Amazon FBA Product Research

SkillDev tools

Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria. Use when the user wants a serious viability review for a keyword, niche, or adjacent niche expansion.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Amazon FBA Product Research skill

What this skill tells your AI

The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research/SKILL.md and read by ahel’s review.

This skill is the deeper, criteria-driven version of launchfast-product-research.

Core criteria

Evaluate every market against these baselines:

CriteriaThreshold
Total niche revenue> $200,000/month
Average price>= $25, ideally >= $40
Average reviews<= 500
Revenue per seller>= $5,000/month
Top-seller dominancetop 2-3 sellers < 50% of revenue
Search volumemust exist
Estimated margin>= 30% before ad costs

Large-market exception:

  • If niche revenue is above $1M, higher review counts can still be acceptable when multiple sellers under 200 reviews are doing strong revenue.

Workflow

1. Initial scan

Run:

research_products(keyword="<keyword>", focus="balanced", product_limit=20)

Extract:

  • search volume
  • average price
  • average reviews
  • opportunity score
  • market grade
  • brand concentration
  • dominant brand
  • total niche revenue
  • average revenue per seller
  • top-seller share

2. Financial trend check

Run:

research_products(keyword="<keyword>", focus="financial", product_limit=20)

Look for:

  • growing vs stable vs declining products
  • average MoM growth
  • short-term momentum using 7d trend fields

3. Listing quality check

Run:

research_products(keyword="<keyword>", focus="titles", product_limit=10)

Look for:

  • low listing quality scores with high revenue
  • listing quality gaps
  • weak copy or obvious differentiation openings

4. Keyword validation

Pick 2-3 relevant ASINs and run:

amazon_keyword_research(asins=["ASIN1", "ASIN2", "ASIN3"], limit=20)

Evaluate:

  • keyword diversity
  • CPC and sponsored density
  • purchase rate
  • obvious ranking gaps

5. Profitability estimate

Present a conservative estimate:

Selling Price
- Amazon Fees (~15%)
- Manufacturing
- Shipping
= Estimated Profit per Unit
= Estimated Margin %

If manufacturing cost is unknown, say so and state the assumption used.

Output format

Use a scorecard first:

## Market Scorecard: [keyword]

| Criteria | Threshold | Actual | Status |
|---|---|---|---|

Then include:

  • market grade
  • opportunity score
  • trend summary
  • verdict: VIABLE, MARGINAL, or NOT RECOMMENDED
  • concise rationale

Rabbit-hole expansion

Use adjacent-niche exploration only when it is helpful:

  • identify variations from the first result set
  • rerun research_products on the most promising adjacent keywords
  • keep the branching tight; do not explode the scope without user intent

Signals

GitHub stars
985
Forks
276
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
product-research-hashgraph-online
Source
github.com/hashgraph-online/awesome-codex-plugins