Agent Skills: Golden Dataset

Golden Dataset

Comprehensive patterns for building, managing, and validating golden datasets for AI/ML evaluation. Each category has individual rule files in rules/ loaded on-demand.

Quick Reference

| Category | Rules | Impact | When to Use | | -------- | ----- | ------ | ----------- | | Curation | 2 | HIGH | Content collection, annotation pipelines | | Management | 2 | HIGH | Versioning, backup/restore | | Validation | 1 | CRITICAL | Regression testing | | Add Workflow | 1 | HIGH | 9-phase curation, quality scoring, bias detection, silver-to-gold |

Total: 6 rules across 4 categories. House thresholds and scars: references/ork-delta.md.

Curation

Content collection, multi-agent annotation, and diversity analysis for golden datasets.

| Rule | File | Key Pattern | | ---- | ---- | ----------- | | Collection | rules/curation-collection.md | Content type classification, quality thresholds, duplicate prevention | | Annotation | rules/curation-annotation.md | Multi-agent pipeline, consensus aggregation, Langfuse tracing |

Difficulty ladder, coverage floors, and duplicate thresholds: references/ork-delta.md.

Management

Versioning, storage, and CI/CD automation for golden datasets.

| Rule | File | Key Pattern | | ---- | ---- | ----------- | | Versioning | rules/management-versioning.md | JSON backup format, embedding regeneration, disaster recovery | | Storage | rules/management-storage.md | Backup strategies, URL contract, data integrity checks |

CI automation for backups is upstream's job; see "Upstream coverage" below.

Validation

Quality scoring, drift detection, and regression testing for golden datasets.

| Rule | File | Key Pattern | | ---- | ---- | ----------- | | Regression | rules/validation-regression.md | Difficulty distribution, pre-commit hooks, full dataset validation |

Schema validation and duplicate detection are upstream's job (see "Upstream coverage" below); the house thresholds they must enforce live in references/ork-delta.md.

Add Workflow

Structured workflow for adding new documents to the golden dataset.

| Rule | File | Key Pattern | | ---- | ---- | ----------- | | Add Document | rules/curation-add-workflow.md | 9-phase curation, parallel quality analysis, bias detection |

Quick Start Example

async def validate_before_add(document: dict, source_url_map: dict) -> dict:
    """Pre-addition validation for golden dataset entries."""
    errors = []

    # 1. URL contract check
    if "placeholder" in document.get("source_url", ""):
        errors.append("URL must be canonical, not a placeholder")

    # 2. Content quality
    if len(document.get("title", "")) < 10:
        errors.append("Title too short (min 10 chars)")

    # 3. Tag requirements
    if len(document.get("tags", [])) < 2:
        errors.append("At least 2 domain tags required")

    return {"valid": len(errors) == 0, "errors": errors}

Key Decisions

| Decision | Recommendation | | -------- | -------------- | | Backup format | JSON (version controlled, portable) | | Embedding storage | Exclude from backup (regenerate on restore) | | Quality threshold | >= 0.70 quality score for inclusion | | Confidence threshold | >= 0.65 for auto-include | | Duplicate threshold | >= 0.90 similarity blocks, >= 0.85 warns | | Min tags per entry | 2 domain tags | | Min test queries | 3 per document | | Difficulty balance | Trivial 3, Easy 3, Medium 5, Hard 3 minimum | | CI frequency | Weekly automated backup (Sunday 2am UTC) |

Common Mistakes

1. Using placeholder URLs instead of canonical source URLs
2. Skipping embedding regeneration after restore
3. Not validating referential integrity between documents and queries
4. Over-indexing on articles (neglecting tutorials, research papers)
5. Missing difficulty distribution balance in test queries
6. Not running verification after backup/restore operations
7. Testing restore procedures in production instead of staging
8. Committing SQL dumps instead of JSON (not version-control friendly)

Running a dataset as an experiment

Curating a dataset is half the job; the other half is running something against it and scoring the result. Both Langfuse SDKs ship a runner, and their shapes differ.

Python (SDK 4.x): see monitoring-observability/references/experiments-api.md.

JS/TS (SDK 5.x): @langfuse/client exposes the runner directly on a fetched dataset.

import { LangfuseClient } from "@langfuse/client";

const langfuse = new LangfuseClient();
const dataset = await langfuse.dataset.get("my-evaluation-dataset");

const result = await dataset.runExperiment({
  name: "Retrieval quality",
  task: myTask,               // (params) => Promise<any>
  evaluators: [myEvaluator],  // per-item: (params) => Promise<Evaluation | Evaluation[]>
});

| Type | Scores | Use for | |---|---|---| | Evaluator | one item | Per-example quality (faithfulness, relevance) | | RunEvaluator | the whole run | Aggregate assertions — pass rate, mean score, regression checks | | Evaluation | — | { name, value, comment?, metadata?, dataType?, configId? } |

A per-item Evaluator cannot see the other items, so anything comparative belongs in a RunEvaluator. createEvaluatorFromAutoevals wraps an autoevals scorer instead of hand-writing one, and RegressionError is thrown when a run regresses against a configured baseline — catch it to fail CI on a quality drop rather than only on an exception.

Full JS surface: monitoring-observability/references/langfuse-js-v5.md.

Evaluations

See test-cases.json for 9 test cases across all categories.

Upstream coverage (do not restate)

| Topic | First-party source | | ----- | ------------------ | | Dataset schema validation (JSON Schema, field constraints) | https://json-schema.org and https://zod.dev | | Duplicate detection via embeddings, cosine similarity | https://github.com/pgvector/pgvector | | Scheduled backup automation (cron workflows, commit bots) | https://docs.github.com/actions/using-workflows/events-that-trigger-workflows#schedule | | Dataset runs, experiment scoring, annotation queues | https://langfuse.com/docs/datasets | | Backup and restore mechanics for postgres datasets | https://www.postgresql.org/docs/current/backup.html |

House thresholds these must enforce: references/ork-delta.md.

Related Skills

• ork:rag-retrieval - Retrieval evaluation using golden dataset
• ork:monitoring-observability - Langfuse tracing patterns for curation workflows
• ork:testing-llm - Evaluation harnesses that consume golden datasets
• ork:testing-unit - Unit testing patterns and strategies

Capability Details

curation

Keywords: golden dataset, curation, content collection, annotation, quality criteria

Solves:

• Classify document content types for golden dataset
• Run multi-agent quality analysis pipelines
• Generate test queries for new documents

management

Keywords: golden dataset, backup, restore, versioning, disaster recovery

Solves:

• Backup and restore golden datasets with JSON
• Regenerate embeddings after restore
• Automate backups with CI/CD

validation

Keywords: golden dataset, validation, schema, duplicate detection, quality metrics

Solves:

• Validate entries against document schema
• Detect duplicate or near-duplicate entries
• Analyze dataset coverage and distribution gaps