@markschema/mdshape

Appearance

length ​

What It Is ​

length is used here as a contract-first parser powered by document-level structure checks, explicit section targeting, and typed field extraction for length scenarios. With document(), heading(), section(), and fields() in the schema, a compact markdown payload is converted into top-level keys title and frontmatter without manual length post-processing. Error cases report issue codes like invalid_type, making operational diagnostics for length flows consistent across local runs and CI.

When to Use ​

This method is a strong fit for tightening scalar constraints without redefining the base shape where deterministic length parsing matters more than free-form flexibility. Do not default to it for very loose drafts where strict refinement would block iteration around length; the main cost is key-level strictness that improves typing but rejects ad-hoc variations. For best results, compose length with document(), heading(), section(), and fields() so length schema intent stays readable and output remains predictable.

Input Markdown ​

md
## 0. META

- 4
- 7
- 9

Schema ​

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

const schema = md.document({
  scores: md.section('0. META').list(md.number()).pipeline(md.list(md.number()).length(3)),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "scores": [
      4,
      7,
      9
    ]
  }
}

Error ​

json
{
  "success": false,
  "error": {
    "issues": [
      {
        "code": "missing_section",
        "message": "Missing section \"0. META\"",
        "path": [
          "scores"
        ],
        "line": 1,
        "position": {
          "start": {
            "line": 1,
            "column": 1
          }
        }
      }
    ]
  }
}