> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectorlint.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Strictness

> Control how strongly errors are penalized when scoring.

**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.

## 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.

## How it works

Strictness is the multiplier in the [density scoring formula](/density-scoring):

```text theme={null}
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:

| Level      | Value | Behavior                                                 |
| ---------- | ----- | -------------------------------------------------------- |
| `lenient`  | 5     | Forgives occasional errors; small penalty per issue      |
| `standard` | 10    | Balanced - the **default** when strictness is unset      |
| `strict`   | 20    | Penalizes errors heavily; small issues still cost points |

Any positive number is also valid, for fine-grained control between the named levels.

## Key properties

Set strictness with the `strictness` field in the rule's frontmatter. A check rule using the strict preset:

```yaml theme={null}
---
specVersion: 2.0.0
evaluator: base
type: check
id: AIPattern
name: AI Pattern
severity: warning
strictness: strict
---
```

A numeric value works the same way:

```yaml theme={null}
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.

## Constraints

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

## Related

* [Density scoring](/density-scoring) - the formula strictness multiplies
* [Rubric scoring](/rubric-scoring) - unaffected by strictness
* [Rule frontmatter](/rule-frontmatter) - where the `strictness` field lives
* [Handling false positives](/handling-false-positives) - strictness controls scoring severity; `CONFIDENCE_THRESHOLD` controls whether a candidate issue is surfaced at all. Different layers, different problems.