Amazon Opportunity Discoverer

Automated product opportunity scanner for Amazon sellers. Scans categories using 14 preset selection strategies, validates candidates with real-time data, brand analysis, and price structure, then ranks opportunities by composite score (1-100). Uses all 11 APIClaw API endpoints. Use when user asks about: find products to sell, product opportunity, what should I sell, niche discovery, profitable products, selection strategy, product scanner, opportunity scan, winning products, untapped niches, product ideas, market gaps. Requires APICLAW_API_KEY.

apiclaw@apiclaw

Install

openclaw skills install @apiclaw/amazon-opportunity-discoverer

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

bash

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.