Amazon-analysis-skill

author: SerendipityOneInc
homepage: https://github.com/SerendipityOneInc/Amazon-analysis-skill
metadata: {"openclaw": {"requires": {"env": ["APICLAW_API_KEY"]}, "primaryEnv": "APICLAW_API_KEY"}}
---

# APIClaw — Amazon Seller Data Analysis

> AI-powered Amazon product research. From market discovery to daily operations.
>
> **Language rule**: Always respond in the user's language. If the user asks in Chinese, reply in Chinese. If in English, reply in English. The language of this skill document does not affect output language.
> All API calls go through `scripts/apiclaw.py` — one script, 5 endpoints, built-in error handling.

## Credentials

- Required: `APICLAW_API_KEY`
- Scope: used only for `https://api.apiclaw.io`
- Resolution order:
  1. **Environment variable** `APICLAW_API_KEY` (preferred, most secure)
  2. **Config file** `config.json` in the skill root directory (fallback)

```json
{ "api_key": "hms_live_xxxxxx" }
```

When user provides a Key, write it to `config.json`. New keys may need 3-5 seconds to activate — if first call returns 403, wait 3 seconds and retry (max 2 retries).

## File Map

| File | When to Load |
|------|-------------|
| `SKILL.md` (this file) | Start here — covers 80% of tasks |
| `scripts/apiclaw.py` | **Execute** for all API calls (do NOT read into context) |
| `references/reference.md` | Need exact field names or filter parameter details |
| `references/scenarios-composite.md` | Comprehensive recommendations (2.10) or Chinese seller cases (3.4) |
| `references/scenarios-eval.md` | Product evaluation, risk assessment, review analysis (4.x) |
| `references/scenarios-pricing.md` | Pricing strategy, profit estimation, listing reference (5.x) |
| `references/scenarios-ops.md` | Market monitoring, competitor tracking, anomaly alerts (6.x) |
| `references/scenarios-expand.md` | Product expansion, trends, discontinuation decisions (7.x) |
| `references/scenarios-listing.md` | Listing writing, optimization, content creation (8.x) |

**Don't guess field names** — if uncertain, load `reference.md` first.

---

## Execution Mode

| Task Type | Mode | Behavior |
|-----------|------|----------|
| Single ASIN lookup, simple data query | **Quick** | Execute command, return key data. Skip evaluation criteria and output standard block. |
| Market analysis, product selection, competitor comparison, risk assessment | **Full** | Complete flow: command → analysis → evaluation criteria → output standard block. |

**Quick mode trigger:** User asks for a single specific data point ("B09XXX monthly sales?", "how many brands in cat litter?") — no decision analysis needed.

---

## ⚠️ Pre-Execution Checklist (MANDATORY for Full Mode)

Before running any Full-mode product selection or market analysis, **complete this checklist**:

- [ ] **Step 1 — Mode Selection:** Check the Product Selection Mode Mapping table below. If ANY of the 14 preset modes matches the user's intent, **USE IT** (`--mode xxx`). Do NOT manually piece together filters when a preset mode exists. Common mappings:
  - Small/lightweight/cheap products → `--mode low-price`
  - New seller / beginner → `--mode beginner`
  - Niche / long-tail → `--mode long-tail`
  - Trending / rising → `--mode emerging`
- [ ] **Step 2 — Realtime Supplement:** Plan to call `product --asin` for the top 3-5 ASINs from results (see Realtime Data Supplementation below).
- [ ] **Step 3 — Review Analysis:** Plan to call `analyze --asins` for top ASINs to get consumer insights (especially painPoints, improvements, buyingFactors).
- [ ] **Step 4 — Output Blocks:** Prepare to include both `📋 Data Source & Conditions` and `📊 API Usage` at the end.

> **Why this exists:** In testing, AI agents repeatedly skipped preset modes, realtime supplements, and review analysis — even though the instructions below clearly describe them. This checklist forces a pause-and-verify before execution.

