Dataset Curation Specialist

Guide the end-to-end curation of high-quality datasets for machine learning. Covers data collection strategies, cleaning pipelines, annotation workflows, bias auditing, deduplication, and dataset documentation following data-centric AI principles.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.3

Completeness (10%)

8.4

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.2

Best for

▸Building high-quality training datasets for LLM fine-tuning with proper annotation workflows
▸Implementing deduplication and quality filtering pipelines for large-scale web scraped data
▸Designing bias audit frameworks for computer vision datasets across demographic groups
▸Creating synthetic data generation strategies with human validation loops
▸Establishing dataset versioning and documentation standards following datasheet protocols

What you'll get

●Detailed data collection strategy with web scraping specifications, API rate limits, and provenance tracking requirements
●Multi-stage cleaning pipeline with deduplication thresholds, quality filters, and PII removal protocols
●Annotation taxonomy with inter-annotator agreement targets, quality control procedures, and labeling guidelines

Not designed for ↓

×Training machine learning models or implementing neural network architectures
×Real-time data processing or streaming analytics pipelines
×Database administration or production data warehouse management
×Business intelligence dashboards or executive reporting

Expects

Clear dataset requirements including target task, domain, quality metrics, annotation budget, and performance objectives.

Returns

Comprehensive dataset curation strategy with collection pipelines, cleaning workflows, annotation guidelines, quality metrics, and documentation templates.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

dataset-curationdata-qualityannotationlabelingbias-auditingdata-cleaningdeduplicationdatasheetstraining-datadata-centric-aifine-tuning-datasnorkel

Research Foundation: 7 sources (4 academic, 2 official docs, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

Python Data AnalystGold

Works well with

Data Labeling Strategy AdvisorGold Data Quality EngineerGold Feature Engineering StrategistGold Synthetic Data GeneratorGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

LLM Fine-Tuning StrategistGold LoRA Dataset CuratorGold Computer Vision Pipeline ArchitectGold

Common Workflows

LLM Fine-tuning Data Pipeline

End-to-end workflow from raw data curation through specialized fine-tuning dataset preparation to model training

dataset-curation-specialist→LoRA Dataset Curator→LLM Fine-Tuning Strategist

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