@markschema/mdshape

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startsWith ​

Type: string

Signature: md.string().startsWith(prefix)

What It Is ​

On this page, md.string().startsWith(prefix) centers on document-level structure checks, explicit section targeting, and typed field extraction to keep starts with parsing deterministic and schema-driven. The example expects 1 h1 heading, 1 h2 section, and list content and returns top-level keys owner directly from the declared starts with extraction rules. Violations produce issue codes like missing_section, which avoids brittle string checks and keeps starts with failure handling explicit.

When to Use ​

Choose md.string().startsWith(prefix) for typed markdown parsing with deterministic contracts, especially when starts with authoring rules must remain stable across teams. Skip it in exploratory drafts that intentionally avoid strict validation workflows for starts with, since key-level strictness that improves typing but rejects ad-hoc variations. Combining it with document(), section(), fields(), and string() yields predictable starts with parsing, clearer errors, and easier runtime integration.

md.string().startsWith(prefix) ​

Input Markdown ​

md
## 1. OWNER

- Role: Lead Engineer

Schema ​

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

const schema = md.document({
  owner: md.section('1. OWNER').fields({
    Role: md.string().startsWith('Lead'),
  }),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "owner": {
      "Role": "Lead Engineer"
    }
  }
}

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. OWNER\"",
        "path": [
          "owner"
        ],
        "line": 1,
        "position": {
          "start": {
            "line": 1,
            "column": 1
          }
        }
      }
    ]
  }
}