ZooData — Market Trend Scanner

ZooData — Market Trend Scanner

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

Files

Credential

Required: ZOODATA_API_KEY. Get free key at zoodata.ai/​api-keys.

Capabilities & Data Flow

• Network: only https://api.zoodata.ai (Bearer ZOODATA_API_KEY). Setting ZOODATA_BASE_URL to any other host triggers a CLI warning — do not point it at hosts you don't trust.
• Execution: bundled shared ZooData CLI {skill_base_dir}/​scripts/​zoodata.py (Python 3, stdlib-only). The shared CLI exposes ALL ZooData endpoints as subcommands; this skill's workflows use: categories, market, products, check. Do not invoke unrelated subcommands for this skill's tasks.
• Local files: scan state under {skill_base_dir}/​scan-data/ (baseline.json, watchlist.json, history/​*.json); reads the optional credential store ~/​.zoodata/​config.json.
• Sent to the API: keywords, category paths, ASINs, marketplace/​date and numeric filter values only. Never sent: budget, experience level, risk tolerance, or any other user-profile text — profile inputs map client-side to numeric filters.
• Credits: every API call consumes account credits. For broad or ambiguous requests, state the estimated credit cost and confirm with the user before running multi-call scans.

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
5. --mode presets are CLI-local, NOT API params — zoodata.py expands them via PRODUCT_MODES before the call; a raw products/​search request must send the expanded filter fields (mode raw → 422). Follow the mode/​CLI-flags pitfall (#9) in zoodata/​SKILL.md

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

Trend Interpretation & Action Guide

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:

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)

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).