@markschema/mdshape

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trim ​

What It Is ​

trim is used here as a contract-first parser powered by document-level structure checks, explicit section targeting, and typed field extraction for trim scenarios. With document(), section(), fields(), and string() in the schema, a compact markdown payload is converted into top-level keys meta without manual trim post-processing. Error cases report issue codes like missing_section, making operational diagnostics for trim 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 trim parsing matters more than free-form flexibility. Do not default to it for very loose drafts where strict refinement would block iteration around trim; the main cost is key-level strictness that improves typing but rejects ad-hoc variations. For best results, compose trim with document(), section(), fields(), and string() so trim schema intent stays readable and output remains predictable.

Input Markdown ​

md
## 1. META

- Team:   Risk Platform

Schema ​

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

const schema = md.document({
  meta: md.section('1. META').fields({
    Team: md.string().trim(),
  }),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "meta": {
      "Team": "Risk Platform"
    }
  }
}

Error ​

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