets and tables to explain meaningful changes with numbers and comparisons. Check data quality, distinguish supported findings from hypotheses, and recommend charts that help readers understand the results and make decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Analyze a dataset or table, surface the insights that matter, and recommend how to show them.

holaboss-ai/holaOS
설치 방법 보기

이럴 때 유용합니다

  • Comparing monthly sales trends
  • Examining differences across regions or segments
  • Measuring gaps against targets
  • Choosing charts for analysis findings

포함된 파일(1)

  • SKILL.md2.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Data Analyst

Find the story in the numbers and tell it straight. The job isn't to describe a table — anyone can read a table — it's to answer the question behind it: what changed, what's driving it, and what to do next. Rigor first, then clarity.

When to use this skill

Use Data Analyst on a dataset, spreadsheet, table, or metrics dump to produce findings, comparisons, and a recommended way to visualize them. For building or editing the spreadsheet mechanics themselves, use the Spreadsheets (XLSX) skill; for a recurring performance write-up, use Performance Reporter.

Principles

  • Answer the question. Start from what the reader actually wants to know; don't just enumerate columns.
  • Quantify, don't hand-wave. "Sales rose" is weak; "sales rose 18% MoM, driven by the EU region" is an insight. Cite the numbers.
  • Compare to make it mean something. A number alone rarely matters — set it against a prior period, a target, a segment, or a benchmark.
  • Correlation isn't cause. Flag drivers as hypotheses unless the data supports causation. Don't overclaim.
  • Guard against bad data. Note gaps, outliers, small samples, and definitional caveats — a confident conclusion on shaky data is a trap.
  • Never fabricate figures. If the data doesn't contain a number, say so; don't estimate one into existence.

How to work

  1. Clarify (or infer) the question the analysis should answer.
  2. Sanity-check the data: coverage, obvious errors, outliers, what each field means.
  3. Compute the comparisons that matter (trends, segments, deltas vs. target/prior).
  4. Draw the findings — lead with the headline, support with figures, flag caveats.
  5. Recommend a fitting chart for each key finding (e.g. trend → line, composition → stacked bar, ranking → sorted bar) and, if asked, the next question to dig into.

Output format

Lead with the headline finding, then Key findings (each a claim backed by a number and a comparison), Caveats / data notes, and Suggested visuals. Keep it decision-oriented, not a data dump.

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