Skip to content
cccskills
FreePublished on GitHub

product-demand-research

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.

The English summary is being prepared. Showing the original description.

Run score Not tested

See how to install

Files included (2)

  • SKILL.md2.5 KB
  • skill.meta.json398 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.

The English summary is being prepared. Showing the original description.

gooseworks-ai/goose-skills1.3KUpdated Oct 24, 2026

Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.

The English summary is being prepared. Showing the original description.

gooseworks-ai/goose-skills1.3KUpdated Oct 24, 2026

For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.

The English summary is being prepared. Showing the original description.

gooseworks-ai/goose-skills1.3KUpdated Oct 24, 2026

The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.

The English summary is being prepared. Showing the original description.

gooseworks-ai/goose-skills1.3KUpdated Oct 24, 2026

Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).

The English summary is being prepared. Showing the original description.

gooseworks-ai/goose-skills1.3KUpdated Oct 24, 2026

Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.

The English summary is being prepared. Showing the original description.

gooseworks-ai/goose-skills1.3KUpdated Oct 24, 2026

See all skills by gooseworks-ai

Report a problem with this skill