Amazon Market Trend Scanner

ZooData — Market Trend Scanner

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

Files

| File | Purpose | |------|---------| | {skill_base_dir}/scripts/zoodata.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: ZOODATA_API_KEY. Get free key at zoodata.ai/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

On Missing Key

When ZOODATA_API_KEY is not set (verify via python {skill_base_dir}/scripts/zoodata.py check — exits 2 if no key in env or ~/.zoodata/config.json): follow the "On Missing Key" protocol in zoodata/SKILL.md — STOP before any call, link the user to https://zoodata.ai/en/api-keys, and DO NOT produce a "partial analysis from public knowledge" / "for reference only" fallback as a substitute.

On 401 Invalid Key

When zoodata.py returns code 401: follow the "On 401 Invalid Key" protocol in zoodata/SKILL.md — STOP further calls, tell the user the key was rejected and direct them to api-keys, do not fabricate missing data.

On 402 Credit Exhausted

When zoodata.py returns code 402: follow the "On 402 Credit Exhausted" protocol in zoodata/SKILL.md — STOP further calls, report partial findings already gathered, do not fabricate missing data.

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 ZooData 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).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: SerendipityOneInc
• Source: SerendipityOneInc/ZooData-Skills
• License: MIT
• Homepage: https://apiclaw.io/

Install and usage instructions live in the source repository linked above.