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generate-synthetic-data

Generate synthetic traces with dimension-based tuples for LLM pipeline evaluation.

Updated Apr 19, 2026
Search Tags:#testing#generate#traces#bootstrapping#eval#synthetic-data#dimension-based
One-click install
npx skills add https://github.com/majidraza1228/eval-framework --skill generate-synthetic-data-majidraza1228
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generate-synthetic-data
Source: https://github.com/majidraza1228/eval-framework/tree/main/skills/generate-synthetic-data
Command: npx skills add https://github.com/majidraza1228/eval-framework --skill generate-synthetic-data-majidraza1228

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create diverse synthetic test inputs to explore and stress-test LLM pipelines when real data is scarce, enabling robust evaluation and faster iteration.

Core Features & Use Cases

  • Dimension-based tuple generation to systematically explore failure space
  • End-to-end workflow from tuple design to natural-language inputs and complete trace outputs
  • Use Case: accelerate eval readiness when real data is limited or biased

Quick Start

Define your failure-relevant dimensions and generate 20 diverse test tuples to bootstrap your eval dataset.

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 for LLM evaluation when real data is scarce?▼

To generate synthetic data for LLM evaluation, this Skill uses a dimension-based design to create approximately 20 unique tuples. It converts these tuples into natural-language inputs and outputs complete traces, including intermediate steps and tool calls, to bootstrap your eval dataset.

What is dimension-based synthetic trace generation for stress-testing pipelines?▼

Dimension-based synthetic trace generation is a method of systematically exploring failure space by defining failure-relevant dimensions. It generates diverse test tuples, converts them to natural-language inputs, and outputs complete traces to stress-test LLM pipelines.

Can I use synthetic traces to bootstrap an eval dataset without real user data?▼

Yes, you can use synthetic traces to bootstrap an eval dataset without real user data. This Skill is specifically applicable to data-scarce bootstrapping, generating approximately 20 diverse tuples to systematically explore the failure space and validate expected behavior.

How do I create test inputs that include intermediate steps and tool calls for LLM pipelines?▼

To create test inputs with intermediate steps and tool calls, this Skill generates diverse tuples and converts them into natural-language inputs. It outputs complete traces that capture the input, intermediate processing steps, tool calls, and final results of your LLM pipeline.

What's the best way to systematically explore failure space for LLM evaluations?▼

The best way to systematically explore failure space for LLM evaluations is using a dimension-based design. By defining failure-relevant dimensions, you can generate diverse test tuples that systematically cover edge cases and stress-test failure hypotheses across your pipeline.

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