# Amazon Market Trend Scanner

> >

- **Type:** Skill
- **Install:** `agentstack add skill-serendipityoneinc-zoodata-skills-amazon-market-trend-scanner`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [SerendipityOneInc](https://agentstack.voostack.com/s/serendipityoneinc)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [SerendipityOneInc](https://github.com/SerendipityOneInc)
- **Source:** https://github.com/SerendipityOneInc/ZooData-Skills/tree/main/amazon-market-trend-scanner
- **Website:** https://apiclaw.io/

## Install

```sh
agentstack add skill-serendipityoneinc-zoodata-skills-amazon-market-trend-scanner
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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](https://zoodata.ai/en/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](https://github.com/SerendipityOneInc)
- **Source:** [SerendipityOneInc/ZooData-Skills](https://github.com/SerendipityOneInc/ZooData-Skills)
- **License:** MIT
- **Homepage:** https://apiclaw.io/

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-serendipityoneinc-zoodata-skills-amazon-market-trend-scanner
- Seller: https://agentstack.voostack.com/s/serendipityoneinc
- Browse the marketplace: https://agentstack.voostack.com/browse

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