Validate data against the schema

Validate data against the schema

docs/index.md says that linkml-validate checks a file against the JSON Schema generated from the schema.¹ Name the class the file holds with -C, for example:

linkml-validate -s src/github_actions/schema/github_actions.yaml -C GhaReusableWorkflow workflow.yaml

tests/test_data.py holds every example in tests/data/ to the same validator: a valid example must pass, and a counter-example must fail.²

What the generated models check

The models in src/github_actions/datamodel/ check less, as LinkML 1.12.1 generates them:

• the dataclasses check required fields and enum values, but no pattern or minimum;
• the Pydantic models also check minimums and the patterns a field carries itself, but not those of the gha_identifier and gha_permission types, so a job, input, output or secret key and a permission go unchecked.

Neither model checks the rules: that a reusable workflow’s triggers include workflow_call, and that a job that calls a reusable workflow names no runner labels or runner group. So validate a file before loading it into either model.

What no tool checks

• that a list never repeats a key, such as two inputs with the same input_id or two jobs with the same job_id;
• that read-all, write-all or {} stands alone in a permissions list, and that id-token takes write or none, never read;
• that a ruleset whose requires_check names a check lists required_status_checks among its rule_types;
• that only a composite action’s outputs carry an output_expression.

Python’s regular expressions also let a value end in one line break, so linkml-validate accepts a permission followed by a newline.

Footnotes

1. docs/index.md, the Validating data section ↩

2. tests/test_data.py, the comment above SCHEMA and the two validator tests ↩