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Implements comprehensive validation error interpretation and systematic fixing guidance. Addresses the validation feedback loop problem (15,107 loops from 19,113 errors). Files created: - 4 evaluations testing error interpretation, false positives, auto-sanitization, validation loop - SKILL.md (690 lines) - Validation philosophy, loop pattern, profiles, auto-sanitization - ERROR_CATALOG.md (865 lines) - 9 error types with examples and fixes - FALSE_POSITIVES.md (669 lines) - 6 false positive patterns with decision framework - README.md (329 lines) - Skill metadata with telemetry statistics Key features: - Error severity levels (errors/warnings/suggestions) - Validation loop pattern (2-3 iterations, 23s+58s average) - 4 validation profiles (minimal/runtime/ai-friendly/strict) - Auto-sanitization system (operator structure fixes) - False positive recognition (~40% of warnings acceptable) - Error distribution analysis (missing_required 45%, invalid_value 28%) - Recovery strategies (progressive validation, error triage, clean connections) - Decision framework for warning acceptance Error catalog: - missing_required (45%) - Required field not provided - invalid_value (28%) - Value doesn't match allowed options - type_mismatch (12%) - Wrong data type - invalid_expression (8%) - Expression syntax errors - invalid_reference (5%) - Referenced node doesn't exist - operator_structure (2%) - Auto-fixed automatically False positive patterns: - Missing error handling (acceptable for dev/test) - No retry logic (acceptable for internal APIs) - Missing rate limiting (acceptable for low volume) - Unbounded queries (acceptable for small datasets) - Missing input validation (acceptable for trusted sources) - Hardcoded credentials (never acceptable!) Validation insights: - 79% of validation errors lead to feedback loops - Average 2-3 iterations to valid configuration - 94% success rate within 3 iterations - ai-friendly profile reduces false positives by 60% - Auto-sanitization handles operator structure issues automatically Total: ~2,553 lines across 8 files Based on analysis of 19,113 validation errors and 15,107 feedback loops. 🤖 Conceived by Romuald Członkowski - https://www.aiadvisors.pl/en
15 lines
645 B
JSON
15 lines
645 B
JSON
{
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"id": "validation-003",
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"skills": ["n8n-validation-expert"],
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"query": "I'm getting an error about 'Binary operator cannot have singleValue property'. What does this mean?",
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"expected_behavior": [
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"Identifies this as an operator structure issue",
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"Explains auto-sanitization system",
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"States that this error will be automatically fixed on next workflow update",
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"Explains binary vs unary operator distinction",
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"References the auto-sanitization section",
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"Suggests that user doesn't need to manually fix this",
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"May suggest running n8n_update_partial_workflow or validate_workflow to trigger auto-fix"
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]
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}
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