Amazon FBA Product Research
SkillDev toolsDeep 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.
No other account needed.
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:
| Criteria | Threshold |
|---|---|
| Total niche revenue | > $200,000/month |
| Average price | >= $25, ideally >= $40 |
| Average reviews | <= 500 |
| Revenue per seller | >= $5,000/month |
| Top-seller dominance | top 2-3 sellers < 50% of revenue |
| Search volume | must 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_productson 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