Copycat Market Research

Turn a selected spike set into bounded, source-backed product and market evidence. Store copy, official-web claims, observed product behavior, user evidence, third-party signals, and inference are different evidence classes.

Research selected products

1. Read the current run.json, shortlist, selection, and compact review evidence. Research only the selected apps and decision-relevant substitutes.

2. Resolve each app to an official domain. Prefer developer links in store metadata, then search by product name, developer, and stable app ID. Verify identity with developer/legal names, reciprocal store links, package references, or consistent privacy-policy ownership. Record ambiguity instead of selecting a convenient domain.

3. Inspect a bounded set of high-value official pages: product/features, pricing, use cases/templates, docs/help, integrations, changelog, and policy pages relevant to a known risk. Stop when the evidence buckets below are covered; do not crawl the site.

4. Search selectively for non-app substitutes, manual workarounds, community demand, comparison alternatives, public tutorials, and reachable acquisition surfaces. Add a query only to close a named evidence gap.

5. Reconcile official claims with store metadata and review evidence. A feature advertised by the company is official_claim; it becomes user_validated only with usage evidence.

6. Read references/product-profile.md. Scaffold and validate each selected app profile, then build portfolio views:

   python3 <skill-dir>/scripts/product_profile.py init <app-folder>
   python3 <skill-dir>/scripts/product_profile.py validate <app-folder>/product-profile.json
   python3 <skill-dir>/scripts/product_profile.py portfolio --apps-root <workspace>/.copycat/apps --run-dir <run-dir>