LoRA Dataset Curator

Designs, builds, and validates high-quality training datasets for LoRA fine-tuning of large language models, applying data-centric AI principles and instruction-tuning best practices to maximize model performance per training example.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.65

Research Quality (15%)

8.7

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.4

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.4

Best for

▸Building high-quality instruction datasets for domain-specific LoRA fine-tuning with 500-10,000 examples
▸Creating synthetic training data using stronger models while maintaining quality control and human validation
▸Designing seed examples and filtering pipelines for parameter-efficient fine-tuning of coding, writing, or reasoning tasks
▸Validating dataset quality using data-centric AI principles before expensive LoRA training runs
▸Optimizing training data composition and format for specific LoRA frameworks like PEFT, QLoRA, or Axolotl

What you'll get

●Complete dataset curation strategy with seed example templates, synthetic generation prompts, and quality scoring rubrics tailored to specific domain and model requirements
●Structured quality filtering pipeline with automated checks, human review sampling protocols, and data validation frameworks optimized for parameter-efficient training
●Domain-specific seed examples with negative cases, edge case coverage, and format specifications ready for scaling via self-instruct methodology

Not designed for ↓

×Training datasets for full model pre-training or large-scale foundation model training
×Computer vision or multimodal dataset curation (focuses specifically on text-based LLM training)
×Real-time data collection or web scraping for training data
×Running the actual LoRA fine-tuning process or hyperparameter optimization

Expects

Clear fine-tuning objectives, target model specifications, example quality requirements, and constraints on dataset size, format, and domain coverage.

Returns

Structured dataset curation plan with seed examples, quality filtering criteria, synthetic data generation prompts, and validation frameworks ready for LoRA training.

Evidence Policy

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

lorafine-tuningdataset-curationinstruction-tuningpeftqloradata-qualitysynthetic-datallm-trainingmachine-learningdata-centric-ai

Research Foundation: 8 sources (3 paper, 3 official docs, 1 web, 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

Works well with

LLM Evaluation Framework DesignerGold LLM Fine-Tuning StrategistGold Prompt Engineering StrategistGold Synthetic Data GeneratorGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

LoRA Fine-Tuning SpecialistGold PyTorch Deep Learning EngineerPlatinum

Common Workflows

Complete LoRA Fine-tuning Pipeline

End-to-end workflow from dataset design through training to evaluation for domain-specific LoRA adapters

lora-dataset-curator→LoRA Fine-Tuning Specialist→LLM Evaluation Framework Designer

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