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Every VectorLint rule file begins with a YAML frontmatter block that controls how VectorLint runs the evaluation, handles the result, and reports violations. The Markdown body that follows is the prompt sent to the LLM. 
---
id: MyRule
name: My Rule
type: check
severity: error
---

Your LLM prompt goes here.

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Required fields

FieldTypeDescription
idstringUnique identifier for the rule. Used in CLI output and in .vectorlint.ini overrides. PascalCase recommended (e.g., GrammarChecker).
namestringHuman-readable display name shown in CLI output.

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Optional fields

FieldDefaultDescription
specVersion(none)Rule spec version. Set to 1.0.0 for rules using the full judge schema.
evaluatorbaseEvaluator type. Use base for standard rules. Use technical-accuracy for rules that verify factual claims against live web search (requires a search provider).
typecheckEvaluation mode. check finds specific, countable violations. judge scores content against weighted criteria on a 1–4 rubric.
severitywarningHow a failing result is reported. error causes a non-zero exit code; warning reports the issue without failing the run.
evaluateAschunkWhether to evaluate content in sections (chunk) or as a single unit (document). Chunking is applied automatically to documents over 600 words. Set to document to disable chunking for a specific rule.
target(none)Regex specification to extract and evaluate a specific portion of the content, such as the H1 headline. See Target fields below.
criteria(none)Required when type is judge. Defines the scoring dimensions and their weights. See Criteria fields below.

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Target fields

The target field narrows evaluation to a specific part of the document. It is an object with the following sub-fields:
FieldTypeRequiredDescription
regexstringYesRegular expression pattern to match the target content.
flagsstringNoRegex flags (e.g., "mu" for multiline and Unicode).
groupnumberNoCapture group index to extract. If omitted, the full match is used.
requiredbooleanNoWhen true, a missing match immediately reports an error with the suggestion text and skips LLM evaluation. When false or omitted, a missing match causes VectorLint to evaluate the full document instead.
suggestionstringNoMessage shown when required: true and the pattern does not match.
Example — targeting only the H1 headline: 
target:
  regex: '^#\s+(.+)$'
  flags: "mu"
  group: 1
  required: true
  suggestion: Add an H1 headline for the article.

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Criteria fields

The criteria field is an array of criterion objects used with type: judge. Each criterion defines one scoring dimension.
FieldTypeRequiredDescription
namestringYesHuman-readable criterion name shown in CLI output.
idstringYesUnique identifier for this criterion. PascalCase recommended (e.g., TechnicalAccuracy). Referenced in the Markdown body rubric as # CriterionName <weight=N>.
weightnumberNoRelative importance of this criterion in the weighted average. Higher values increase the criterion’s influence on the final score. Defaults to 1.
targetobjectNoCriterion-specific content matching. Uses the same sub-fields as the top-level target. Overrides the rule-level target for this criterion only.
Example — judge rule with two weighted criteria: 
criteria:
  - name: Technical Accuracy
    id: TechnicalAccuracy
    weight: 40
  - name: Readability
    id: Readability
    weight: 30

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Complete example

The following rule uses all commonly-used fields. It targets the H1 headline, judges it against two criteria, and requires the headline to exist before evaluation runs. 
---
specVersion: 1.0.0
evaluator: base
type: judge
id: HeadlineEvaluator
name: Headline Evaluator
severity: error
evaluateAs: document
target:
  regex: '^#\s+(.+)$'
  flags: "mu"
  group: 1
  required: true
  suggestion: Add an H1 headline for the article.
criteria:
  - name: Value Communication
    id: ValueCommunication
    weight: 12
  - name: Curiosity Gap
    id: CuriosityGap
    weight: 2
---

You are a headline evaluator for developer blog posts.

## RUBRIC

# Value Communication <weight=12>

### Excellent <score=4>
Specific, immediately appealing benefit clearly stated.

### Good <score=3>
Clear benefit but less specific impact.

### Fair <score=2>
Vague benefit implied but not stated.

### Poor <score=1>
No apparent benefit to the reader.

# Curiosity Gap <weight=2>

### Excellent <score=4>
Creates genuine intrigue without being misleading.

### Good <score=3>
Mildly interesting, reader may continue.

### Fair <score=2>
Neutral — no curiosity created.

### Poor <score=1>
Actively off-putting or confusing.