@markschema/mdshape

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transform ​

What It Is ​

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

When to Use ​

Use transform when you need tightening scalar constraints without redefining the base shape for transform workflows and want parsing behavior that remains enforceable in review and CI. Avoid it for very loose drafts where strict refinement would block iteration in transform documents, because it introduces key-level strictness that improves typing but rejects ad-hoc variations. It pairs well with document(), section(), fields(), and string() to keep transform extraction boundaries explicit while preserving typed output for downstream code.

Input Markdown ​

md
## 1. META

- Alias: ALEX

Schema ​

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

const schema = md.document({
  meta: md.section('1. META').fields({
    Alias: md.string().transform((value) => value.toLowerCase()),
  }),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "meta": {
      "Alias": "alex"
    }
  }
}

Error ​

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