product-demand-research

v2026.09.25

Test product-demand hypotheses using public customer language, search and social signals, competitor offers, reviews, comments, creator content, and social-commerce evidence before committing to a product, launch, offer, or campaign.

GitHub
安装命令
npx skhub add gooseworks-ai/product-demand-research
Markdown
SKILL.md

Product Demand Research

Assess whether a product problem, promise, audience, or concept has meaningful evidence behind it.

Inputs

  • Product idea or current product, target market, geography, price/offer assumptions, competitors, and the decision at stake.
  • Hypotheses to test: problem frequency, urgency, current alternatives, willingness signals, objections, or message fit.

Workflow

  1. Write the hypotheses and what evidence would support or weaken each one.
  2. Collect customer language from reviews, forums, search results, social posts, video transcripts, and comments. Use scrapecreators-api, comment-mining, and transcript-intelligence where appropriate.
  3. When the category is sold through social commerce, add a social-commerce evidence pass: TikTok Shop search and category results, product details, reviews, creator showcases, and relevant Amazon Shop pages. Record prices, offers, ratings, review themes, creator-product adjacency, and visible assortment changes.
  4. Map alternatives: direct competitors, substitutes, workarounds, and doing nothing. Record offers, pricing, proof, repeated complaints, and audience response.
  5. Separate attention signals from buying signals. Views and likes show interest; questions about price, availability, comparison, results, repeat use, and credible purchase or review evidence are closer to demand. Shop presence and creator showcases are not proof of sales.
  6. Score each hypothesis by evidence strength, consistency across sources, recency, source diversity, and distance from an observable purchase decision.
  7. Recommend the cheapest next validation: interview, landing-page test, waitlist, offer test, creator test, merchandising test, or ad-message test.

Output

  • Decision summary: proceed, refine, test further, or weak evidence.
  • Hypothesis evidence table with supporting and contradicting sources.
  • Customer language, use cases, triggers, alternatives, objections, willingness signals, and social-commerce evidence when relevant.
  • Market/competitor observations without unsupported market-size claims.
  • Recommended validation plan and success thresholds.

This is directional research, not proof of product-market fit or a substitute for first-party sales data.

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版本
最新版本元数据

版本

v2026.09.25

发布时间

2026年9月25日

分类

未分类

许可证

MIT

源路径

skills/research/composites/product-demand-research

默认分支

main

最新提交

a9f4676

Tree SHA

99d4730