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demand-intelligencelisted

Use when researching whether a product niche/concept has real demand signal before committing further research budget to it. Generates precise Google Trends data requests, ingests human-supplied CSV exports, and reads the deterministic demand qualification the engine computes. Use before Amazon discovery for a new concept, or when deciding which of several concepts to prioritize.
zaidadaqqa/amazon-opportunity-research-skills · ★ 1 · Data & Documents · score 72
Install: claude install-skill zaidadaqqa/amazon-opportunity-research-skills
# Demand Intelligence Establishes whether a concept has an observable Google Trends search-interest signal, using the engine's existing human-in-the-loop demand-recording layer. This skill drives four CLI commands only: `hunt demand-request`, `hunt demand-set`, `hunt demand-show`, `hunt demand-rank`. It never computes qualification itself and never fetches Trends data itself — Trends has no API here, and the recording service is structurally network-free (see `references/demand-intelligence.md` SVC-06). Full rule detail lives in two reference files — read them before running this skill for the first time, and re-check them whenever output looks surprising: - `references/google-trends-protocol.md` — the request/record workflow, file-identification rules, and the "never do this" list. - `references/demand-intelligence.md` — exact qualification thresholds, recording rules, and CSV parsing behavior. --- ## Operating procedure ### 1. Check what's already recorded ``` hunt demand-show <concept_query> ``` If a recent, relevant signal already exists, do not re-request Trends data for the same concept — reuse it. `NOT_RECORDED` means proceed to step 2. ### 2. Decide the minimum data actually needed Per `google-trends-protocol.md` §1: decide which export(s) — interest over time, related queries, rising queries — you actually need for the decision at hand, and how many `--compare` terms (0-2, and only with a reason). Do not request more than the decision requires. ### 3.