Agent skill

Launchfast Full Research Loop

by LeoYeAI in LeoYeAI/openclaw-master-skills

Complete Amazon FBA product research pipeline using the LaunchFast MCP.

Install Launchfast Full Research Loop

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a claude-code

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

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills launchfast-full-research-loop --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/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/launchfast-full-research-loop .claude/skills/launchfast-full-research-loop && 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/

Paste into Claude Code

Install the "launchfast-full-research-loop" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop into .claude/skills/launchfast-full-research-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launchfast-full-research-loop", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills launchfast-full-research-loop --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/launchfast-full-research-loop .agents/skills/launchfast-full-research-loop && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "launchfast-full-research-loop" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop into .agents/skills/launchfast-full-research-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launchfast-full-research-loop", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills launchfast-full-research-loop --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/launchfast-full-research-loop .cursor/skills/launchfast-full-research-loop && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "launchfast-full-research-loop" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop into .cursor/skills/launchfast-full-research-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launchfast-full-research-loop", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/launchfast-full-research-loop

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills launchfast-full-research-loop --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/launchfast-full-research-loop .gemini/skills/launchfast-full-research-loop && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "launchfast-full-research-loop" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop into .gemini/skills/launchfast-full-research-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launchfast-full-research-loop", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills launchfast-full-research-loop

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/launchfast-full-research-loop .github/skills/launchfast-full-research-loop && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "launchfast-full-research-loop" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop into .github/skills/launchfast-full-research-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launchfast-full-research-loop", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills launchfast-full-research-loop --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/launchfast-full-research-loop .opencode/skills/launchfast-full-research-loop && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "launchfast-full-research-loop" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/launchfast-full-research-loop into .opencode/skills/launchfast-full-research-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launchfast-full-research-loop", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: launchfast-full-research-loop
GitHub stars: 2.2k
Token cost: ~4.5k tokens
SKILL.md length: 421 words
Files: 2
Skills in repo: 1,235
Repo updated: Jul 20, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Complete Amazon FBA product research pipeline using the LaunchFast MCP.

Works in 7 steps: Gather inputs → PRODUCT RESEARCH → IP CHECK → …
Tasks that involve Keyword research
SKILL.md covers STEP 1 — Gather inputs, ═══════════════════════════════…, PHASE 1 — PRODUCT RESEARCH and ═══════════════════════════════…, plus 13 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Launchfast Full Research Loop is an agent skill from LeoYeAI/openclaw-master-skills. Complete Amazon FBA product research pipeline using the LaunchFast MCP. Runs product research, IP checks, supplier sourcing, and PPC keyword research in sequence, then compiles everything into a clean downloadable HTML report. USE THIS SKILL FOR: - "full research on [keyword]" - "research everything about [product]" - "give me a complete FBA opportunity report" - "run the full loop on [keyword]" Requirements: - mcplaunchfastresearchproducts - mcplaunchfastipcheckmanage - mcplaunchfastsupplierresearch -…

Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

It sits in Marketing & SEO, covering Keyword research and Paid advertising. It works with Model Context Protocol. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

Tasks that involve Keyword research
Tasks that involve Paid advertising

Example prompts

“full research on [keyword]”
“research everything about [product]”
“give me a complete FBA opportunity report”
“/launchfast-full-research-loop”

Workflow steps

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

Gather inputs
PRODUCT RESEARCH
IP CHECK
SUPPLIER RESEARCH
PPC KEYWORD RESEARCH
GENERATE HTML REPORT
Summary to user

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. 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 html).
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 Full Research Loop loads about 4.5k tokens when it runs. Until then it costs about 149 tokens; SKILL.md has 421 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~149
When it runs · the whole SKILL.md, loaded when a task matches: ~4.5k

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 LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 421 words, ~4,522 tokens.

Download SKILL.mdSave it as .claude/skills/launchfast-full-research-loop/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: launchfast-full-research-loop
description: Complete Amazon FBA product research pipeline using the LaunchFast MCP. Runs product research, IP checks, supplier sourcing, and PPC keyword research in sequence, then compiles everything into a clean downloadable HTML report. USE THIS SKILL FOR: - "full research on [keyword]" - "research everything about [product]" - "give me a complete FBA opportunity report" - "run the full loop on [keyword]" Requirements: - mcp__launchfast__research_products - mcp__launchfast__ip_check_manage - mcp__launchfast__supplier_research - mcp__launchfast__amazon_keyword_research
argument-hint: product keyword

LaunchFast Full Research Loop

You are a senior Amazon FBA analyst. You run a complete 5-phase research pipeline on a product opportunity and compile the results into a professional HTML report that sellers can save, share, or present.

