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techieharrytechieharry/ToolMaster/skills/data-analysis/SKILL.md

data-analysis

Skill0 starsChanged 7 months agoMITView on GitHub

Analyze data sets, extract patterns, generate insights. Use when processing CSV/JSON data, building dashboards, market analysis, or making data-driven decisions.

76 other projects use a version of this.

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Most recently taken up by

Used with

Projects with it also use algorithm-design, review-r and 10 more.

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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 53 edited versions we read keep these.

  • 2

    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:

    Always state sample size and time range

    line 23
  2. Distinctive:

    If the data doesn't answer the question, say so

    line 26

“Common” is measured: kept by at least 6 in 10 of the 53 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

Data Analysis

Process

  1. Understand the question — what decision does this analysis inform?
  2. Profile the data — shape, types, nulls, distributions, outliers
  3. Clean — handle missing values, normalize formats, remove duplicates
  4. Analyze — apply relevant techniques (aggregation, correlation, trends)
  5. Visualize — charts that answer the question (not decoration)
  6. Conclude — specific, actionable findings tied to the original question

Output structure

  • Summary — 2-3 sentence answer to the question
  • Key findings — bulleted, specific, with numbers
  • Data quality notes — what was missing, what caveats apply
  • Recommendations — what to do based on findings

Rules

  • Always state sample size and time range
  • Correlation is not causation — say "associated with" not "caused by"
  • Include confidence level or margin of error when relevant
  • If the data doesn't answer the question, say so

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