Design: /teardown Skill — Competitive UX Teardown

Status: Backlog (jstack v0.3.0 candidate) Author: Joe + Claude Date: 2026-04-06

Problem

Competitive UX teardowns are one of the highest-value research activities before building a product, but the process is manual, tedious, and inconsistent. For Word Freak, Joe manually tore down 5+ competing apps (Scrabble GO, WWF, Wordscapes, Woogles, NYT Crossplay) across multiple sessions. The output was excellent but the process was ad-hoc — no repeatable workflow, no standardized report format, no automated discovery.

A /teardown skill would make this a one-command operation for any product category.

User Story

/teardown "word games like Scrabble"

The skill discovers competing apps, scrapes their public-facing UX, builds feature matrices, and produces a structured teardown report — all saved to a Research topic folder.

Workflow

Phase 1: Discovery (automated)

Given a topic/app description, the skill:

1. App Store search — Browse Apple App Store and Google Play Store for the category. Extract: app name, developer, rating, review count, price/IAP model, screenshots.
2. Web search — Find web-based competitors, review articles ("best X apps 2026"), Reddit threads, Product Hunt listings.
3. Deduplication — Merge results, identify the top 5-10 competitors by relevance + popularity.
4. User confirmation — Present the discovered list. User can add/remove before proceeding.

Phase 2: Scrape (browse daemon)

For each competitor:

1. App store listing — Full screenshot gallery, description, version history, review highlights.
2. Marketing site — Browse the product's website. Screenshot hero, pricing, feature pages.
3. Web app (if available) — Navigate the actual product. Screenshot key flows:
   • Onboarding / first-time experience
   • Core gameplay / interaction loop
   • Settings / customization
   • Social / multiplayer features
   • Monetization touchpoints (paywall, ads, IAP prompts)
4. Mobile app (stretch goal) — If the app has a mobile web version, browse it in mobile viewport. If native-only, rely on app store screenshots + review videos.

Phase 3: Analysis (agent team)

Spawn 3 research agents in parallel:

1. Feature Matrix Agent — Build a structured comparison table across all competitors. Columns: app name. Rows: every feature discovered. Values: yes/no/partial + notes.
2. UX Pattern Agent — Identify recurring UX patterns across competitors (onboarding flows, tutorial styles, reward loops, social hooks). Flag anti-patterns.
3. Differentiation Agent — Given the user's product concept, identify: (a) table-stakes features everyone has, (b) differentiators no one is doing well, (c) "steal this" moments worth copying, (d) "avoid this" anti-patterns.

Phase 4: Synthesis

Merge the three reports into a README.md with:

• TL;DR decision matrix (table format)
• Ranked competitor list with 1-line summaries
• Feature matrix (the big table)
• UX pattern catalog with annotated screenshots
• "Steal List" — specific things to copy with source attribution
• "Avoid List" — specific things competitors do poorly
• Differentiation opportunities
• Links to individual agent reports

Phase 5: Output

Save everything to a Research topic folder:

Research/
  {topic}-ux-teardown/
    README.md                    # Synthesis
    report-feature-matrix.md     # Feature comparison table
    report-ux-patterns.md        # Pattern catalog
    report-differentiation.md    # Opportunities analysis
    screenshots/                 # Organized by competitor
      scrabble-go/
        onboarding-1.png
        gameplay-1.png
        monetization-1.png
      words-with-friends/
        ...
    competitors.json             # Structured data for all discovered competitors

Technical Dependencies

• Browse daemon (jstack/browse) — for web scraping and screenshots
• Deep researcher agents — for Phase 1 discovery and Phase 3 analysis
• App Store scraping — needs browse daemon with anti-bot stealth (already in jstack v0.2.0)
• Screenshot annotation — stretch goal, could use Excalidraw MCP or simple labels

Key Design Decisions

Mobile-first or web-first?

Start web-first. Mobile app teardowns require either:

• A physical device + Appium (complex, fragile)
• App store screenshots + review videos (good enough for v1)
• Mobile web viewport in the browse daemon (covers responsive web apps)

v1 should focus on what the browse daemon can already do — web apps and mobile web viewports. Native app teardowns via screenshots + reviews. True mobile automation is a v2 feature.

How much user interaction?

Minimal. The skill should run mostly autonomously after the initial topic input and competitor confirmation. The user reviews the final output, not every intermediate step.

How does this relate to existing research?

The skill should check for existing research in the Research folder and offer to update/extend rather than starting from scratch. If word-freak-game-ux-teardowns/ already exists, the skill should read it and identify what's stale or missing.

Scope for v1

• Discovery via web search (not direct app store scraping)
• Web-based competitor scraping via browse daemon
• Feature matrix generation
• UX pattern identification
• Synthesis README
• Screenshot capture and organization

Stretch Goals (v2+)

• Direct App Store / Google Play API scraping
• Mobile viewport screenshots for responsive apps
• Video recording of user flows (browse daemon screen capture)
• Automated re-runs to detect competitor changes over time
• Integration with the /brainstorm skill for product strategy sessions
• Price tracking / monetization model analysis

Prior Art

• The manual teardown at Research/word-freak-game-ux-teardowns/ is the gold standard for output quality. The skill should produce equivalent or better output.
• jstack's /qa skill demonstrates the browse-screenshot-analyze loop pattern.
• jstack's /design-review skill demonstrates the iterative visual analysis pattern.

Effort Estimate