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generate-synthetic-data

Generates dimension-based synthetic inputs to evaluate LLM pipelines via tuples and queries.

Updated Mar 17, 2026
Search Tags:#testing#llm#evaluation#data#generate#synthetic#dimension
One-click install
npx skills add https://github.com/Avi977/ace-claude-toolkit --skill generate-synthetic-data-avi977
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generate-synthetic-data
Source: https://github.com/Avi977/ace-claude-toolkit/tree/main/skills/generate-synthetic-data
Command: npx skills add https://github.com/Avi977/ace-claude-toolkit --skill generate-synthetic-data-avi977

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.

Core Features & Use Cases

  • Dimension-based tuple generation to cover failure space across inputs, personas, and scenarios.
  • Draft 20 tuples per domain and convert them into realistic, natural-language queries.
  • Expand to additional random combinations and run the full pipeline to produce complete traces.
  • Optional: integrate real user data to fill gaps or validate synthetic coverage when available.

Quick Start

Define at least three dimensions, draft 20 tuples, translate each to a realistic query, and run the synthetic-data pipeline to generate traces.

Frequently Asked Questions about generate-synthetic-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate synthetic data to evaluate an LLM pipeline?▼

Generate synthetic data by defining at least three dimensions, drafting 20 tuples per domain, and translating them into realistic natural-language queries to produce complete evaluation traces.

What is dimension-based synthetic data generation for LLMs?▼

Dimension-based synthetic data generation creates diverse test inputs by defining dimensions across inputs, personas, and scenarios to systematically cover the LLM failure space.

When should I use synthetic data instead of real user data for evaluation?▼

Use synthetic data when bootstrapping an evaluation dataset, when real user data is sparse, or when stress-testing specific failure hypotheses across failure modes.

When should I not use synthetic data generation for LLM evaluation?▼

Avoid synthetic data generation when you already have 100 or more representative real traces, as stratified sampling should be used instead, or when the task is simply collecting production logs.

Can I integrate real user data into the synthetic data generation pipeline?▼

Yes, you can optionally integrate real user data to fill coverage gaps or validate the synthetic coverage when available within the pipeline.

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