Skills Amazon Opportunity Discoverer — Niche Scanner & Scoring

Name: Amazon Opportunity Discoverer — Niche Scanner & Scoring
Author: openclaw

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/apiclaw/amazon-opportunity-discoverer" ~/.claude/skills/openclaw-skills-amazon-opportunity-discoverer-niche-scanner-scoring && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/apiclaw/amazon-opportunity-discoverer" ~/.openclaw/skills/openclaw-skills-amazon-opportunity-discoverer-niche-scanner-scoring && rm -rf "$T"

manifest: skills/apiclaw/amazon-opportunity-discoverer/SKILL.md

Amazon Opportunity Discoverer — Niche Scanner & Scoring

Tell me your budget and experience. I find opportunities, score them, and rank.

Files

Script:

{skill_base_dir}/scripts/apiclaw.py

— run

--help

for params

Reference:
```
{skill_base_dir}/references/reference.md
```
(field names & response structure)

Credential

Required:

APICLAW_API_KEY

. Get free key at apiclaw.io/api-keys

Input

Required: keyword or category + budget (Low/Med/High) + experience (Beginner/Intermediate/Advanced)
Recommended: risk tolerance (Conservative/Moderate/Aggressive)
Optional: fulfillment preference (FBA/FBM), specific filter criteria

API Pitfalls (see apiclaw skill for full list)

categoryPath is auto-resolved via
```
categories
```
, with fallback to top search result. If
```
category_source
```
is
```
inferred_from_search
```
, confirm with user — keyword-only queries contaminate results
All keyword-based endpoints MUST include
```
--category
```
when locked

Revenue =

sampleAvgMonthlyRevenue

directly. Sales =

monthlySalesFloor

(lower bound)

```
reviews/analysis
```
needs 50+ reviews
Deduplicate ASINs across modes — same product appears in multiple scans
Each mode has built-in filters that STACK with user filters (e.g. beginner: $15-60, sales≥300)

Unique Logic

Profile → Strategy Mapping

Profile	Primary Modes	Price	Max Reviews
Beginner + Conservative	beginner, long-tail, fbm-friendly	$15-60	<50
Beginner + Moderate	beginner, emerging, low-price	$10-50	<100
Intermediate + Moderate	fast-movers, underserved, single-variant	$15-80	<200
Intermediate + Aggressive	high-demand-low-barrier, speculative	$10-100	<500
Advanced + Aggressive	fast-movers, speculative, top-bsr	any	any

User Criteria → Filter Params

Always translate: "300+ monthly sales" →

--sales-min 300

, "reviews <100" →

--ratings-max 100

, "$15-35" →

--price-min 15 --price-max 35

. If user has specific criteria, use custom filters (Approach B/C), NOT default modes.

Data-Driven Category Selection (no specific category given)

Scan with

market --keyword "{broad}" --topn 10

, rank subcategories by: newSkuRate>10%, topBrandSalesRate<60%, fbaRate>50%, avgPrice $10-50, avgMonthlySales>200. Pick top 3-5.

Opportunity Score (per candidate, 1-100)

Dimension	Weight	Good	Medium	Warning
Demand Signal	20%	sales>300, rev>$5K	100-300	<100
Competition Gap	20%	reviews<200, CR10<40%	200-1K, 40-60%	>1K, >60%
Price Opportunity	15%	in best opp band, opp>1.0	0.5-1.0	<0.5
Trend Momentum	15%	BSR rising	stable	declining
Profit Margin	15%	>30%	15-30%	<15%
Differentiation	10%	clear pain points	some gaps	none
Profile Fit	5%	matches user profile	partial	mismatch

Tiers

Score	Tier	Label
80-100	S	🔥 Hot — act fast
60-79	A	✅ Strong — worth pursuing
40-59	B	⚠️ Moderate — needs differentiation
0-39	C	❌ Weak — skip

Quick-Scan Mode (~10 credits): 2 modes × 1 page, skip realtime/trend. Label as "directional only."

Composite Command

python3 {skill_base_dir}/scripts/apiclaw.py opportunity-scan --keyword "{kw}" --category "{path}" --modes "beginner,emerging,underserved"

Or with custom filters:

--sales-min 300 --ratings-max 100 --price-min 15 --price-max 35

Output

Respond in user's language.

Sections: Scan Summary → Top 10 Opportunities Table → Detailed Analysis (Top 3) → Category Heatmap → Risk Alerts → Next Steps (S: buy sample, A: deep-dive, B: watch) → Data Provenance → API Usage

If user provides COGS, calculate profit. User criteria override: ANY fail → CAUTION/AVOID.

Language (required)

Output language MUST match the user's input language. If the user asks in Chinese, the entire report is in Chinese. If in English, output in English. Exception: API field names (e.g.

monthlySalesFloor

categoryPath

), endpoint names, technical terms (e.g. ASIN, BSR, CR10, FBA, credits) remain in English.

Disclaimer (required, at the top of every report)

Data is based on APIClaw API sampling as of [date]. Monthly sales (
monthlySalesFloor
) are lower-bound estimates. This analysis is for reference only and should not be the sole basis for business decisions. Validate with additional sources before acting.

Confidence Labels (required, tag EVERY conclusion)

📊 Data-backed — direct API data (e.g. "CR10 = 54.8% 📊")
🔍 Inferred — logical reasoning from data (e.g. "brand concentration is moderate 🔍")
💡 Directional — suggestions, predictions, strategy (e.g. "consider entering $10-15 band 💡")

Rules: Strategy recommendations are NEVER 📊. Anomalies (>200% growth) are always 💡. User criteria override AI judgment.

Data Provenance (required)

Include a table at the end of every report:

Data	Endpoint	Key Params	Notes
(e.g. Market Overview)	`markets/search`	categoryPath, topN=10	📊 Top N sampling, sales are lower-bound
...	...	...	...

Extract endpoint and params from

_query

in JSON output. Add notes: sampling method, T+1 delay, realtime vs DB, minimum review threshold, etc.

API Usage (required)

Endpoint	Calls	Credits
(each endpoint used)	N	N
Total	N	N

Extract from

meta.creditsConsumed

per response. End with

Credits remaining: N