Requirements before starting:

All four LaunchFast MCP tools available (see above)

STEP 1 — Gather inputs

Ask in one shot if not provided:

To run the full research loop, I need:

1. Product keyword(s) to research (e.g. "silicone spatula")
2. Target selling price? (e.g. $24.99)
3. Target first-order quantity for sourcing? (e.g. 500 units)
4. Any competitor ASINs you already know? (optional — for PPC phase)
5. Where to save the report? (default: ~/Downloads/launchfast-report-[keyword]-[date].html)

═══════════════════════════════════════

PHASE 1 — PRODUCT RESEARCH

═══════════════════════════════════════

Run for each keyword provided:

mcp__launchfast__research_products(keyword: "[keyword]")

Extract for report:

Total products analyzed
Grade distribution (count per grade tier)
Revenue range (min/max/median)
Price range
Review range
Top 5 products (grade, revenue, price, reviews)
Opportunity score (calculate per skill: launchfast-product-research formula)
Verdict: GO / INVESTIGATE / PASS

Tell user: ✓ Phase 1 complete — [N] products analyzed across [N] keywords

═══════════════════════════════════════

PHASE 2 — IP CHECK

═══════════════════════════════════════

For each winning keyword from Phase 1 (score ≥ 40):

mcp__launchfast__ip_check_manage(
  action: "ip_conflict_check",
  keyword: "[keyword]"
)

Also run targeted trademark search:

mcp__launchfast__ip_check_manage(
  action: "trademark_search",
  keyword: "[keyword]",
  statusFilter: "active"
)

Extract for report:

Conflict level: LOW / MEDIUM / HIGH
Active trademarks found (name, owner, status)
Any patent hits (flag if found)
Risk assessment: CLEAR / CAUTION / BLOCKED

Tell user: ✓ Phase 2 complete — IP risk: [level]

═══════════════════════════════════════

PHASE 3 — SUPPLIER RESEARCH

═══════════════════════════════════════

For the top keyword (highest opportunity score):

mcp__launchfast__supplier_research(
  keyword: "[keyword]",
  goldSupplierOnly: true,
  tradeAssuranceOnly: true,
  maxResults: 10
)

Extract top 5 suppliers for report:

Company name
Quality score
Price range
MOQ
Years in business
Verifications (Gold, Trade Assurance, Assessed, etc.)

Tell user: ✓ Phase 3 complete — [N] suppliers found

═══════════════════════════════════════

PHASE 4 — PPC KEYWORD RESEARCH

═══════════════════════════════════════

If competitor ASINs were provided OR if Phase 1 returned any ASINs:

mcp__launchfast__amazon_keyword_research(asins: ["B0...", ...])

Extract for report:

Total unique keywords found
Top 20 keywords by search volume
Top 5 exact-match opportunities (high volume, lower competition)
Estimated CPCs where available
Recommended campaign structure

If no ASINs available, note in report: "PPC research requires competitor ASINs — add them to run this phase."

Tell user: ✓ Phase 4 complete — [N] keywords extracted

Show full SKILL.md (141 more words)Show less

═══════════════════════════════════════

PHASE 5 — GENERATE HTML REPORT

═══════════════════════════════════════

Generate a complete standalone HTML file. Save to the path specified in Step 1.

Report design system

Match LaunchFast's design exactly:

Font: -apple-system, BlinkMacSystemFont, 'SF Pro Display', 'Segoe UI', system-ui, sans-serif
Text: #1a1a1a | Muted: #666666 | Very muted: #999999
Background: #fafafa | Card: #ffffff
Border: 1px solid #e5e5e5 | Border radius: 8px
Accent: border-left: 3px solid #1a1a1a for callout blocks
Bullet: 6px circle background: #1a1a1a; border-radius: 50%
Go badge: background: #dcfce7; color: #166534
Investigate badge: background: #fef9c3; color: #854d0e
Pass badge: background: #fee2e2; color: #991b1b
IP LOW badge: background: #dcfce7; color: #166534
IP MEDIUM badge: background: #fef9c3; color: #854d0e
IP HIGH badge: background: #fee2e2; color: #991b1b

