
Synthetic Market Research
from agi-super-team31
Run fast, low-cost market research using LLM-generated synthetic survey responses and Semantic Similarity Rating (SSR) to estimate purchase intent, concept appe
What it does
This skill automates directional market research by generating persona‑conditioned free‑text survey responses with an LLM and converting those responses into Likert-like probability ratings using Semantic Similarity Rating (SSR). It produces PMF distributions, mean scores, segment breakdowns, qualitative themes, and a methodology summary saved as a markdown report.
When to use it
Use when you need quick, inexpensive validation of product concepts, pricing, or purchase intent without recruiting real panels — for early-stage founders, product managers, and researchers who want directional signals before running real user studies. Also useful for A/B comparisons of concepts or pricing tiers.
What's included
- Scripts: example Python SSR pipeline (references present in the skill directory)
- References: SSR_METHODOLOGY.md and example runs for concept and pricing tests
- Instructions: persona generation, prompt templates, SSR reference sets, recommended sampling (2 responses/persona), and how to save results to output/research_<concept>_<timestamp>.md
Compatible agents
Works with agents that can call LLM APIs and run Python/batch scripts (OpenAI/Anthropic-backed agents, Claude, Copilot-style automation). It expects access to an LLM and the semantic-similarity-rating package.
Tags
Information
- Repository
- agi-super-team
- Stars
- 31
- Installs
- 0