amazon-market-entry-analyzer-go-caution-avoid-verdicts

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":"[Truncated]"},"primaryEnv":"APICLAW_API_KEY"}}

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