Golden Dataset Validation

Ensure data integrity, prevent duplicates, and maintain quality standards

Overview

This skill provides comprehensive validation patterns for the golden dataset, ensuring every entry meets quality standards before inclusion.

When to use this skill:

• Validating new documents before adding

• Running integrity checks on existing dataset

• Detecting duplicate or similar content

• Analyzing coverage gaps

• Pre-commit validation hooks

Schema Validation

Document Schema (v2.0)

{ "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "required": ["id", "title", "source_url", "content_type", "sections"], "properties": { "id": { "type": "string", "pattern": "^[a-z0-9-]+$", "description": "Unique kebab-case identifier" }, "title": { "type": "string", "minLength": 10, "maxLength": 200 }, "source_url": { "type": "string", "format": "uri", "description": "Canonical source URL (NOT placeholder)" }, "content_type": { "type": "string", "enum": ["article", "tutorial", "research_paper", "documentation", "video_transcript", "code_repository"] }, "bucket": { "type": "string", "enum": ["short", "long"] }, "tags": { "type": "array", "items": {"type": "string"}, "minItems": 2, "maxItems": 10 }, "sections": { "type": "array", "minItems": 1, "items": { "type": "object", "required": ["id", "title", "content"], "properties": { "id": {"type": "string", "pattern": "^[a-z0-9-/]+$"}, "title": {"type": "string"}, "content": {"type": "string", "minLength": 50}, "granularity": {"enum": ["coarse", "fine", "summary"]} } } } } }

Query Schema

{ "type": "object", "required": ["id", "query", "difficulty", "expected_chunks", "min_score"], "properties": { "id": {"type": "string", "pattern": "^q-[a-z0-9-]+$"}, "query": {"type": "string", "minLength": 5, "maxLength": 500}, "modes": {"type": "array", "items": {"enum": ["semantic", "keyword", "hybrid"]}}, "category": {"enum": ["specific", "broad", "negative", "edge", "coarse-to-fine"]}, "difficulty": {"enum": ["trivial", "easy", "medium", "hard", "adversarial"]}, "expected_chunks": {"type": "array", "items": {"type": "string"}, "minItems": 1}, "min_score": {"type": "number", "minimum": 0, "maximum": 1} } }

Validation Rules Summary

Rule Purpose Severity

No Placeholder URLs Ensure real canonical URLs Error

Unique Identifiers No duplicate doc/query/section IDs Error

Referential Integrity Query chunks reference valid sections Error

Content Quality Title/content length, tag count Warning

Difficulty Distribution Balanced query difficulty levels Warning

Quick Reference

Duplicate Detection Thresholds

Similarity Action

= 0.90 Block - Content too similar

= 0.85 Warn - High similarity detected

= 0.80 Note - Similar content exists

< 0.80 Allow - Sufficiently unique

Coverage Requirements

Metric Minimum

Tutorials

= 15% of documents

Research papers

= 5% of documents

Domain coverage

= 5 docs per expected domain

Hard queries

= 10% of queries

Adversarial queries

= 5% of queries

Difficulty Distribution Requirements

Level Minimum Count

trivial 3

easy 3

medium 5

hard 3

References

For detailed implementation patterns, see:

• references/validation-rules.md

• URL validation, ID uniqueness, referential integrity, content quality, and duplicate detection code

• references/quality-metrics.md

• Coverage analysis, pre-addition validation workflow, full dataset validation, and CLI/hook integration

Related Skills

• golden-dataset-curation

• Quality criteria and workflows

• golden-dataset-management

• Backup/restore operations

• pgvector-search

• Embedding-based duplicate detection

Version: 1.0.0 (December 2025) Issue: #599

Capability Details

schema-validation

Keywords: schema, validation, schema check, format validation Solves:

• Validate entries against document schema

• Check required fields are present

• Verify data types and constraints

duplicate-detection

Keywords: duplicate, detection, deduplication, similarity check Solves:

• Detect duplicate or near-duplicate entries

• Use semantic similarity for fuzzy matching

• Prevent redundant entries in dataset

referential-integrity

Keywords: referential, integrity, foreign key, relationship Solves:

• Verify relationships between documents and queries

• Check source URL mappings are valid

• Ensure cross-references are consistent

coverage-analysis

Keywords: coverage, analysis, distribution, completeness Solves:

• Analyze dataset coverage across domains

• Identify gaps in difficulty distribution

• Report coverage metrics and recommendations