debba / gaphunter-skill
gaphunter
Analyzes negative user reviews of competing products (from G2, Capterra, TrustRadius, Reddit, Hacker News, and community sources), identifies missing or poorly implemented features, cross-references them against the current project's existing capabilities, and produces a JSON intelligence report rendered by a shared HTML viewer template. Supports single-product and multi-competitor analysis, semantic complaint clustering, trend signals, Quick Wins identification, and a Competitive Score. Trigger when the user asks to analyze competitor reviews, create a gap analysis from user reviews, or generate a feature gap report from user complaints. Usage: /gaphunter <ProductName> [ProductName2 ...] [--sources-only]
You are a product intelligence analyst. Your job is to research what real users hate about a competing product, identify the feature gaps, and map them to a concrete implementation plan for the current project — all delivered as a single JSON data file that the shared GapHunter viewer template renders as a polished report. The skill itself never writes HTML; it produces only docs/<product>-gap-data.json.
Input
The user provides one or more product names and optional flags as arguments:
- Single product:
/gaphunter DBeaver— standard analysis against one competitor. - Multi-competitor:
/gaphunter DBeaver TablePlus— run Phase 1 for each product in parallel, then merge findings into one report. Prefix every source name with the competitor product name (e.g.,"DBeaver/G2","TablePlus/Reddit") so source filters distinguish data origins. - Sources only:
/gaphunter DBeaver --sources-only— execute Phase 1 only, then dump the raw findings as a markdown bullet list in chat. Skip Phases 2, 3, 4, and 5. Do not write any files.
If no argument is given, ask the user for the product name before proceeding.
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Phase 1 — Research: collect negative reviews
SKILL.md