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Strictness is a per-rule setting, configured in a rule’s YAML frontmatter, that controls how strongly density scoring penalizes errors in the documents a check rule reviews.

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Why it exists

Not every rule should penalize errors equally. A rule that catches banned phrases in polished marketing copy should be unforgiving; a rule that flags style preferences in early drafts should be more lenient. Strictness scales the penalty per rule without changing the rule’s criteria or the scoring formula.

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How it works

Strictness is the multiplier in the density scoring formula: 
rawScore = clamp(0, 100,  100 - density * strictness)
A higher strictness means a given error density causes a larger score drop. VectorLint accepts three named levels or any positive number:
LevelValueBehavior
lenient5Forgives occasional errors; small penalty per issue
standard10Balanced - the default when strictness is unset
strict20Penalizes errors heavily; small issues still cost points
Any positive number is also valid, for fine-grained control between the named levels.

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Key properties

Set strictness with the strictness field in the rule’s frontmatter. A check rule using the strict preset: 
---
specVersion: 2.0.0
evaluator: base
type: check
id: AIPattern
name: AI Pattern
severity: warning
strictness: strict
---
A numeric value works the same way: 
strictness: 15
When strictness is omitted, the rule scores at standard (10).
  • lenient forgives occasional errors - suited to draft-stage or low-stakes content.
  • standard is the balanced default for most rules.
  • strict penalizes errors heavily - suited to published, high-stakes content where even rare issues matter.

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Constraints

  • Strictness applies only to density scoring - rules that count discrete issues. It has no effect on rubric scoring, which uses criterion weights instead.
  • Strictness is set per rule, in its frontmatter.