data-validation

name	data-validation
description	Validates data against common and custom rules (required fields, formats, ranges). Use when checking data quality, input validation, or enforcing schemas and constraints.
metadata	{"displayName":"Data Validation","version":"1.0.0","author":"Browser4","tags":"validation, data, quality","dependencies":""}

Data Validation Skill

Description

Validate data against specified rules. This skill provides a flexible validation framework supporting common validation patterns like email format, required fields, and custom rules.

Dependencies

None

Parameters

Parameter	Type	Required	Default	Description
data	Map<String, Any>	Yes	-	The data to validate
rules	List	Yes	-	List of validation rules to apply

Return Value

Returns a SkillResult with the following data structure on success:

{
  "validationResults": {
    "rule1": true,
    "rule2": true
  }
}

On failure, returns:

{
  "validationResults": {
    "rule1": true,
    "rule2": false
  },
  "errors": ["error message 1", "error message 2"]
}

Supported Validation Rules

Rule	Description	Example
email	Validates email format	"[e-mail]"
required	Checks all fields are non-null and non-blank	All values must be present

Usage Examples

Email Validation

val result = registry.execute(
    skillId = "data-validation",
    context = context,
    params = mapOf(
        "data" to mapOf("email" to "[e-mail]"),
        "rules" to listOf("email")
    )
)

Multiple Rules Validation

val result = registry.execute(
    skillId = "data-validation",
    context = context,
    params = mapOf(
        "data" to mapOf(
            "email" to "[e-mail]",
            "name" to "John Doe",
            "age" to "25"
        ),
        "rules" to listOf("email", "required")
    )
)

Error Handling

The skill returns a failure result in the following cases:

• Missing required parameter data
• Missing required parameter rules
• Unknown validation rule specified
• Validation rule fails for the provided data

Implementation Notes

• Rules are applied in the order specified in the rules list
• All rules are executed even if some fail (to provide complete feedback)
• Custom validation rules can be added by extending the skill
• Validation is performed synchronously
• Thread-safe execution

Extending with Custom Rules

To add custom validation rules, extend the skill and override the execute method to handle additional rule types:

class ExtendedDataValidationSkill : DataValidationSkill() {
    override suspend fun execute(context: SkillContext, params: Map<String, Any>): SkillResult {
        // Handle custom rules first
        val rules = params["rules"] as? List<String> ?: emptyList()

        if (rules.contains("custom-rule")) {
            // Implement custom validation
        }

        // Delegate to parent for standard rules
        return super.execute(context, params)
    }
}

Best Practices

• Always provide clear error messages
• Validate input data before processing
• Use multiple validation rules for comprehensive checks
• Combine with other skills in a pipeline for data processing workflows
• Log validation failures for monitoring and debugging

See Also

• Web Scraping Skill
• Form Filling Skill
• Skills Framework Documentation