pipeline

Type: wrappers

Signature: pipeline(nextSchema)

What It Is

pipeline(nextSchema) is used here as a contract-first parser powered by document-level structure checks, explicit section targeting, and typed field extraction for pipeline scenarios. With document(), section(), fields(), and string() in the schema, 1 h1 heading, 1 h2 section, and list content is converted into top-level keys meta without manual pipeline post-processing. Error cases report issue codes like missing_section, making operational diagnostics for pipeline flows consistent across local runs and CI.

When to Use

This method is a strong fit for typed markdown parsing with deterministic contracts where deterministic pipeline parsing matters more than free-form flexibility. Do not default to it for exploratory drafts that intentionally avoid strict validation around pipeline; the main cost is key-level strictness that improves typing but rejects ad-hoc variations. For best results, compose pipeline(nextSchema) with document(), section(), fields(), and string() so pipeline schema intent stays readable and output remains predictable.

pipeline(nextSchema)

Input Markdown

## 1. META

- Score: 7

Schema

import { md } from '@markschema/mdshape'

const schema = md.document({
  meta: md.section('1. META').fields({
    Score: md
      .string()
      .transform((value) => Number(value))
      .pipeline(md.number().int().min(0).max(10)),
  }),
})

Result

Success

{
  "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.

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