HTML report template

html

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
  <title>LaunchFast Research Report — [Keyword] — [Date]</title>
  <style>
    * { box-sizing: border-box; margin: 0; padding: 0; }
    body {
      font-family: -apple-system, BlinkMacSystemFont, 'SF Pro Display', 'Segoe UI', system-ui, sans-serif;
      background: #fafafa;
      color: #1a1a1a;
      line-height: 1.5;
      padding: 40px 20px;
    }
    .page { max-width: 960px; margin: 0 auto; }

    /* Header */
    .report-header { margin-bottom: 40px; }
    .report-header .brand { font-size: 13px; font-weight: 600; color: #999; letter-spacing: 0.08em; text-transform: uppercase; margin-bottom: 12px; }
    .report-header h1 { font-size: 32px; font-weight: 700; letter-spacing: -0.03em; margin-bottom: 8px; }
    .report-header .meta { font-size: 14px; color: #666; }

    /* Verdict banner */
    .verdict-banner {
      display: flex; align-items: center; gap: 16px;
      background: #fff; border: 1px solid #e5e5e5; border-radius: 8px;
      padding: 20px 24px; margin-bottom: 32px;
    }
    .verdict-banner .verdict-label { font-size: 12px; font-weight: 600; color: #999; text-transform: uppercase; letter-spacing: 0.06em; }
    .verdict-banner .verdict-value { font-size: 22px; font-weight: 700; letter-spacing: -0.02em; }
    .verdict-banner .divider { width: 1px; height: 40px; background: #e5e5e5; }
    .verdict-banner .stat { }
    .verdict-banner .stat-label { font-size: 11px; color: #999; text-transform: uppercase; letter-spacing: 0.05em; }
    .verdict-banner .stat-value { font-size: 18px; font-weight: 600; letter-spacing: -0.01em; }

    /* Section */
    .section { background: #fff; border: 1px solid #e5e5e5; border-radius: 8px; padding: 28px; margin-bottom: 20px; }
    .section-header { display: flex; align-items: center; justify-content: space-between; margin-bottom: 20px; padding-bottom: 16px; border-bottom: 1px solid #e5e5e5; }
    .section-title { font-size: 16px; font-weight: 600; letter-spacing: -0.01em; }
    .phase-label { font-size: 11px; font-weight: 600; color: #999; text-transform: uppercase; letter-spacing: 0.08em; }

    /* Tables */
    table { width: 100%; border-collapse: collapse; font-size: 13px; }
    th { text-align: left; font-size: 11px; font-weight: 600; color: #999; text-transform: uppercase; letter-spacing: 0.05em; padding: 0 12px 10px 0; border-bottom: 1px solid #e5e5e5; }
    td { padding: 10px 12px 10px 0; border-bottom: 1px solid #f0f0f0; color: #1a1a1a; vertical-align: top; }
    tr:last-child td { border-bottom: none; }
    .grade { font-weight: 700; font-size: 15px; }
    .grade-a { color: #166534; }
    .grade-b { color: #1d4ed8; }
    .grade-c { color: #92400e; }
    .grade-d, .grade-f { color: #991b1b; }

    /* Badges */
    .badge { display: inline-block; font-size: 11px; font-weight: 600; padding: 3px 8px; border-radius: 4px; letter-spacing: 0.03em; }
    .badge-go { background: #dcfce7; color: #166534; }
    .badge-investigate { background: #fef9c3; color: #854d0e; }
    .badge-pass { background: #fee2e2; color: #991b1b; }
    .badge-low { background: #dcfce7; color: #166534; }
    .badge-medium { background: #fef9c3; color: #854d0e; }
    .badge-high { background: #fee2e2; color: #991b1b; }
    .badge-clear { background: #dcfce7; color: #166534; }
    .badge-caution { background: #fef9c3; color: #854d0e; }
    .badge-blocked { background: #fee2e2; color: #991b1b; }

    /* Callout */
    .callout { background: #fafafa; border-left: 3px solid #1a1a1a; padding: 14px 18px; border-radius: 4px; margin: 16px 0; font-size: 14px; color: #444; }
    .callout strong { color: #1a1a1a; }

    /* Stats grid */
    .stats-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); gap: 16px; margin-bottom: 20px; }
    .stat-card { background: #fafafa; border: 1px solid #e5e5e5; border-radius: 6px; padding: 14px 16px; }
    .stat-card .label { font-size: 11px; font-weight: 600; color: #999; text-transform: uppercase; letter-spacing: 0.05em; margin-bottom: 6px; }
    .stat-card .value { font-size: 20px; font-weight: 700; letter-spacing: -0.02em; }
    .stat-card .sub { font-size: 12px; color: #666; margin-top: 2px; }

