Individual skill
synthetic-data
Generate high-quality training data with models — pipelines, filtering, and validation for synthetic datasets that actually help.
skills/synthetic-data/SKILL.md
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## Overview
Synthetic data is training data generated by models rather than collected from
humans or the world: instruction-response pairs, reasoning traces, code with
tests, preference judgments, tool-use trajectories. It's the engine behind most
modern post-training — distillation, self-improvement loops, and domain adaptation
all run on synthetic data. Done well, it converts a strong teacher model's
capabilities into targeted training signal for a student. Done poorly, it produces
fluent garbage that teaches the student the teacher's failure modes.
The core discipline is treating synthetic generation as a data pipeline with
quality gates, not as "prompt a big model and hope." Every synthetic dataset needs:
a generation step with controlled diversity, a verification step that filters for
correctness (execution, entailment checks, cross-model agreement), and a validation