@markschema/mdshape

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min ​

Type: heading-text

Signature: md.headingText().min(length)

What It Is ​

md.headingText().min(length) is used here as a contract-first parser powered by document-level structure checks and boundary constraints for min scenarios. With document(), section(), headingLevel(), and each() in the schema, 1 h1 heading, 1 h2 section, and 1 h3 subsection is converted into top-level keys events without manual min post-processing. Error cases report issue codes like string_too_short, making operational diagnostics for min flows consistent across local runs and CI.

When to Use ​

This method is a strong fit for typed markdown parsing with deterministic contracts where deterministic min parsing matters more than free-form flexibility. Do not default to it for exploratory drafts that intentionally avoid strict validation around min; the main cost is more explicit schema maintenance to keep output deterministic. For best results, compose md.headingText().min(length) with document(), section(), headingLevel(), and each() so min schema intent stays readable and output remains predictable.

md.headingText().min(length) ​

Input Markdown ​

md
## 3. EVENTS

### ABC

**NARRATION:** Validate signals.

Schema ​

ts
import { md } from '@markschema/mdshape'

const schema = md.document({
  events: md
    .section('3. EVENTS')
    .subsections(3)
    .each(
      md.object({
        title: md.headingText().min(3),
      }),
    ),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "events": [
      {
        "title": "ABC"
      }
    ]
  }
}

Error ​

Failure trigger: The input violates one or more constraints declared in the schema; use issues[].path and issues[].code to locate the exact failing node.

json
{
  "success": false,
  "error": {
    "issues": [
      {
        "code": "missing_section",
        "message": "Missing section \"3. EVENTS\"",
        "path": [
          "events"
        ],
        "line": 1,
        "position": {
          "start": {
            "line": 1,
            "column": 1
          }
        }
      }
    ]
  }
}