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Data analysis workflow

Skill kernelshreyak/agentic-data-grokker/.codex/skills/data-analysis-workflow

Codex/Claude Code-native workspace for analyzing local data files and saving reproducible notes, SQL, and charts.

Install
npx -y skills add kernelshreyak/agentic-data-grokker --skill data-analysis-workflow

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What its author says it does

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Use this skill when the task is open-ended analysis of local files or mixed data sources in this repository, especially CSV, XLSX, PDF, DOCX, PPTX, JSON, text, HTML, XML, or folders of related evidence. It provides the default workflow, helper scripts, and output conventions for reproducible data exploration in Codex.

SKILL.md

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Data Analysis Workflow

Use this skill for exploratory analysis, document review, dataset triage, metric investigation, or mixed-source fact finding inside this repo.

Default source rule: if the user does not name a path, start from workspace/input/.

Default Workflow

  1. Inventory the input files.
  2. Run scripts/file_profile.py on the likely inputs.
  3. Split the work by source type:
    • Tabular: CSV, TSV, XLSX, JSON, parquet-like exports
    • Document: PDF, DOCX, PPTX, TXT, MD, HTML, XML
    • Mixed folders: create a short source map first
  4. Create reproducible analysis:
    • SQL in workspace/sql/
    • Notes in workspace/notes/
    • Charts and exports in workspace/artifacts/
  5. End with findings, caveats, and suggested next drills.

Scripts

  • scripts/file_profile.py Use first for profiling a file or folder. It prints a compact summary of structure, schema, sample content, and likely analysis directions.
  • scripts/query_tabular.py Use for SQL over one or more tabular files. It registers files as DuckDB tables and returns markdown, CSV, or JSON output.
  • scripts/chart_from_query.py Use when a chart should be produced from a repeatable SQL query rather than one-off notebook code.

Source-Type Guidance

  • For CSV/XLSX/JSON tabular data, prefer SQL or pandas over manual reading.
  • For PDFs and DOCX files, extract text first, then summarize sections and search for anchors before making claims.
  • For PPTX files, treat slide titles and bullets as the first-pass structure.
  • For unknown formats, inspect metadata and fall back to raw text or binary summary rather than guessing.

Reporting Standard

Every substantial analysis should leave behind at least one durable artifact:

  • a note with the question, method, caveats, and findings
  • a SQL file or Python snippet if the analysis is likely to be reused
  • a chart or export when it materially helps interpretation

References

Read these only when needed:

  • references/workflow-recipes.md for concrete investigation patterns
  • references/output-conventions.md for where to store results and how to name them

Gives 0 of the 12 instructions most automation workflows skills give

Counted across 745 of the 1,008 authors here whose files we hold, read 2026-08-06

  • write conventional commit messagesin 36 of 745, across 35 files
  • delete branches after mergein 30 of 745, across 21 files
  • make atomic commitsin 25 of 745, across 15 files
  • write minimal code to pass testsin 22 of 745, across 10 files
  • run tests before committingin 21 of 745, across 13 files
  • re-snapshot after navigation or DOM changesin 21 of 745, across 13 files
  • use try-catch for error handlingin 20 of 745, across 6 files
  • write tests before implementationin 20 of 745, across 8 files
  • configure branch protection rulesin 19 of 745, across 5 files
  • explain the why in commit messagesin 19 of 745, across 9 files
  • refactor code while tests remain greenin 19 of 745, across 6 files
  • Interact with elements using refsin 19 of 745, across 11 files

Said here and by no other author read

  • inventory the input files first
  • profile likely inputs with file_profile.py
  • split work by source type
  • create reproducible analysis artifacts
  • prefer SQL or pandas for tabular data
  • extract text before summarizing documents

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

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