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Agent skill

Launchfast Product Research

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

Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP.

Apache-2.0Auto-check passedAgent Workflows

Install Launchfast Product Research

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

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins launchfast-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/launchfast-product-research .claude/skills/launchfast-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
launchfast-product-research
GitHub stars
1.3k
Token cost
~618 tokens
SKILL.md length
232 words
Files
1
Skills in repo
716
Repo updated
First seen
Licence
Apache-2.0

At a glance

Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP.

  • Works in 4 steps: Run research → Extract core metrics per keyword → Score the market → …
  • The user wants to research 1-10 keywords
  • SKILL.md covers Inputs, Workflow and Notes
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Launchfast Product Research is an agent skill from hashgraph-online/awesome-codex-plugins. Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP. Use when the user wants to research 1-10 keywords, compare niches, or decide whether a product idea is a GO, INVESTIGATE, or PASS. Requires the LaunchFast MCP tool researchproducts.

Its SKILL.md is about 620 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering MCP servers. 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 to research 1-10 keywords
  • Decide whether a product idea is a GO

Example prompts

  • “/launchfast-product-research”

Workflow steps

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

  1. Run research
  2. Extract core metrics per keyword
  3. Score the market
  4. Present results

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

Launchfast Product Research loads about 618 tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 232 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~618

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). 232 words, ~618 tokens.

Download SKILL.mdSave it as .claude/skills/launchfast-product-research/SKILL.md (or your agent's skills folder).
name
launchfast-product-research
description
Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP. Use when the user wants to research 1-10 keywords, compare niches, or decide whether a product idea is a GO, INVESTIGATE, or PASS. Requires the LaunchFast MCP tool `research_products`.
argument-hint
[keyword 1], [keyword 2], [keyword 3]

LaunchFast Product Research

Use this skill for a fast, opinionated market scan across up to 10 keywords.

Inputs

If missing, ask once for:

  • keywords to research
  • optional target price band
  • optional competition tolerance

Workflow

1. Run research
  • Run research_products for each keyword.
  • Prefer parallel tool calls when researching more than one keyword.
  • Use focus="balanced" and product_limit=20 unless the user asks otherwise.
2. Extract core metrics per keyword

From the response, compute:

  • products analyzed
  • grade distribution
  • median monthly revenue
  • top product revenue
  • average or median price
  • average or median reviews
  • brand concentration
  • dominant brand
3. Score the market

Use this score out of 100:

text
score =
  (% of products graded B5 or better) * 30
+ (median revenue >= 8000 ? 30 : (median revenue / 8000) * 30)
+ (median reviews < 300 ? 20 : (300 / median reviews) * 20)
+ (median price between 18 and 60 ? 20 : 10)

Competition bands:

  • Low: median reviews < 200
  • Medium: median reviews 200-800
  • High: median reviews > 800

Verdicts:

  • GO: score >= 65
  • INVESTIGATE: score 40-64
  • PASS: score < 40
4. Present results

Always show:

  • a ranked summary table for all keywords
  • a short deep dive for the top 3

Use this table shape:

markdown
## Product Opportunity Scan

| Rank | Keyword | Score | Market Grade | Top Revenue | Avg Price | Competition | Verdict |
|------|---------|-------|--------------|-------------|-----------|-------------|---------|

For each top-3 keyword include:

  • products analyzed
  • grade distribution
  • revenue range
  • price range
  • review range
  • best product snapshot
  • one-sentence key insight
  • risk flags
  • verdict with 1-2 sentence rationale

Notes

  • If a keyword has missing or zero search volume, explicitly call that out.
  • If the results look too narrow or too broad, suggest the better keyword variant inline.
  • Do not force a follow-up question at the end; only recommend next actions when the result is borderline or strong enough to justify it.

© 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/launchfast-product-research of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Launchfast 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.

Launchfast Product Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Launchfast Product Research this skillhashgraph-online/awesome-codex-plugins1.3k—~618Automated 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
Crush Configurationcharmbracelet/crush29k—~3.7kAutomated safety check: PassCustom licence
Context Mode Output Sandboxmksglu/context-mode26k—~4.1kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Launchfast Product Research

What does Launchfast Product Research do?

Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP. Launchfast Product Research is an agent skill from hashgraph-online/awesome-codex-plugins. Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP.

When should I use Launchfast Product Research?

Launchfast Product Research fits situations like: the user wants to research 1-10 keywords; decide whether a product idea is a GO.

How do I install Launchfast Product Research in Claude Code?

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

How do I install Launchfast Product Research in Codex?

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

Can I use Launchfast 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 launchfast-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/launchfast-product-research, .gemini/skills/launchfast-product-research, .github/skills/launchfast-product-research and .opencode/skills/launchfast-product-research in your project.

What does Launchfast Product Research need to run?

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

Does Launchfast 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 Launchfast 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 Launchfast Product Research use?

Launchfast 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 Launchfast Product Research use?

About 618 tokens (SKILL.md is roughly 2.5k 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 Launchfast Product Research?

Skills that share tags, products or a category with Launchfast 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 Crush Configuration (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Launchfast 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.