---

name: golden-dataset-management description: Use when backing up, restoring, or validating golden datasets. Prevents data loss and ensures test data integrity for AI/ML evaluation systems. context: fork agent: data-pipeline-engineer version: 1.0.0 author: OrchestKit AI Agent Hub tags: [golden-dataset, backup, data-protection, testing, regression, 2026] allowed-tools:

• Read
• Grep
• Glob
• Bash # For backup/restore scripts user-invocable: false

---

Golden Dataset Management

Protect and maintain high-quality test datasets for AI/ML systems

Overview

A golden dataset is a curated collection of high-quality examples used for:

• Regression testing: Ensure new code doesn't break existing functionality
• Retrieval evaluation: Measure search quality (precision, recall, MRR)
• Model benchmarking: Compare different models/approaches
• Reproducibility: Consistent results across environments

When to use this skill:

• Building test datasets for RAG systems
• Implementing backup/restore for critical data
• Validating data integrity (URL contracts, embeddings)
• Migrating data between environments

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OrchestKit's Golden Dataset

Stats (Production):

• 98 analyses (completed content analyses)
• 415 chunks (embedded text segments)
• 203 test queries (with expected results)
• 91.6% pass rate (retrieval quality metric)

Purpose:

• Test hybrid search (vector + BM25 + RRF)
• Validate metadata boosting strategies
• Detect regressions in retrieval quality
• Benchmark new embedding models

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Core Concepts

Data Integrity Contracts

The URL Contract: Golden dataset analyses MUST store real canonical URLs, not placeholders.

Copy# WRONG - Placeholder URL (breaks restore)
analysis.url = "https://orchestkit.dev/placeholder/123"

# CORRECT - Real canonical URL (enables re-fetch if needed)
analysis.url = "https://docs.python.org/3/library/asyncio.html"

Why this matters:

• Enables re-fetching content if embeddings need regeneration
• Allows validation that source content hasn't changed
• Provides audit trail for data provenance

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Backup Strategy Comparison

OrchestKit uses JSON backup for version control and portability.

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Quick Reference

Backup Format

Copy{
  "version": "1.0",
  "created_at": "2025-12-19T10:30:00Z",
  "metadata": {
    "total_analyses": 98,
    "total_chunks": 415,
    "total_artifacts": 98
  },
  "analyses": [
    {
      "id": "550e8400-e29b-41d4-a716-446655440000",
      "url": "https://docs.python.org/3/library/asyncio.html",
      "content_type": "documentation",
      "status": "completed",
      "created_at": "2025-11-15T08:20:00Z",
      "chunks": [
        {
          "id": "7c9e6679-7425-40de-944b-e07fc1f90ae7",
          "content": "asyncio is a library...",
          "section_title": "Introduction to asyncio"
          // embedding NOT included (regenerated on restore)
        }
      ]
    }
  ]
}

Key Design Decisions:

• Embeddings excluded (regenerate on restore with current model)
• Nested structure (analyses -> chunks -> artifacts)
• Metadata for validation
• ISO timestamps for reproducibility

CLI Commands

Copycd backend

# Backup golden dataset
poetry run python scripts/backup_golden_dataset.py backup

# Verify backup integrity
poetry run python scripts/backup_golden_dataset.py verify

# Restore from backup (WARNING: Deletes existing data)
poetry run python scripts/backup_golden_dataset.py restore --replace

# Restore without deleting (adds to existing)
poetry run python scripts/backup_golden_dataset.py restore

Validation Checks

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Best Practices Summary

1. Version control backups - Commit to git for history and diffs
2. Validate before deployment - Run verify before production changes
3. Test restore in staging - Never test restore in production first
4. Document changes - Track additions/removals in metadata

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Disaster Recovery Quick Guide

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References

For detailed implementation patterns, see:

• references/storage-patterns.md - Backup strategies, JSON format, backup script implementation, CI/CD automation
• references/versioning.md - Restore implementation, embedding regeneration, validation checklist, disaster recovery scenarios

---

Related Skills

• golden-dataset-validation - Schema and integrity validation
• golden-dataset-curation - Quality criteria and curation workflows
• pgvector-search - Retrieval evaluation using golden dataset
• ai-native-development - Embedding generation for restore

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Version: 1.0.0 (December 2025) Status: Production-ready patterns from OrchestKit's 98-analysis golden dataset

Capability Details

backup

Keywords: golden dataset, backup, export, json backup, version control data Solves:

• How do I backup the golden dataset?
• Export analyses to JSON for version control
• Protect critical test datasets
• Create portable database snapshots

restore

Keywords: restore dataset, import analyses, regenerate embeddings, disaster recovery, new environment Solves:

• How do I restore from backup?
• Import golden dataset to new environment
• Regenerate embeddings after restore
• Disaster recovery procedures

validation

Keywords: verify dataset, url contract, data integrity, validate backup, placeholder urls Solves:

• How do I validate dataset integrity?
• Check URL contracts (no placeholders)
• Verify embeddings exist
• Detect orphaned chunks

ci-cd-automation

Keywords: automated backup, github actions, ci cd backup, scheduled backup Solves:

• How do I automate dataset backups?
• Set up GitHub Actions for weekly backups
• Commit backups to git automatically
• CI/CD integration patterns

disaster-recovery

Keywords: disaster recovery, accidental deletion, migration failure, rollback Solves:

• What if I accidentally delete the dataset?
• Database migration gone wrong
• Restore after data corruption
• Rollback procedures

orchestkit-golden-dataset

Keywords: orchestkit, 98 analyses, 415 chunks, retrieval evaluation, real world Solves:

• What is OrchestKit's golden dataset?
• How does OrchestKit protect test data?
• Real-world backup/restore examples
• Production golden dataset stats