---

## Execution Standards

**Prioritize script execution for API calls.** The script includes:
- Parameter format conversion (e.g. topN auto-converted to string)
- Retry logic (429/timeout auto-retry)
- Standardized error messages
- `_query` metadata injection (for query traceability)

**Fallback:** If script fails and can't be quickly fixed, use curl directly. Note "using curl direct call" in output.

---

## Realtime Data Supplementation

When `products` or `competitors` returns ASINs in Full-mode analysis, **automatically call `product --asin` for the top 3-5 most relevant ASINs** to get current real-time data.

| Scenario | Supplement? | How many ASINs |
|----------|-------------|----------------|
| Single ASIN lookup (Quick mode) | Already using realtime | — |
| Market overview (no specific ASINs) | ❌ No | — |
| Product selection / competitor analysis | ✅ Yes | Top 3 by sales |
| Risk assessment | ✅ Yes | Target ASIN + top 2 competitors |
| Multi-product comparison | ✅ Yes | All compared ASINs (max 5) |
| Listing analysis | Already using realtime | — |

**Handling data conflicts** — `products`/`competitors` has ~T+1 delay; `realtime/product` is live:

| Field | Use from | Reason |
|-------|----------|--------|
| Price | **realtime** (`buyboxWinner.price`) | Changes frequently |
| BSR | **realtime** (`bestsellersRank`) | Updates hourly |
| Rating / ratingCount | **realtime** | More current |
| Monthly Sales | **products/competitors** | Realtime doesn't have this |
| Profit Margin / FBA Fee | **products/competitors** | Realtime doesn't have this |

When realtime data differs significantly, note it: e.g. "⚡ Price updated: database $29.99 → realtime $24.99 (likely promotion)"

---

## Script Usage

All commands output JSON. Progress messages go to stderr.

### categories — Category tree lookup

```bash
python3 scripts/apiclaw.py categories --keyword "pet supplies"
python3 scripts/apiclaw.py categories --parent "Pet Supplies"
```

Common fields: `categoryName` (not `name`), `categoryPath`, `productCount`, `hasChildren`

### market — Market-level aggregate data

```bash
python3 scripts/apiclaw.py market --category "Pet Supplies,Dogs" --topn 10
```

Key output fields: `sampleAvgMonthlySales`, `sampleAvgPrice`, `topSalesRate` (concentration), `topBrandSalesRate`, `sampleNewSkuRate`, `sampleFbaRate`, `sampleBrandCount`

### products — Product selection with filters

```bash
# Preset mode (14 built-in)
python3 scripts/apiclaw.py products --keyword "yoga mat" --mode beginner

# Explicit filters
python3 scripts/apiclaw.py products --keyword "yoga mat" --sales-min 300 --reviews-max 50

# Mode + overrides (overrides win)
python3 scripts/apiclaw.py products --keyword "yoga mat" --mode beginner --price-max 30
```

Available modes: `fast-movers`, `emerging`, `single-variant`, `high-demand-low-barrier`, `long-tail`, `underserved`, `new-release`, `fbm-friendly`, `low-price`, `broad-catalog`, `selective-catalog`, `speculative`, `beginner`, `top-bsr`

**Keyword matching:** Default is `fuzzy` (matches brand names too — e.g. "smart ring" matches "Smart Color Art" pens). Use `--keyword-match-type exact` or `phrase` for precise results. Always combine with `--category` when possible to reduce noise.

**Category path with commas:** Some category names contain commas (e.g. "Pacifiers, Teethers & Teething Relief"). Use ` > ` separator instead of `,` to avoid parsing errors:
```bash
# ❌ Wrong — comma in name breaks parsing
--category "Baby Products,Baby Care,Pacifiers, Teethers & Teething Relief"
# ✅ Correct — use ' > ' separator
--category "Baby Products > Baby Care > Pacifiers, Teethers & Teething Relief"
```

### competitors — Competitor lookup

```bash
python3 scripts/apiclaw.py competitors --keyword "wireless earbuds"
python3 scripts/apiclaw.py competitors --asin B09V3KXJPB
```

