🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί Amazon Hot Products
πŸ¦€ ClawHub

Amazon Hot Products

by @mguozhen

Scout Amazon trending products, hot searches, new releases, and rising categories to find blue ocean opportunities early. Triggers: hot products, hot search,...

Versionv1.0.0
Installs1
#ecommerce#legal#real-estate#automation
TERMINAL
clawhub install amazon-hot-products

πŸ“– About This Skill

name: amazon-hot-products description: "Scout Amazon trending products, hot searches, new releases, and rising categories to find blue ocean opportunities early. Triggers: hot products, hot search, hot new releases, hot movers, hot seasonal, hot compare, hot report, hot save" allowed-tools: Bash metadata: openclaw: homepage: https://github.com/mguozhen/amazon-hot-products

Amazon Hot Products & Trending Scout

Track Amazon's real-time hot searches, new releases, and rising categories. Spot trending products before they become saturated β€” find blue ocean opportunities early.

Commands

hot products                    # scan trending products across categories
hot search [category]           # analyze hot search terms in category
hot new releases [category]     # find new releases with early traction
hot movers [category]           # find products with rapid BSR improvement
hot seasonal                    # identify upcoming seasonal trends
hot compare [cat1] [cat2]       # compare trend momentum between categories
hot report                      # generate weekly trend report
hot save [opportunity]          # save a trend opportunity to memory

What Data to Provide

  • Category β€” broad (Electronics) or specific (Wireless Earbuds)
  • BSR data β€” paste BSR rankings if you have them
  • Search term data β€” trending search terms from Seller Central
  • Time period β€” last 7/30/90 days
  • Market β€” US, UK, DE, JP, etc.

    • No API key needed. Provide data verbally or paste raw numbers.

    Trend Identification Framework

    Signal 1: Search Volume Surge

  • Search term appears in Amazon's "Hot New Keywords" (from Seller Central Brand Analytics)
  • Week-over-week search volume growth >20%
  • Low current competition (fewer than 1,000 results for exact match)

    • Signal 2: BSR Velocity

    | BSR Movement | Signal Strength | |---|---| | BSR improved >50% in 30 days | πŸ”₯ Strong | | BSR improved 20–50% in 30 days | βœ… Moderate | | BSR stable | βšͺ Neutral | | BSR declining | ❌ Avoid |

    Signal 3: Review Accumulation Rate

  • New products getting 50+ reviews in first 60 days = high demand signal
  • Multiple competitors launching simultaneously = category heating up

    • Signal 4: Seasonal Calendar

    | Month | Trending Categories | |---|---| | Jan–Feb | Fitness, Organization, New Year | | Mar–Apr | Outdoor, Garden, Spring Cleaning | | May–Jun | Graduation, Father's Day, Summer | | Jul–Aug | Back to School, Pool/Beach | | Sep–Oct | Halloween, Fall Home | | Nov–Dec | Holiday Gifts, Holiday Decor |

    Blue Ocean Score (1–10)

    Score each trending product opportunity:

  • Demand (1–3): Search volume trend direction
  • Competition (1–3): # of sellers, review counts, listing quality
  • Margin (1–2): Estimated price point vs. likely COGS
  • Differentiation (1–2): Can you improve on existing products?

    • Score 7+ = Enter aggressively Score 5–6 = Enter cautiously with differentiation Score <5 = Skip or monitor

    Output Format

    1. Trending Opportunities β€” ranked list with Blue Ocean Score 2. Category Heat Map β€” which categories are rising vs. cooling 3. Early Entry Windows β€” products with <200 reviews but rising BSR 4. Avoid List β€” saturated trends (too late to enter profitably) 5. 30-Day Watch List β€” opportunities to monitor for next scan

    Rules

    1. Always check review count before calling a trend "early" β€” >500 reviews = not early 2. Flag categories with known high return rates (electronics, clothing) 3. Distinguish between fad (short spike) and trend (sustained growth) 4. Note when seasonal peaks are approaching β€” timing matters 5. Always pair trend data with estimated margin β€” demand means nothing if margins are thin

    πŸ”’ Constraints

    1. Always check review count before calling a trend "early" β€” >500 reviews = not early 2. Flag categories with known high return rates (electronics, clothing) 3. Distinguish between fad (short spike) and trend (sustained growth) 4. Note when seasonal peaks are approaching β€” timing matters 5. Always pair trend data with estimated margin β€” demand means nothing if margins are thin