    /* Supplier score bar */
    .score-bar { display: flex; align-items: center; gap: 8px; }
    .score-bar .bar { flex: 1; height: 4px; background: #e5e5e5; border-radius: 2px; overflow: hidden; }
    .score-bar .fill { height: 100%; background: #1a1a1a; border-radius: 2px; }
    .score-bar .num { font-size: 12px; font-weight: 600; color: #1a1a1a; min-width: 28px; text-align: right; }

    /* Footer */
    .report-footer { margin-top: 40px; padding-top: 20px; border-top: 1px solid #e5e5e5; display: flex; justify-content: space-between; align-items: center; }
    .report-footer .brand-mark { font-size: 13px; font-weight: 600; color: #1a1a1a; }
    .report-footer .generated { font-size: 12px; color: #999; }
  </style>
</head>
<body>
<div class="page">

  <!-- HEADER -->
  <div class="report-header">
    <div class="brand">LaunchFast · FBA Research Report</div>
    <h1>[Keyword] Opportunity Report</h1>
    <div class="meta">Generated [Full Date] · [N] keywords · [N] products analyzed</div>
  </div>

  <!-- VERDICT BANNER -->
  <div class="verdict-banner">
    <div class="stat">
      <div class="verdict-label">Overall Verdict</div>
      <div class="verdict-value"><span class="badge badge-[go/investigate/pass]">[GO / INVESTIGATE / PASS]</span></div>
    </div>
    <div class="divider"></div>
    <div class="stat">
      <div class="stat-label">Opp Score</div>
      <div class="stat-value">[N]/100</div>
    </div>
    <div class="divider"></div>
    <div class="stat">
      <div class="stat-label">IP Risk</div>
      <div class="stat-value"><span class="badge badge-[low/medium/high]">[LOW/MEDIUM/HIGH]</span></div>
    </div>
    <div class="divider"></div>
    <div class="stat">
      <div class="stat-label">Suppliers Found</div>
      <div class="stat-value">[N]</div>
    </div>
    <div class="divider"></div>
    <div class="stat">
      <div class="stat-label">PPC Keywords</div>
      <div class="stat-value">[N]</div>
    </div>
  </div>

  <!-- PHASE 1: PRODUCT RESEARCH -->
  <div class="section">
    <div class="section-header">
      <div class="section-title">Product Research</div>
      <div class="phase-label">Phase 1</div>
    </div>

    <div class="stats-grid">
      <div class="stat-card">
        <div class="label">Products Analyzed</div>
        <div class="value">[N]</div>
      </div>
      <div class="stat-card">
        <div class="label">Top Revenue</div>
        <div class="value">$[X]k<span style="font-size:14px;font-weight:500">/mo</span></div>
      </div>
      <div class="stat-card">
        <div class="label">Price Range</div>
        <div class="value">$[X]–$[X]</div>
      </div>
      <div class="stat-card">
        <div class="label">Avg Reviews</div>
        <div class="value">[N]</div>
      </div>
    </div>

    <table>
      <thead>
        <tr>
          <th>#</th>
          <th>Product</th>
          <th>Grade</th>
          <th>Revenue/mo</th>
          <th>Price</th>
          <th>Reviews</th>
          <th>BSR</th>
        </tr>
      </thead>
      <tbody>
        <!-- Repeat for top 5–10 products -->
        <tr>
          <td style="color:#999">1</td>
          <td>[Product title truncated to 60 chars]</td>
          <td><span class="grade grade-[a/b/c]">[Grade]</span></td>
          <td>$[X,XXX]</td>
          <td>$[XX.XX]</td>
          <td>[X,XXX]</td>
          <td>#[X,XXX]</td>
        </tr>
      </tbody>
    </table>

    <div class="callout" style="margin-top:20px">
      <strong>Key finding:</strong> [1-2 sentence insight about the market — grade distribution, revenue consistency, competitive dynamics]
    </div>
  </div>

  <!-- PHASE 2: IP CHECK -->
  <div class="section">
    <div class="section-header">
      <div class="section-title">IP & Trademark Check</div>
      <div class="phase-label">Phase 2</div>
    </div>

    <div class="stats-grid">
      <div class="stat-card">
        <div class="label">IP Risk Level</div>
        <div class="value"><span class="badge badge-[low/medium/high]">[LOW/MEDIUM/HIGH]</span></div>
      </div>
      <div class="stat-card">
        <div class="label">Active Trademarks</div>
        <div class="value">[N]</div>
      </div>
      <div class="stat-card">
        <div class="label">Patent Hits</div>
        <div class="value">[N]</div>
      </div>
      <div class="stat-card">
        <div class="label">Assessment</div>
        <div class="value"><span class="badge badge-[clear/caution/blocked]">[CLEAR/CAUTION/BLOCKED]</span></div>
      </div>
    </div>