**Easily confused fields (products/competitors shared)**:

| ❌ Wrong | ✅ Correct | Note |
|----------|-----------|------|
| `reviewCount` | `ratingCount` | Review count |
| `bsr` | `bsrRank` | BSR ranking (integer, only in products/competitors) |
| `monthlySales` / `salesMonthly` | `atLeastMonthlySales` | Monthly sales (lower bound estimate, NOT in realtime/product) |
| `bestsellersRank` | `bsrRank` | `bestsellersRank` is realtime/product only (array format); use `bsrRank` for products/competitors |
| `price` (in realtime) | `buyboxWinner.price` | realtime/product nests price inside buyboxWinner object |
| `profitMargin` (in realtime) | ❌ N/A | realtime/product does NOT return profitMargin; use products/competitors |

> Complete field list: `reference.md` → Shared Product Object

### product — Single ASIN real-time detail

```bash
python3 scripts/apiclaw.py product --asin B09V3KXJPB
```

Returns: title, brand, rating, ratingBreakdown, features, topReviews, specifications, variants, bestsellersRank, buyboxWinner

### analyze — Review analysis (sentiment + consumer insights)

```bash
# Single ASIN
python3 scripts/apiclaw.py analyze --asin B09V3KXJPB

# Multiple ASINs (competitive review comparison)
python3 scripts/apiclaw.py analyze --asins B09V3KXJPB,B08YYYYY,B07ZZZZZ

# Category-level insights
python3 scripts/apiclaw.py analyze --category "Pet Supplies,Dogs,Toys" --period 90d

# Specific insight dimension
python3 scripts/apiclaw.py analyze --asin B09V3KXJPB --label-type painPoints,buyingFactors
```

Returns: `totalReviews`, `avgRating`, `sentimentDistribution`, `ratingDistribution`, `consumerInsights` (by labelType), `topKeywords`, `verifiedRatio`

Available labelType: `scenarios`, `issues`, `positives`, `improvements`, `buyingFactors`, `painPoints`, `keywords`, `userProfiles`, `usageTimes`, `usageLocations`, `behaviors`

### report — Full market analysis (composite)

```bash
python3 scripts/apiclaw.py report --keyword "pet supplies"
```

Runs: categories → market → products (top 50) → realtime detail (top 1).

### opportunity — Product opportunity discovery (composite)

```bash
python3 scripts/apiclaw.py opportunity --keyword "pet supplies" --mode fast-movers
```

Runs: categories → market → products (filtered) → realtime detail (top 3).

---

## ⚠️ Interface Data Differences

The 4 types of interfaces return **different fields**. Do NOT assume they share the same structure.

| Data | `market` | `products`/`competitors` | `realtime/product` | `reviews/analyze` |
|------|----------|--------------------------|--------------------|--------------------|
| Monthly Sales | `sampleAvgMonthlySales` | ✅ `atLeastMonthlySales` | ❌ | ❌ |
| Revenue | `sampleAvgMonthlyRevenue` | `salesRevenue` | ❌ | ❌ |
| Price | `sampleAvgPrice` | `price` | `buyboxWinner.price` | ❌ |
| BSR | `sampleAvgBsr` | `bsrRank` (integer) | `bestsellersRank` (array) | ❌ |
| Rating | `sampleAvgRating` | `rating` | `rating` | `avgRating` |
| Review Count | `sampleAvgReviewCount` | `ratingCount` | `ratingCount` | `totalReviews` |
| Review Details | ❌ | ❌ | ✅ `topReviews` + `ratingBreakdown` | ❌ (no raw reviews) |
| Sentiment Analysis | ❌ | ❌ | ❌ | ✅ `sentimentDistribution` |
| Consumer Insights | ❌ | ❌ | ❌ | ✅ `consumerInsights` (11 dimensions) |
| Pain Points/Issues | ❌ | ❌ | ❌ (manual from topReviews) | ✅ AI-analyzed |
| Top Keywords | ❌ | ❌ | ❌ | ✅ `topKeywords` |
| Seller | ❌ | `buyboxSeller` (string) | `buyboxWinner` (object) | ❌ |
| Profit Margin | ❌ | `profitMargin` | ❌ | ❌ |
| FBA Fee | ❌ | `fbaFee` | ❌ | ❌ |
| Seller Count | ❌ | `sellerCount` | ❌ | ❌ |
| Features/Bullets | ❌ | ❌ | ✅ `features` | ❌ |
| Variants | ❌ | `variantCount` (integer) | `variants` (full list) | ❌ |

