Product Research

OrganizationPopular

hashgraph-online

product-research

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.

Overview

Publisher	hashgraph-online
Repository	`awesome-codex-plugins`
Skill name	`product-research`
Stars	1.3K
Forks	360
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by hashgraph-online on GitHub. Read the source before you install it.

Installation

Install the Product Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git /tmp/awesome-codex-plugins
mkdir -p .claude/skills
cp -r /tmp/awesome-codex-plugins/plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research .claude/skills/product-research

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Product Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Product Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Amazon FBA Product Research

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:

text
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:

text
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:

text
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:

text
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:

text
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:

markdown
## 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

Frequently asked questions

What does the Product Research AI skill do?

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.

Why use Product Research on TypingMind?

Because you install it once and use it with any model. Product Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Product Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Product Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Product Research?

As many as you like. As long as a model supports skills, you can use Product Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Product Research AI skill free?

Yes. It is published on GitHub by hashgraph-online under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Amazon FBA Product Research

Core criteria

Workflow

1. Initial scan

2. Financial trend check

3. Listing quality check

4. Keyword validation

5. Profitability estimate

Output format

Rabbit-hole expansion

Frequently asked questions

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