Format entire codebase with Prettier (mechanical, no behavior change)

docs/architecture/06-type-inference.md

@@ -13,7 +13,7 @@ flow, the detail panel) calls into this module; nothing here reaches back out.
 
 ## 1. Why infer types at all
 
-A dataset is just rows of values. The UI wants to *describe* it without
+A dataset is just rows of values. The UI wants to _describe_ it without
 re-parsing the payload every time: how many rows and columns, what the columns
 are called, and roughly what each column contains. The inferred type drives the
 small type indicator next to each column name in the dataset detail panel and
@@ -50,7 +50,7 @@ Given the values of a single column, decide its type.
    entirely empty, or there are zero rows), default to `string`. There is no
    evidence for any other type.
 3. **Run the type checks in precedence order.** For each candidate type, ask:
-   *does **every** surviving value match this type?* The first candidate for
+   _does **every** surviving value match this type?_ The first candidate for
    which the answer is yes wins. This is the **"all values match → that type,
    else fall back"** rule: one stray value that doesn't fit knocks the column
    down to the next candidate, and ultimately to `string`.
@@ -62,7 +62,7 @@ overlap, and we want the most specific interpretation that fits.
 
 1. **boolean** first. The strings `"true"`/`"false"` are not numbers and not
    dates, so booleans never collide with the other checks — but putting them
-   first keeps a `0`/`1`-free true/false column out of `string`. (We do *not*
+   first keeps a `0`/`1`-free true/false column out of `string`. (We do _not_
    treat `0`/`1` as boolean; that's a number column.)
 2. **number** second. `Number("2024")` is a perfectly good number, so a column
    of bare years would read as `number` — which is the honest answer. Numbers
@@ -82,7 +82,7 @@ overlap, and we want the most specific interpretation that fits.
   whitespace so `Number("") === 0` doesn't sneak through.
 - **boolean**: native `boolean` values pass; otherwise the trimmed,
   lower-cased string must be exactly `"true"` or `"false"`.
-- **date**: guard *before* parsing. Require the trimmed value to look
+- **date**: guard _before_ parsing. Require the trimmed value to look
   date-shaped (a leading `YYYY-MM-DD` or `YYYY/MM/DD`, or `M/D/YYYY`) **and**
   then confirm `Date.parse` returns a finite timestamp. The shape guard is
   essential: `Date.parse` will happily accept `"42"` or `"March"` on some
@@ -155,7 +155,7 @@ export function inferColumnType(values: readonly unknown[]): ColumnType {
 - **Do** ignore empty cells before classifying.
 - **Do** keep the precedence boolean → number → date → string.
 - **Do** guard date detection with a shape regex before trusting `Date.parse`.
-- **Don't** classify a column unless *every* present value matches — one
+- **Don't** classify a column unless _every_ present value matches — one
   outlier means `string`.
 - **Don't** add more types (integer, float, datetime, json). Four, no more.
 - **Don't** let `Number("")`, `Date.parse("42")`, or `0`/`1` leak into the wrong
@@ -169,13 +169,13 @@ A **profile** is the set of derived summary fields stored on a dataset record so
 the UI can describe it without re-parsing the payload. Per the data model, a
 profiled dataset carries:
 
-| Field         | Type                              | Meaning                                |
-| ------------- | --------------------------------- | -------------------------------------- |
-| `rowCount`    | `number \| null`                  | Data rows, or `null` when N/A.         |
-| `columnCount` | `number \| null`                  | Columns, or `null` when N/A.           |
-| `columns`     | `string[]`                        | Column names, in order.                |
-| `columnTypes` | `Array<{ name; type }>`           | Per-column inferred type (see §2).     |
-| `size`        | `number`                          | Approximate payload size in bytes.     |
+| Field         | Type                    | Meaning                            |
+| ------------- | ----------------------- | ---------------------------------- |
+| `rowCount`    | `number \| null`        | Data rows, or `null` when N/A.     |
+| `columnCount` | `number \| null`        | Columns, or `null` when N/A.       |
+| `columns`     | `string[]`              | Column names, in order.            |
+| `columnTypes` | `Array<{ name; type }>` | Per-column inferred type (see §2). |
+| `size`        | `number`                | Approximate payload size in bytes. |
 
 `null` row/column counts and an empty `columns`/`columnTypes` are how the UI
 shows **"N/A"** — see §3.2.
@@ -274,7 +274,7 @@ export function profileData(
 }
 ```
 
-Parsing CSV/TSV text and detecting the payload shape happen *upstream* of
+Parsing CSV/TSV text and detecting the payload shape happen _upstream_ of
 `profileData`; this function takes already-parsed rows so it stays pure and
 trivially testable. The caller passes `null` for URL and non-tabular datasets.