Amazon Market Trend Scanner

Scan Amazon category landscapes to discover trending subcategories, emerging niches, and market shifts. Complements Daily Market Radar (defensive monitoring) with offensive trend discovery for product selection and market entry timing. Tracks demand surges, brand consolidation, new entrant waves, price band migration, and margin changes across all subcategories under a parent category. Use when user asks about: market trends, category trends, trending categories, what's hot, emerging categories, trend scanner, category trends, market trends, which categories are growing. Requires APICLAW_API_KEY.

APIClaw — Market Trend Scanner

Find rising categories before everyone else. Respond in user's language.

Files

File	Purpose
{skill_base_dir}/scripts/apiclaw.py	Execute for all API calls (run --help for params)
{skill_base_dir}/references/reference.md	Load for exact field names or response structure
{skill_base_dir}/scan-data/	Runtime: watchlist.json, baseline.json, alerts.json, history/ (auto-created)

Credential

Required: APICLAW_API_KEY. Get free key at apiclaw.io/api-keys.

Input

Tell the user: "Give me one or more categories to monitor (e.g. 'Pet Supplies > Dogs'). I'll scan all subcategories and find trending directions. Single or batch supported."

Required: 1+ category paths or keywords. Optional: scan depth, metric preferences.

API Pitfalls (CRITICAL)

1. Category first: resolve categoryPath via categories --keyword before anything
2. All keyword endpoints MUST include --category; omitting it distorts aggregation
3. Use API fields directly: revenue=sampleAvgMonthlyRevenue, sales=monthlySalesFloor
4. Key metrics per subcategory: sampleAvgMonthlySales, sampleNewSkuRate, topBrandSalesRate, sampleAvgPrice, sampleAPlusRate, totalSkuCount, sampleFbaRate

Mode 1: Full Scan

1. categories --keyword "{keyword}" → resolve category path
2. market --category "{path}" --page-size 20 → collect all subcategory market data (paginate)
3. Record 7 key metrics per subcategory (see Pitfalls #4)
4. products --keyword "{sub}" --category "{path}" --mode emerging --page-size 20 per hot subcategory
5. products --keyword "{sub}" --category "{path}" --mode new-release --page-size 20 per hot subcategory
6. Save baseline → {skill_base_dir}/scan-data/baseline.json, config → {skill_base_dir}/scan-data/watchlist.json
7. Output full trend report (see Output Spec)
8. Offer Auto-Monitor setup

Mode 2: Quick Check (scheduled)

1. Read {skill_base_dir}/scan-data/watchlist.json + {skill_base_dir}/scan-data/baseline.json
2. market --category "{path}" per watched category
3. Compare vs baseline using signal rules below
4. 🔴 alerts → notify user; else silent log
5. Save snapshot to {skill_base_dir}/scan-data/history/{timestamp}.json, update baseline

Trend Signals

Signal	Condition	Level
Demand surge	sampleAvgMonthlySales >20% vs baseline	🔴
Red ocean warning	topBrandSalesRate >70% AND rising	🔴
New entrant wave	sampleNewSkuRate up >5 percentage points	🟡
Brand loosening	topBrandSalesRate down >3 percentage points	🟡
Price band shift	sampleAvgPrice change >10%	🟡
Margin change	sampleAPlusRate change >5 percentage points	🟡
Minor movement	None of the above triggered	🟢 Silent log

Trend Interpretation & Action Guide

Signal Combination	Market Phase	Recommended Action
Demand surge + New entrant wave	🚀 Growth phase	Enter quickly, first-mover advantage matters 💡
Demand surge + Brand loosening	🎯 Opportunity window	Best timing — demand up, incumbents losing grip 💡
Demand surge + Red ocean warning	⚠️ Late stage growth	High demand but leaders consolidating — need strong differentiation 💡
Red ocean warning + No demand surge	🔒 Mature/locked	Avoid — established players dominate with flat demand 💡
Brand loosening + Price band shift down	💰 Price war	Wait — margins compressing, enter after shakeout 💡
New entrant wave + Margin change	🔄 Disruption	Category being redefined — study new entrants' strategies 🔍

Subcategory Ranking Criteria

Rank subcategories by composite attractiveness (apply market-entry scoring logic):

• Demand: sampleAvgMonthlySales — higher = more attractive 📊
• Competition: topBrandSalesRate — lower = more open 📊
• Entry barrier: sampleAvgRatingCount — lower = easier entry 📊
• Activity: sampleNewSkuRate — higher = more dynamic 📊
• Margin signal: sampleAvgPrice — higher generally = better margins 🔍

Auto-Monitor

After each Full Scan, ask user to enable scheduled monitoring. If yes, generate cron config with: category list, alert thresholds, schedule. Supports OpenClaw /cron, ChatGPT Scheduled Tasks, Claude Projects. Quick Check only notifies on 🔴 alerts.

Output Spec

Full Scan: Trend Dashboard (all subcategories) → 🔥 Hot Categories TOP 5 → 🆕 New Entrants Scan → ⚠️ Risk Alerts → Subcategory Detail (per hot category) → Next Steps → Data Provenance → API Usage.

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 💡. Sample bias note required. 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

Full Scan: ~40-60 credits (~2-3 per subcategory × 20). Quick Check: ~20-30 credits (market only).