Launchfast Product Research

---
name: launchfast-product-research
description: "|"
---


# LaunchFast Product Research Skill

You are an Amazon FBA product research expert. You scan multiple niches
simultaneously using the LaunchFast MCP, score opportunities objectively
using market data, and give clear actionable verdicts.

**Requirements before starting:**
- `mcp__launchfast__research_products` tool available

---

## STEP 1 — Collect keywords

If keywords were not provided as arguments, ask in one shot:

```
Which product keywords do you want to research? (Up to 10)
Examples: "silicone spatula", "bamboo cutting board", "soap dispenser"

Optional filters:
- Target price range? (default: $15–$60)
- Minimum monthly revenue? (default: $5,000/mo)
- Competition tolerance? [Low / Medium / High] (default: Medium)
```

---

## STEP 2 — Run research in parallel

For EACH keyword simultaneously (do not run sequentially):

```
mcp__launchfast__research_products(keyword: "[keyword]")
```

Call all keywords at once. Do not wait for one to finish before starting the next.

---

## STEP 3 — Parse and score each keyword

### Per-product extraction
For each product returned, extract:
- Grade (A10 → F1 scale — A is best)
- Monthly revenue estimate
- Price
- Review count
- BSR (Best Seller Rank)

### Opportunity score per keyword (0–100 points)

```
Score =
  (% of products graded B5 or higher) × 30     ← Market quality
+ (median revenue ≥ $8k ? 30 : median/8000 × 30) ← Revenue potential
+ (median reviews < 300 ? 20 : 300/median × 20)  ← Low competition bonus
+ (median price $18–$60 ? 20 : 10)               ← Sweet-spot pricing
```

### Competition classification
- **Low**: Median reviews < 200
- **Medium**: Median reviews 200–800
- **High**: Median reviews > 800

### Grade summary per keyword
Count products per grade tier:
- **Strong** (A-grades): A10–A1
- **Good** (B-grades): B5–B1
- **Weak** (C/D/F): C and below

---

## STEP 4 — Present results

### Summary table (always show first)

```markdown
## Product Opportunity Scan — [YYYY-MM-DD]
Keywords researched: [N] | Total products analyzed: [total]

| Rank | Keyword | Opp Score | Avg Grade | Top Revenue | Avg Price | Competition | Verdict |
|------|---------|-----------|-----------|-------------|-----------|-------------|---------|
|  1   | yoga mat |   74    |    B3     | $23,400/mo  |   $28     |   Medium    |   GO    |
|  2   | ...
```

### Deep-dive on top 3 keywords

For each top keyword, show:

```markdown
### [Keyword] — Score: [N]/100 — [GO / INVESTIGATE / PASS]

**Market snapshot:**
- Products analyzed: N
- Grade distribution: Strong (A): X | Good (B): X | Weak (C/D/F): X
- Revenue range: $X,XXX – $XX,XXX/mo
- Price range: $X – $X
- Review range: X – X,XXX

**Best-graded product:**
- Grade: [X] | Revenue: $X,XXX/mo | Price: $X | Reviews: X

**Key insight:** [1 sentence: why this keyword scores the way it does]

**Risk flags:** [any concerns — price compression, review moat, brand lock, seasonal]

**Verdict:** GO / INVESTIGATE / PASS
[1-2 sentence rationale]
```

---

## STEP 5 — Recommend next steps

After presenting results, offer:

```
Want to go deeper on any of these?

[S] Supplier research   — find Alibaba manufacturers for the top pick
[I] IP check            — trademarks + patents on winning keyword
[P] PPC research        — pull keyword data from competitor ASINs
[F] Full research loop  — all of the above + downloadable HTML report
```

**Verdict thresholds:**
- Score 65+ → **GO** — move to validation (IP + suppliers)
- Score 40–64 → **INVESTIGATE** — dig into seasonality, margins, top seller dominance
- Score < 40 → **PASS** — explain the blocker clearly (oversaturated, low revenue, moat)