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mguozhen

Amazon Hot Products

by mguozhen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install 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,...
README (SKILL.md)

Amazon Hot Products & Trending Scout

Track Amazon's real-time hot searches, new releases, and rising categories. Spot trending products before they become saturated — 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 — broad (Electronics) or specific (Wireless Earbuds)
  • BSR data — paste BSR rankings if you have them
  • Search term data — trending search terms from Seller Central
  • Time period — last 7/30/90 days
  • Market — 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 🔥 Strong
BSR improved 20–50% in 30 days ✅ Moderate
BSR stable ⚪ 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 \x3C5 = Skip or monitor

Output Format

  1. Trending Opportunities — ranked list with Blue Ocean Score
  2. Category Heat Map — which categories are rising vs. cooling
  3. Early Entry Windows — products with \x3C200 reviews but rising BSR
  4. Avoid List — saturated trends (too late to enter profitably)
  5. 30-Day Watch List — opportunities to monitor for next scan

Rules

  1. Always check review count before calling a trend "early" — >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 — timing matters
  5. Always pair trend data with estimated margin — demand means nothing if margins are thin
Usage Guidance
This skill is instruction-only and internally consistent with its purpose. Before installing: (1) Do not paste credentials, CSV files containing Seller Central login tokens, or other secrets — the skill expects pasted data only. (2) Confirm how your agent implementation handles 'memory' (what is stored, retention policy, and how to delete saved items) before using 'hot save'. (3) If you want additional safety, restrict the agent's allowed tools (the SKILL.md lists Bash as an allowed tool) so it cannot execute shell commands in your environment. (4) If you plan to use actual Seller Central/Brand Analytics data, consider extracting and sanitizing only the fields needed (search terms, counts, BSR) rather than full reports. (5) Optionally review the linked GitHub homepage to ensure there is no hidden code you need to be aware of before granting broader runtime privileges.
Capability Analysis
Type: OpenClaw Skill Name: amazon-hot-products Version: 1.0.0 The skill bundle is a set of instructions for an AI agent to perform Amazon market research and trend analysis. It contains no executable code or malicious scripts, focusing entirely on a framework for evaluating product demand and competition. While it requests 'Bash' tool access, no actual shell commands are provided, and the logic is strictly aligned with the stated purpose of scouting trending products.
Capability Assessment
Purpose & Capability
Name/description (scout Amazon trends) matches the SKILL.md: it defines commands, scoring, and required user-supplied data (BSR, search terms, category). There are no unrelated dependencies or credentials requested.
Instruction Scope
SKILL.md instructs the agent to analyze user-provided trend data and apply heuristics (BSR, search volume, reviews, seasonality). It does not direct reading system files, environment variables, or external endpoints beyond asking the user to paste data. The only persistence hint is 'hot save' which saves opportunities to agent memory — expected for this use case.
Install Mechanism
No install spec and no code files — instruction-only skill. Nothing will be downloaded or written to disk by the skill itself.
Credentials
The skill requests no environment variables, credentials, or config paths. It references Seller Central data conceptually but does not ask the platform for Seller Central credentials — user supplies data manually if desired.
Persistence & Privilege
always:false (normal). The skill includes a 'hot save' command implying use of agent memory to persist opportunities; this is proportionate but users should be aware saved opportunities may be retained by the agent's memory system and could contain sensitive business data.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install amazon-hot-products
  3. After installation, invoke the skill by name or use /amazon-hot-products
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of "amazon-hot-products" skill. - Scout Amazon trending products, hot searches, new releases, and emerging categories without needing an API key. - Provides commands to track hot products, search trends, BSR movers, seasonal trends, and generate weekly reports. - Introduces framework to evaluate trends using search volume, BSR velocity, review rates, and seasonality. - Includes "Blue Ocean Score" system for ranking opportunities by demand, competition, margin, and differentiation. - Structured output for trending opportunities, category heat map, early-entry products, avoid list, and watch list.
Metadata
Slug amazon-hot-products
Version 1.0.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Amazon Hot Products?

Scout Amazon trending products, hot searches, new releases, and rising categories to find blue ocean opportunities early. Triggers: hot products, hot search,... It is an AI Agent Skill for Claude Code / OpenClaw, with 174 downloads so far.

How do I install Amazon Hot Products?

Run "/install amazon-hot-products" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Amazon Hot Products free?

Yes, Amazon Hot Products is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Amazon Hot Products support?

Amazon Hot Products is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Amazon Hot Products?

It is built and maintained by mguozhen (@mguozhen); the current version is v1.0.0.

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