Amazon Opportunity Discoverer

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name: Amazon Opportunity Discoverer — Niche Scanner & Scoring version: 1.0.1 description: > 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. author: SerendipityOneInc homepage: https://github.com/SerendipityOneInc/APIClaw-Skills metadata: {"openclaw": {"requires": {"env": ["APICLAW_API_KEY"]}, "primaryEnv": "APICLAW_API_KEY"}}

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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.

API Budget: ~50-60 credits