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

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)

Tiers

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:

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)

Extract from meta.creditsConsumed per response. End with Credits remaining: N.

API Budget: ~50-60 credits