data-to-document

name

data-to-document

description

Generate a polished PDF document from a dataset using Typst, with layout chosen to match the data shape (wide tables → landscape multi-page reference, narrow tables → portrait report, per-record → one-record-per-page card/profile layout, grouped → sectioned report). Supports user-selected field subsets, custom column labels, optional filtering/sorting, cover page, summary stats, and branded templates. Use when the user wants the dataset (or a slice of it) rendered as a shareable/printable document rather than a spreadsheet export.

Data to Document (Typst → PDF)

Render a dataset — or a user-defined slice of it — as a PDF via Typst, with layout driven by data shape.

When to invoke

• User asks "make a PDF of this data", "generate a report from this CSV", "print-ready document from my dataset", "turn these records into a profile document".
• User wants to share a dataset with a non-technical audience and a spreadsheet won't do.

Layouts

The skill picks a layout based on data shape (and lets the user override):

Shape	Layout	Description
Small narrow table (<10 cols, <50 rows)	Report (portrait)	Title, intro, table, footer
Wide table (>10 cols)	Landscape reference	Landscape orientation; column groups split across page blocks if needed
Many rows (>100) with logical groups	Sectioned report	Grouped by a user-chosen column; one section per group with a summary
Detailed per-record content	One-per-page profile	One record per page — label/value pairs, good for directory-style docs
Summary / dashboard	Exec summary	Page 1 key stats + top-N; appendix with full detail

Inputs to gather

1. Dataset path and format.
2. Field selection — which columns to include; default all non-PII.
3. Custom labels — display name per column (e.g. iso3166_alpha2 → "Country Code"). Provide an interactive mapping or a YAML config.
4. Filters / sort — optional WHERE and ORDER BY equivalent expressed in pandas terms.
5. Grouping — column to group by (for sectioned report).
6. Layout override — let the user force a layout if the auto-pick is wrong.
7. Template — plain (default), branded (if typst-document-generator plugin is installed), or user-supplied .typ.
8. Title / subtitle / author / date — metadata for the cover page.
9. Summary stats toggle — include min/max/mean/count per numeric column? Default yes for sectioned and exec-summary layouts.

Procedure

1. Locate and load the dataset. Profile columns (dtypes, cardinalities, null rates).

2. Suggest a layout based on shape; confirm with user.

3. Interactive field selection — list columns with defaults suggested. Let user drop columns and edit display labels. Save the selection as a side-car config data_to_document_config.yaml alongside the dataset so the same document can be regenerated later.

   title: "Customers — Q1 2026"
   subtitle: "Tier-1 accounts"
   author: "Operations"
   layout: sectioned
   group_by: country_name
   sort_by: [country_name, -revenue_numeric]
   filter: "tier == 'T1' and active == True"
   fields:
     - column: name
       label: "Customer"
       width: 30%
     - column: country_name
       label: "Country"
       width: 15%
     - column: revenue_numeric
       label: "Revenue (USD)"
       width: 15%
       format: "#,##0"
     - column: signed_at
       label: "Signed"
       format: "%Y-%m-%d"
   summary_stats: true
   template: plain
   

4. Apply filter / sort / grouping via pandas.

5. Render a Typst document:

   • Cover page (title, subtitle, author, date, record count).
   • Body per layout:
     • Report: single table, column widths from config.
     • Landscape reference: landscape page, wide table; split column groups if width still overflows.
     • Sectioned: heading per group, inline summary stats row, table per group.
     • One-per-page profile: one record per page as a label/value block, optional photo/avatar column if a URL path is supplied.
     • Exec summary: first page KPIs (counts, top-N by a measure), appendix with full rows.
   • Footer with page numbers and generation timestamp.

6. Number / date formatting — honour per-field format specifiers; default to dataset-locale-aware formatting via babel.

7. Compile:

   typst compile document.typ document.pdf --input data=<path>.json
   

   Pass the filtered dataset to Typst as a JSON input so the template stays data-agnostic.

8. Validate — check exit code, PDF page count sanity, and that the record count matches filtered rows.

9. Report output path; offer to open the PDF.

10. Update the data dictionary with a note that a document was generated from this slice (skill + date + config reference).

Preferred integration

If the typst-document-generator plugin is installed, delegate template selection to its public-doc / personal-doc / dsr-client-confidential skills for visual consistency with other documents. Otherwise emit a self-contained standalone .typ file.

Related: data-dictionary-export produces a similar PDF but specifically for the dictionary file; this skill produces a PDF of the data itself.

Dependencies

• typst CLI on PATH — install from https://typst.app/docs/.
• Python: pandas, pyyaml, optionally babel for locale-aware number/date formatting.

pip install pandas pyyaml babel

Edge cases

• Very wide tables that still overflow landscape — split into column groups across multiple table blocks within the same section, each block sharing a sticky key column (e.g. the customer name).
• Very large datasets (>thousands of rows) — warn the user; offer (a) filter / sort / top-N, (b) sampling, (c) sectioned report where each section stays short. Refuse to silently produce a 10,000-page PDF.
• PII-heavy columns — run pii-flag first and exclude flagged columns by default; require explicit opt-in to include them.
• Non-Latin scripts — ensure the Typst font covers them (Noto Sans fallback); test rendering before declaring success.
• Custom labels collision — two columns mapped to the same label → warn and refuse.
• Nested JSON values — flatten via json-restructure or stringify with a length cap before rendering; long JSON in a cell will overflow.

Safety

This skill is read-only on the source dataset and writes a new PDF alongside it. No backup policy step required unless the user asks it to overwrite an existing PDF — in which case follow CONVENTIONS.md and rename the prior output with a timestamp.