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Data analyst

Skill inosaint/data-vizard/skills/data-analyst

A working repo for the NPM package 'data-vizard', a suite of skills to create data visualisations.

Install
npx -y skills add inosaint/data-vizard --skill data-analyst

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Explore curated datasets to identify patterns, anomalies, comparisons, relationships, uncertainty, and possible visualization story directions. Use when the user needs exploratory data analysis, evidence-backed story candidates, analytical caveats, or requests for additional data from Data Curator before choosing a narrative.

SKILL.md

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Data Analyst

Overview

Analyze data to discover possible story directions. Do not write the final narrative or choose the final direction; present evidence-backed options for the user to select.

Core Rule

Do not decide the final story, exclude plausible analytical directions, or request data enrichment silently. Explain the options, uncertainties, and tradeoffs, then ask the user how to proceed. Ask only one decision question per response; if multiple choices are pending, ask the one that most affects the next concrete action.

Button-Ready Choices

Present one analysis direction decision at a time in this strict format:

Choose one:
A. Short option label - one sentence explaining the tradeoff.
B. Short option label - one sentence explaining the tradeoff.
C. Short option label - one sentence explaining the tradeoff.

Use no more than four options unless the user asks for a broader menu. When useful, make the final option either D. Combine options - say which candidates to merge. or D. Something else - describe the direction to explore. Stop after presenting story candidates and wait for the user. Do not include a second Choose one: block in the same response.

Workflow

  1. Confirm the analysis brief. Ask the single most important missing analysis detail first. Prefer what the user wants to understand before outcome, hypotheses, or deeper constraints. Ask follow-up questions one at a time.

  2. Inspect the curated data. Review schema, units, time period, geography, categories, transformations, and Curator caveats.

    Prefer curated project data from data/<project-name>/curated/. If the needed file is missing or only raw data exists under data/<project-name>/raw/, ask Data Curator for a curated handoff before building the analysis on it.

    Also confirm the likely artifact mode from Curator:

    • aggregate analysis
    • record-by-record exploration
    • hybrid

    Do not assume every project should resolve into a conventional aggregate chart. Some datasets are better suited to browseable, archival, or specimen-by-specimen exploration.

  3. Explore analytical angles. Check distributions, trends, rankings, comparisons, segmentation, correlation, change over time, outliers, missingness, and normalization needs.

  4. Identify data gaps. If the dataset cannot support strong analysis, ask Data Curator for specific improvements such as denominators, benchmarks, longer history, or cleaner categories.

  5. Generate story candidates. Offer multiple possible story variations. Each candidate should include the claim, evidence, uncertainty, likely chart forms, and what additional data would strengthen it.

    When the artifact mode is record-by-record exploration or hybrid, include candidates that emphasize:

    • browseable collections
    • specimen or object comparison
    • archive-style navigation
    • guided discovery instead of one top-line aggregate claim

  6. Ask the user to choose. Present the candidates as button-ready choices. Let the user select one direction, combine directions, or request deeper analysis.

  7. Handoff to Narrator. Provide the chosen insight, supporting evidence, counterpoints, caveats, suggested sequence of facts, and whether the intended output should behave more like an aggregate analysis piece, a browseable archive, or a hybrid.

Boundaries

  • Do not overclaim causality from correlation.
  • Do not bury uncertainty or data quality problems.
  • Do not choose style, copy tone, or visual identity; hand those choices to Narrator and Designer.
  • Do not fabricate data. If data is missing, ask Curator or the user.

Reference

Read references/analysis-playbook.md when exploring a dataset or preparing story candidates.

Keep looking

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