    <!-- If trademarks found, show table -->
    <table>
      <thead>
        <tr><th>Trademark</th><th>Owner</th><th>Status</th><th>Class</th></tr>
      </thead>
      <tbody>
        <tr>
          <td>[Trademark name]</td>
          <td>[Owner]</td>
          <td>[Live/Dead]</td>
          <td>[Class number]</td>
        </tr>
      </tbody>
    </table>

    <div class="callout" style="margin-top:20px">
      <strong>Recommendation:</strong> [Clear action — e.g. "No direct conflicts found. Avoid branding your product as [word] to stay safe." or "HIGH risk — consult an IP attorney before proceeding."]
    </div>
  </div>

  <!-- PHASE 3: SUPPLIER RESEARCH -->
  <div class="section">
    <div class="section-header">
      <div class="section-title">Alibaba Supplier Research</div>
      <div class="phase-label">Phase 3</div>
    </div>

    <table>
      <thead>
        <tr>
          <th>#</th>
          <th>Supplier</th>
          <th>Score</th>
          <th>Price Range</th>
          <th>MOQ</th>
          <th>Years</th>
          <th>Verified</th>
        </tr>
      </thead>
      <tbody>
        <!-- Repeat for top 5 suppliers -->
        <tr>
          <td style="color:#999">1</td>
          <td>[Company Name]</td>
          <td>
            <div class="score-bar">
              <div class="bar"><div class="fill" style="width:[score]%"></div></div>
              <div class="num">[score]</div>
            </div>
          </td>
          <td>$[X.XX]–$[X.XX]</td>
          <td>[N] units</td>
          <td>[N] yrs</td>
          <td>[Gold · TA · Assessed]</td>
        </tr>
      </tbody>
    </table>

    <div class="callout" style="margin-top:20px">
      <strong>Top pick:</strong> [Company Name] — [reason: highest score, most verifications, best price range for target margin]
    </div>
  </div>

  <!-- PHASE 4: PPC KEYWORDS -->
  <div class="section">
    <div class="section-header">
      <div class="section-title">PPC Keyword Intelligence</div>
      <div class="phase-label">Phase 4</div>
    </div>

    <div class="stats-grid">
      <div class="stat-card">
        <div class="label">Total Keywords</div>
        <div class="value">[N]</div>
      </div>
      <div class="stat-card">
        <div class="label">Tier 1 (Priority)</div>
        <div class="value">[N]</div>
      </div>
      <div class="stat-card">
        <div class="label">Tier 2 (Growth)</div>
        <div class="value">[N]</div>
      </div>
      <div class="stat-card">
        <div class="label">Tier 3 (Discovery)</div>
        <div class="value">[N]</div>
      </div>
    </div>

    <table>
      <thead>
        <tr><th>#</th><th>Keyword</th><th>Search Vol</th><th>Tier</th><th>Match Types</th><th>Est. CPC</th></tr>
      </thead>
      <tbody>
        <!-- Top 20 keywords -->
        <tr>
          <td style="color:#999">1</td>
          <td>[keyword]</td>
          <td>[X,XXX]</td>
          <td>Tier 1</td>
          <td>Exact · Phrase</td>
          <td>$[X.XX]</td>
        </tr>
      </tbody>
    </table>

    <div class="callout" style="margin-top:20px">
      <strong>Campaign strategy:</strong> [Brief recommendation — e.g. "Start with the 12 Tier 1 exact-match keywords at $0.90 bid. Run broad on Tier 3 for discovery data. Revisit in 2 weeks."]
    </div>
  </div>

  <!-- FOOTER -->
  <div class="report-footer">
    <div class="brand-mark">LaunchFast</div>
    <div class="generated">Generated [Date] · Data via LaunchFast MCP</div>
  </div>

</div>
</body>
</html>

Fill ALL placeholder values ([...]) with real data from the research phases. Save the complete file to the path from Step 1.

