@markschema/mdshape

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regex ​

What It Is ​

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

When to Use ​

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

Input Markdown ​

md
# RUNBOOK: Fraud Routing

Schema ​

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

const schema = md.document({
  title: md.heading(1).regex(/^RUNBOOK:\s.+/),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "title": "RUNBOOK: Fraud Routing"
  }
}

Error ​

json
{
  "success": false,
  "error": {
    "issues": [
      {
        "code": "missing_heading",
        "message": "Missing heading with depth 1",
        "path": [
          "title"
        ],
        "line": 1,
        "position": {
          "start": {
            "line": 1,
            "column": 1
          }
        }
      }
    ]
  }
}