BayramAnnakov

BayramAnnakov / synthetic-market-research

Public

Agent skill for fast, cheap market research using LLM synthetic surveys + Semantic Similarity Rating (SSR). No API keys needed.

19
3
100% credibility
Found Feb 17, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An AI skill that simulates customer survey responses to quickly evaluate product concepts, pricing, and purchase intent using natural language analysis.

How It Works

1
🔍 Discover Fast Market Tester

You find a handy tool that lets you test product ideas with pretend customers in minutes, no real surveys needed.

2
📥 Add to Your AI Assistant

You simply add this skill to your AI coding helper so it's ready to use anytime.

3
🔧 Prepare the Word Analyzer

You install a one-time helper that understands customer opinions from their words.

4
Choose Your Testing Way
🚀
Quick Check

Test one idea fast with ready-made customer types.

💬
Step-by-Step Chat

Guide the tool interactively through your questions.

⚖️
Compare Options

Pit two to four ideas against each other side-by-side.

5
💡 Describe Your Product

You share details about your product concept, price, and target customers—it feels like chatting with a research expert.

6
👥 See Customer Reactions

The tool creates realistic customer profiles who give honest feedback, just like real people would.

📈 Receive Your Report

You get clear scores, group breakdowns, and key insights to decide your next steps confidently.

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Star Growth

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AI-Generated Review

What is synthetic-market-research?

This Python agent skill delivers fast market research using synthetic data: it generates target-market personas, has LLMs role-play their reactions to your product concept or pricing, then scores free-text responses on Likert scales via local semantic similarity—no API keys or survey costs needed. You get segment breakdowns, overall scores, and qualitative themes in minutes, claiming 90% correlation with real human surveys per the backing research paper. Slash commands like `/synthetic-market-research --quick "your idea"` kick off quick tests or comparisons right in tools like Claude Code or Cursor.

Why is it gaining traction?

It stands out for agent skills in GitHub Copilot, Claude, or VSCode setups by running fully local with sentence-transformers, dodging OpenAI/Anthropic API bills while delivering actionable reports on purchase intent. Developers dig the zero-setup npx install and comparative mode for A/B testing variants, plus validated methodology that beats direct LLM Likert prompting. As market research using synthetic data goes mainstream, this hooks solo founders iterating ideas without panels.

Who should use this?

Indie hackers validating SaaS pricing tiers before launch, product managers at startups comparing messaging variants, or growth leads screening concepts pre-MVP. Ideal for B2B tools like AI copilot agents where you need directional signals on appeal across demographics, not final validation.

Verdict

Grab it for early-stage hypothesis testing—solid docs, runnable demo, and MIT license make it low-risk at 14 stars—but the 1.0% credibility score flags it as immature; pair with real users before betting big. Promising agent GitHub repo for synthetic surveys if you're in the agent skills ecosystem.

(198 words)

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