Amazon Market Entry Analyzer

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name: Amazon Market Entry Analyzer — GO/CAUTION/AVOID Verdicts version: 1.0.1 description: > One-click market viability assessment for Amazon sellers. Analyzes market size, competition intensity, brand landscape, pricing structure, and consumer pain points to deliver a GO/CAUTION/AVOID recommendation. Uses all 11 APIClaw API endpoints with cross-validation for data-backed decisions. Use when user asks about: market entry, can I sell, should I enter, market viability, is this niche worth it, category analysis, market opportunity, market assessment, niche evaluation, product category research. 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 Market Entry Analyzer — GO / CAUTION / AVOID

One input (keyword/category). Full market viability assessment with sub-market discovery.

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 categoryPath

Optional: marketplace (default US)

API Pitfalls (shared with apiclaw skill — critical!)

Keyword search is broad → categoryPath is auto-resolved via categories endpoint, with fallback to top search result. If category_source is inferred_from_search, confirm with user

Brand/price-band queries MUST include --category to avoid cross-category contamination

Revenue = sampleAvgMonthlyRevenue (NEVER calculate avgPrice × totalSales — overestimates 30-70%)

Sales = monthlySalesFloor (lower bound). Fallback: 300,000 / BSR^0.65, tag 🔍

Use sampleOpportunityIndex, sampleTop10BrandSalesRate directly — never reinvent

reviews/analysis needs 50+ reviews; fallback to realtime ratingBreakdown

Aggregation endpoints without categoryPath produce severely distorted data

Unique Logic

Sub-Market Discovery

Run market --category "{path}" --topn 10 --page-size 20, paginate all pages. Score each sub-market (1-100):

| Dimension | Weight | Field | Good→100 | Bad→0 | |-----------|--------|-------|----------|-------| | Demand | 25% | sampleAvgMonthlySales | ≥1500 | <200 | | Profit | 25% | sampleAPlusRate | ≥0.35 | <0.15 | | New Entrant | 20% | sampleNewSkuRate | ≥0.20 | <0.05 | | Brand Openness | 20% | topBrandSalesRate | ≤0.50 | ≥0.90 (inverted) | | Capacity | 10% | totalSkuCount | 300-8000 | extreme |

Fallback (grossMargin=0 for all): redistribute to Demand 30%, New Entrant 25%, Brand 25%, Capacity 20%.

Present TOP 10 sub-markets. Ask user which to deep-dive (default: top 3). If ≤3 sub-markets, deep-dive all.

Market Viability Score (1-100)

| Dimension | Weight | Good | Medium | Warning | |-----------|--------|------|--------|---------| | Market Size | 15% | >$10M/mo | $5-10M | <$5M | | Market Trend | 10% | Rising | Stable | Declining | | Competition | 25% | CR10<40% | 40-60% | >60% | | Price Opportunity | 15% | oppIndex>1.0 | 0.5-1.0 | <0.5 | | New Entrant Space | 10% | >15% | 5-15% | <5% | | Consumer Pain Points | 15% | Clear gaps | Some | None | | Profit Potential | 10% | >30% | 15-30% | <15% |

Go/No-Go Decision

| Score | Signal | Action | |-------|--------|--------| | 70-100 | ✅ GO | Proceed with product development | | 40-69 | ⚠️ CAUTION | Possible but needs differentiation | | 0-39 | 🔴 AVOID | Too competitive or too small |

CR10 dual-level check: Category CR10 PASS + sub-market CR10 FAIL → ⚠️ CAUTION. Both FAIL → AVOID. User criteria override: If user sets thresholds, ANY fail → CAUTION/AVOID. Never override.

Composite Command

python3 {skill_base_dir}/scripts/apiclaw.py market-entry --keyword "{kw}" --category "{path}"

Runs all 11 endpoints (~20 calls). Output JSON is large — use targeted extraction, not full read.

Output

Respond in user's language.

Sections: Sub-Market Landscape → Executive Summary → Market Overview → Trend → Brand Landscape → Price Structure → Top 5 Competitors → Consumer Insights → Scoring Breakdown (with "Basis" column) → Entry Strategy → Data Provenance → API Usage → Cross-Market Comparison

If user provides COGS, calculate break-even and profit. If not, prompt for it.

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: ~20 calls