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Agent skill

Product Research

by hashgraph-online in hashgraph-online/awesome-codex-plugins

Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria.

Apache-2.0Auto-check passed

Install Product Research

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill product-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins product-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research .claude/skills/product-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
product-research
GitHub stars
1.3k
Token cost
~706 tokens
SKILL.md length
255 words
Files
1
Skills in repo
716
Repo updated
First seen
Licence
Apache-2.0

At a glance

Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria.

  • Works in 5 steps: Initial scan → Financial trend check → Listing quality check → …
  • The user wants a serious viability review for a keyword
  • SKILL.md covers Core criteria, Workflow, Output format and Rabbit-hole expansion
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Product Research is an agent skill from hashgraph-online/awesome-codex-plugins. 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.

Its SKILL.md is about 710 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Model Context Protocol. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • The user wants a serious viability review for a keyword
  • Adjacent niche expansion

Example prompts

  • “/product-research”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Initial scan
  2. Financial trend check
  3. Listing quality check
  4. Keyword validation
  5. Profitability estimate

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Research loads about 706 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 255 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~706

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its Apache-2.0 licence (© hashgraph-online). 255 words, ~706 tokens.

Download SKILL.mdSave it as .claude/skills/product-research/SKILL.md (or your agent's skills folder).
name
product-research
description
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.

Amazon FBA Product Research

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:

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

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Product Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Product Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Product Research this skillhashgraph-online/awesome-codex-plugins1.3k—~706Automated safety check: PassApache-2.0
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
MCP Server BuildershareAI-lab/learn-claude-code78k4 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
Figma use_figma Plugin API Ruleswarpdotdev/warp65k4 repos~4.4kAutomated safety check: PassAGPL-3.0
Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills8.5k6 repos~3.2kAutomated safety check: NotesApache-2.0

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Works with

Questions about Product Research

What does Product Research do?

Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria. Product Research is an agent skill from hashgraph-online/awesome-codex-plugins. Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria.

When should I use Product Research?

Product Research fits situations like: the user wants a serious viability review for a keyword; adjacent niche expansion.

How do I install Product Research in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill product-research -a claude-code`. Or copy the skill folder (plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research in hashgraph-online/awesome-codex-plugins) into .claude/skills/product-research in your project. Claude Code loads it when a task matches its description.

How do I install Product Research in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill product-research -a codex`. Or copy the skill folder (plugins/BlockchainHB/launchfast_codex_plugin/skills/product-research in hashgraph-online/awesome-codex-plugins) into .agents/skills/product-research in your project. Codex loads it when a task matches its description.

Can I use Product Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill product-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-research, .gemini/skills/product-research, .github/skills/product-research and .opencode/skills/product-research in your project.

What does Product Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Product Research is instructions for the agent only.

Does Product Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Product Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Product Research use?

Product Research is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Research use?

About 706 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Product Research?

Skills that share tags, products or a category with Product Research: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Research?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.