@markschema/mdshape

Appearance

int ​

Type: number

Signature: md.number().int()

What It Is ​

md.number().int() parses markdown with document-level structure checks, explicit section targeting, and typed field extraction, so this page defines a strict int contract instead of permissive text scraping. The schema combines operators such as document(), section(), fields(), and number() to map 1 h1 heading, 1 h2 section, and list content into top-level keys meta for this int behavior. If parsing fails, the result carries issue codes like missing_section, giving the caller precise debugging context for int paths.

When to Use ​

Use md.number().int() when you need typed markdown parsing with deterministic contracts for int workflows and want parsing behavior that remains enforceable in review and CI. Avoid it for exploratory drafts that intentionally avoid strict validation in int documents, because it introduces key-level strictness that improves typing but rejects ad-hoc variations. It pairs well with document(), section(), fields(), and number() to keep int extraction boundaries explicit while preserving typed output for downstream code.

md.number().int() ​

Input Markdown ​

md
## 1. META

- Score: 7

Schema ​

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

const schema = md.document({
  meta: md.section('1. META').fields({
    Score: md.number().int(),
  }),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "meta": {
      "Score": 7
    }
  }
}

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 \"1. META\"",
        "path": [
          "meta"
        ],
        "line": 1,
        "position": {
          "start": {
            "line": 1,
            "column": 1
          }
        }
      }
    ]
  }
}