STEP 6 — Summary to user

After saving the file:

## Research Complete ✓

Report saved to: [file path]

Quick summary:
- Keyword: [keyword]
- Verdict: [GO / INVESTIGATE / PASS] (Score: [N]/100)
- IP Risk: [LOW / MEDIUM / HIGH]
- Best supplier: [Company Name] ($X.XX–$X.XX/unit, MOQ: N)
- PPC keywords found: [N] (Tier 1: N | Tier 2: N | Tier 3: N)

Next steps:
[If GO]: Ready to contact suppliers? Run /alibaba-supplier-outreach [keyword]
[If GO]: Ready to build your PPC campaign? Run /launchfast-ppc-research [ASINs]
[If INVESTIGATE]: [Specific concern to investigate]
[If PASS]: [Clear reason — what would need to change for this to become viable]

Files

SKILL.md and 1 other file in skills/launchfast-full-research-loop of LeoYeAI/openclaw-master-skills.

SKILL.md
_meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Launchfast Full Research Loop 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 Full Research Loop compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Launchfast Full Research Loop this skillLeoYeAI/openclaw-master-skills	2.2k	—	~4.5k	Automated safety check: Pass	MIT	2 mo ago
Launchfast Full Research Loophashgraph-online/awesome-codex-plugins	1.3k	—	~657	Automated safety check: Pass	Apache-2.0	today
Launchfast Ppc Researchhashgraph-online/awesome-codex-plugins	1.3k	—	~784	Automated safety check: Pass	Apache-2.0	today
Sealeap Dijiang Amazon Conversion Rate Keyword Filterxjli360/sealeap-amazon-skills	247	—	~768	Automated safety check: Pass	MIT	11 days ago
Evaluate Skillevery-app/open-seo	23k	—	~1.8k	Automated safety check: Notes	MIT	today
Blog GoogleAgriciDaniel/claude-blog	2.3k	1 repos	~3.3k	Automated safety check: Notes	MIT	today

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Questions about Launchfast Full Research Loop

What does Launchfast Full Research Loop do?

Complete Amazon FBA product research pipeline using the LaunchFast MCP. Launchfast Full Research Loop is an agent skill from LeoYeAI/openclaw-master-skills. Complete Amazon FBA product research pipeline using the LaunchFast MCP.

When should I use Launchfast Full Research Loop?

Launchfast Full Research Loop fits situations like: tasks that involve Keyword research; tasks that involve Paid advertising.

How do I install Launchfast Full Research Loop in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a claude-code`. Or copy the skill folder (skills/launchfast-full-research-loop in LeoYeAI/openclaw-master-skills) into .claude/skills/launchfast-full-research-loop in your project. Claude Code loads it when a task matches its description.

How do I install Launchfast Full Research Loop in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -a codex`. Or copy the skill folder (skills/launchfast-full-research-loop in LeoYeAI/openclaw-master-skills) into .agents/skills/launchfast-full-research-loop in your project. Codex loads it when a task matches its description.

Can I use Launchfast Full Research Loop 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 LeoYeAI/openclaw-master-skills --skill launchfast-full-research-loop -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-full-research-loop, .gemini/skills/launchfast-full-research-loop, .github/skills/launchfast-full-research-loop and .opencode/skills/launchfast-full-research-loop in your project.

What does Launchfast Full Research Loop need to run?

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

Does Launchfast Full Research Loop 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 Full Research Loop 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 Full Research Loop use?

Launchfast Full Research Loop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Launchfast Full Research Loop use?

About 4.5k tokens (SKILL.md is roughly 18k 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 Full Research Loop?

Skills that share tags, products or a category with Launchfast Full Research Loop: Launchfast Full Research Loop (hashgraph-online/awesome-codex-plugins, 1.3k stars), Launchfast Ppc Research (hashgraph-online/awesome-codex-plugins, 1.3k stars), Sealeap Dijiang Amazon Conversion Rate Keyword Filter (xjli360/sealeap-amazon-skills, 247 stars) and Evaluate Skill (every-app/open-seo, 23k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Launchfast Full Research Loop?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Launchfast Full Research Loop

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

LaunchFast Full Research Loop

STEP 1 — Gather inputs

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PHASE 1 — PRODUCT RESEARCH

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PHASE 2 — IP CHECK

═══════════════════════════════════════

═══════════════════════════════════════

PHASE 3 — SUPPLIER RESEARCH

═══════════════════════════════════════

═══════════════════════════════════════

PHASE 4 — PPC KEYWORD RESEARCH

═══════════════════════════════════════

═══════════════════════════════════════

PHASE 5 — GENERATE HTML REPORT

═══════════════════════════════════════

Report design system

HTML report template

STEP 6 — Summary to user

Files

Compare with similar skills

Similar skills

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Questions about Launchfast Full Research Loop