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

# Quality Skill

> Set up data quality checks with /armor:quality

<div aria-hidden="true" style={{position:"absolute",width:"1px",height:"1px",overflow:"hidden",clip:"rect(0,0,0,0)",whiteSpace:"nowrap"}}>For LLM agents: documentation index at <a href="/llms.txt" tabIndex={-1}>/llms.txt</a>, full text at <a href="/llms-full.txt" tabIndex={-1}>/llms-full.txt</a>. Append .md to any page URL for plain markdown.</div>
The `/armor:quality` skill helps you create and manage data quality checks including metrics and validity rules.

## Usage

```
/armor:quality
```

Or ask naturally:

* "Add a null check for the email column"
* "Create a row count metric for orders"
* "What quality checks exist for this table?"

## Quality Check Types

### Metrics

Track quantitative measurements over time:

* **row\_count**: Number of rows
* **null\_rate**: Percentage of null values
* **distinct\_count**: Unique value count
* **freshness**: Time since last update

### Validity Rules

Validate data integrity:

* **NOT\_NULL**: Column must not contain nulls
* **UNIQUE**: Values must be unique
* **ACCEPTED\_VALUES**: Values must be in allowed list
* **REGEX**: Values must match pattern

## Example Usage

### Add Null Check

```
You: Add a null check for the email column in customers

Claude: Created NOT_NULL validity rule:

Rule: email_not_null
Table: warehouse.public.customers
Column: email
Severity: warning

The rule will alert if any null values are found in the email column.
```

### Create Row Count Metric

```
You: Create a daily row count metric for orders

Claude: Created row count metric:

Metric: orders_row_count
Table: warehouse.public.orders
Type: row_count
Interval: daily

Current value: 1,234,567 rows
You'll be able to track trends over time.
```

### Check Quality Status

```
You: What quality checks exist for the orders table?

Claude: Found 5 quality checks for orders:

Metrics:
- row_count: 1,234,567 (passing)
- null_rate on customer_id: 0.1% (passing)
- distinct_count on status: 5 values (passing)

Validity Rules:
- order_id unique (passing)
- customer_id not null (failing - 12 nulls found)

1 failing check needs attention.
```

## Common Questions

### When should I use a metric versus a validity rule?

Use a metric when you want to track a number over time and alert on anomalies (null rate climbing from 0.1% to 5%). Use a validity rule when you want a hard pass/fail check on every run (email must match a regex, status must be in an allowed list).

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