data-reporting

name	data-reporting
description	Produce a parametric PDF report describing a dataset — size, schema, distributions, key statistics, and findings from other skills — compiled via Typst. Use when the user wants a shareable, print-ready document about their data, not a one-off markdown summary.

Data Reporting

Generate a parametric dataset report as a Typst-compiled PDF. "Parametric" here means the report structure is fixed and reusable — you fill in the parameters (dataset path, title, sections to include, cut-offs) and the document regenerates from scratch.

Use this skill when the user wants:

• A stakeholder-ready PDF describing a dataset.
• A reproducible document that can be regenerated on data refresh.
• A consolidation of outputs from other skills (data-dictionary-creator, trend-analysis, correlation-analysis, standard-deviation, sample-size, forensic-sweep, etc.) into one artefact.

For quick markdown summaries, use the reporting step inside the relevant analysis skill instead — this skill is for when PDF is the deliverable.

Inputs

• Path to a dataset (CSV / Parquet / Excel / DuckDB table).
• Report parameters (all optional; sensible defaults applied):
  • title — report title. Default: dataset filename.
  • subtitle — e.g. snapshot date.
  • author — defaults to system user.
  • sections — which sections to include. Default: overview, schema, size, distributions, quality, findings.
  • max_columns_profiled — cap detailed profiling for very wide tables. Default: 50.
  • include_charts — boolean. Default: true.
  • theme — plain / dsr-business / personal. Default: plain.

Recommended tooling

• duckdb — all stat computation.
• uv run --with pandas --with matplotlib python -c '...' — distribution plots rendered to PNG for Typst to embed. Matplotlib only (no seaborn) to keep dependencies minimal.
• typst — compile the document.
• The typst-document-generator skills (public-doc, personal-doc, dsr-business-doc) — for themed output. When theme != plain, delegate final compilation to the matching skill.

Output layout

Create an output folder: outputs/data-reporting/<dataset-stem>-<YYYYMMDD>/:

outputs/data-reporting/sales-2026-04-23/
  report.typ          -- the Typst source
  report.pdf          -- compiled output
  assets/
    hist_price.png
    hist_quantity.png
    bar_category.png
    missingness.png
  data/
    summary_stats.csv
    schema.csv
  params.json         -- exact parameters used, for reproducibility

Procedure

Step 1 — Parse parameters

Resolve defaults. Validate that the dataset exists and is readable. Read row count and column count to calibrate the rest.

Step 2 — Compute sections (as data)

For each requested section, produce the underlying tables / values. Don't render yet — keep everything as CSV / JSON under data/ so the final compile is pure templating.

overview — dataset name, source path, row count, column count, file size, last modified, snapshot timestamp.

schema — per-column: name, inferred type, non-null count, null %, unique count, sample values. Cap to max_columns_profiled; note the cap in the report if hit.

size — delegate to sample-size skill (or inline the same computations): total n, unit of analysis (prompt the user if ambiguous), group cell sizes for any low-cardinality categorical.

distributions — for each numeric column: mean, median, SD, min, max, q25, q75, skew, kurtosis. For each categorical with ≤ 30 levels: top values with counts and %. Pull SD values from standard-deviation if already computed.

quality — null rates, duplicate row count, constant columns, suspected sentinel values. If forensic-sweep has been run, embed its headline verdict.

findings — include outputs from any prior skills the user has run (trend-analysis, correlation-analysis, multivariate-analysis, hypothesis-testing, data-enrichment). The user passes these in explicitly; don't rerun analyses.

Step 3 — Render charts (if include_charts)

For each numeric column (up to max_columns_profiled): histogram PNG at assets/hist_<col>.png. 600×400 px, dpi=120, no title (Typst adds the caption).

For each included categorical: horizontal bar chart of top 15 levels at assets/bar_<col>.png.

One overall missingness chart: bar chart of null % per column at assets/missingness.png.

Keep the matplotlib script minimal — no styling flourishes. Typst handles presentation.

Step 4 — Write report.typ

Emit a single Typst file. Structure:

#set document(title: "<title>", author: "<author>")
#set page(paper: "a4", margin: 2cm, numbering: "1 / 1")
#set text(font: "New Computer Modern", size: 10pt)
#set heading(numbering: "1.1")

#align(center)[
  #text(size: 20pt, weight: "bold")[<title>]
  #v(0.3em)
  #text(size: 12pt)[<subtitle>]
  #v(0.3em)
  #text(size: 10pt)[<author> · <snapshot-date>]
]

#outline()

= Overview
<overview prose + small table>

= Schema
#table(
  columns: (auto, auto, auto, auto, auto),
  [*Column*], [*Type*], [*Non-null*], [*Unique*], [*Sample*],
  ...rows...
)

= Sample Size
<size section>

= Distributions
== Numeric columns
<for each: summary row + histogram figure>

== Categorical columns
<for each: top-values table + bar figure>

= Data Quality
<null rates table, duplicate count, sentinel flags, forensic-sweep verdict if present>

= Findings
<embedded prior-skill outputs, one subsection each>

= Reproducibility
Generated <date> from `<dataset-path>`.
Parameters: see `params.json`.

Use #figure(image("assets/hist_price.png"), caption: [Distribution of price]) for charts. Use #table(...) for tabular data. Keep column widths readable — no tables wider than the page.

Step 5 — Compile

If theme == plain:

typst compile report.typ report.pdf

If theme is dsr-business, personal, or public: delegate to the matching typst-document-generator skill, passing the generated body. Those skills have the themed templates already wired; don't reinvent styling.

Verify the PDF exists and is non-trivially sized (> 10 KB).

Step 6 — Save parameters

Write params.json with the exact inputs (dataset path, absolute; all parameters resolved; skill version). This is what makes the report reproducible.

Step 7 — Report back

Tell the user:

• Path to the PDF.
• Path to the Typst source (for edits).
• How to regenerate (typst compile outputs/.../report.typ).
• Any sections that were skipped (e.g. "distributions skipped for 12 columns over the cap — raise max_columns_profiled to include them").

Notes

• Typst must be on PATH. If it isn't, fall back to markdown output and tell the user how to install (cargo install typst-cli or the release binary from typst.app).
• Wide tables: if a table would overflow the page width, split it by column groups or use landscape for that section only (#page(flipped: true)[ ... ]).
• Don't put raw data rows in the PDF — aggregates, samples, and schema only. Full data belongs in the source files.
• Pair with data-dictionary-creator for the schema section (use its output as-is) and sample-size / standard-deviation for the stat blocks — avoid re-implementing those computations here.
• If the user wants the report hosted or shared, don't auto-upload. Hand them the PDF path and let them move it.