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Documentation Index

Fetch the complete documentation index at: https://dms.dzaleka.com/llms.txt

Use this file to discover all available pages before exploring further.

The dms validate command checks one or more records against the DMS JSON Schema. It distinguishes between errors that must be fixed and warnings for recommended fields that are absent.

​
Validating a single file

dms validate my-record.json
If the record is valid you will see a green panel, followed by any warnings for recommended fields that are missing: 
╭────────────────────────────────────╮
│ ✓ VALID  examples/story.json       │
╰────────────────────────────────────╯
  ⚠ Recommended field 'Format' is not provided.
If the record has schema violations you will see a red panel and a table listing every problem: 
╭────────────────────────────────────╮
│ ✗ INVALID  bad-record.json         │
╰────────────────────────────────────╯
 Field            Issue
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Title            Value cannot be empty.
 Type             Invalid value 'unknown'. Allowed: story, ...

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Validating an entire directory

Pass --dir to validate every .json file in a directory at once: 
dms validate --dir records/
Each file is reported individually, then a batch summary is printed: 
╭──────────────────────────────────╮
│ Batch Validation Summary         │
│                                  │
│   Total files:  12               │
│   Valid:        11               │
│   Invalid:      1                │
╰──────────────────────────────────╯

​
Errors vs. warnings

Errors mean the record does not conform to the DMS schema. The record will be rejected by any tool that enforces the schema. Common causes:
Error messageWhat to do
Required field 'Title' is missing.Add the title key with a non-empty string value.
Required field 'ID' is missing.Add the id key containing a valid UUID.
Value cannot be empty.The field is present but set to "" — provide a real value.
Invalid value 'unknown'. Allowed: story, photo, …The type value is not in the permitted enum list. Run dms info to see all valid types.
Expected type 'array', got 'str'.A field that expects an array (such as subject) was given a plain string. Wrap the value in [].
Value '2024-99-01' is not a valid date.Dates must be ISO 8601 (YYYY-MM-DD).
Value 'urn:...' does not match expected format.The id field must be a UUID (xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx).

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Validation workflow

1

Create or edit your record

Use dms init, the DMS Vault, or a text editor to produce a .json file. See Creating records for details.
2

Run single-file validation

dms validate my-record.json
Read every error in the output table — each row names the field and describes the problem precisely.
3

Fix errors

Address each error. The most common fixes:
  • Add missing required fields (id, title, type, description, language)
  • Replace empty string values with real content
  • Correct the type value to one of the permitted enum values
  • Wrap multi-value fields (subject) in a JSON array
Run dms info at any time to see all valid type values, creator roles, and access levels.
4

Re-validate until clean

Re-run dms validate after each round of fixes. Repeat until you see the green ✓ VALID panel.
5

Batch-validate your archive directory

Before publishing or sharing your collection, run a batch validation:
dms validate --dir records/
The command exits with code 1 if any file is invalid, making it suitable for CI/CD pipelines.

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Using validation in CI/CD

Because dms validate exits with code 0 when all records are valid and 1 when any are invalid, you can integrate it directly into a CI pipeline:
.github/workflows/validate.yml
- name: Validate DMS records
  run: dms validate --dir records/
The pipeline step will fail automatically if a contributor adds an invalid record, preventing malformed metadata from reaching your main branch.
Add dms validate --dir records/ as a pre-commit hook to catch errors before they are ever committed.