**Usage rule:**
- Use `products` / `competitors` for **sales, pricing, and competition data**
- Use `realtime/product` for **review details, listing content, and seller info**
- Use `market` for **category-level aggregate metrics**
- Use `reviews/analyze` for **AI-powered review insights** (sentiment, pain points, buying factors — covers all reviews, not just topReviews)
- For reports: combine `products`/`competitors` (quantitative) + `realtime/product` (qualitative) + `reviews/analyze` (consumer insights) as evidence

## Data Structure Reminder

All interfaces return `.data` as an **array**. Use `.data[0]` to get the first record, NOT `.data.fieldName`.

---

## Intent Routing

| User Says | Run This | Scenario File? |
|-----------|----------|----------------|
| "which category has opportunity" | `market` + `categories` | No |
| "check B09XXX" / "analyze ASIN" | `product --asin XXX` | No |
| "Chinese seller cases" | `competitors --keyword XXX --page-size 50` | `scenarios-composite.md` → 3.4 |
| "pain points" / "negative reviews" / "consumer insights" | `analyze --asin XXX` + `product --asin XXX` | `scenarios-eval.md` → 4.2 |
| "category pain points" / "category user portrait" | `analyze --category XXX` | `scenarios-eval.md` → 4.6 |
| "compare products" | `competitors` or multiple `product` | `scenarios-eval.md` → 4.3 |
| "risk assessment" / "can I do this" | `product` + `market` + `competitors` | `scenarios-eval.md` → 4.4 |
| "monthly sales" / "estimate sales" | `competitors --asin XXX` | `scenarios-eval.md` → 4.5 |
| "help me select products" / "find products" | `products --mode XXX` (see mode table) | No |
| "comprehensive recommendations" / "what should I sell" | `products` (multi-mode) + `market` | `scenarios-composite.md` → 2.10 |
| "pricing strategy" / "how much to price" | `market` + `products` | `scenarios-pricing.md` → 5.1 |
| "profit estimation" | `competitors` | `scenarios-pricing.md` → 5.2 |
| "listing reference" | `product --asin XXX` | `scenarios-pricing.md` → 5.3 |
| "market changes" / "recent changes" | `market` + `products` | `scenarios-ops.md` → 6.1 |
| "competitor updates" | `competitors --brand XXX` | `scenarios-ops.md` → 6.2 |
| "anomaly alerts" | `market` + `products` | `scenarios-ops.md` → 6.4 |
| "what else can I sell" / "related products" | `categories` + `market` | `scenarios-expand.md` → 7.1 |
| "trends" | `products --growth-min 0.2` | `scenarios-expand.md` → 7.3 |
| "should I delist" | `competitors --asin XXX` + `market` | `scenarios-expand.md` → 7.4 |
| "write listing" / "generate bullet points" / "write title" | `product --asin XXX` (competitors) | `scenarios-listing.md` → 8.2 |
| "analyze competitor listing" / "their selling points" | `product --asin XXX` (multiple) | `scenarios-listing.md` → 8.1 |
| "optimize my listing" / "listing diagnosis" | `product --asin XXX` + `competitors` | `scenarios-listing.md` → 8.3 |
| Need exact filters or field names | — | Load `reference.md` |

**Product Selection Mode Mapping (14 types)**:

| User Intent | Mode | Key Filters |
|-------------|------|-------------|
| "beginner friendly" / "new seller" | `--mode beginner` | Sales≥300, growth≥3%, $15-60, FBA, ≤1yr, auto-excludes 150+ red ocean keywords |
| "fast turnover" / "hot selling" | `--mode fast-movers` | Sales≥300, growth≥10% |
| "emerging" / "rising" | `--mode emerging` | Sales≤600, growth≥10%, ≤180d |
| "single variant" / "small but beautiful" | `--mode single-variant` | Growth≥20%, variants=1, ≤180d |
| "high demand low barrier" / "easy entry" | `--mode high-demand-low-barrier` | Sales≥300, reviews≤50, ≤180d |
| "long tail" / "niche" | `--mode long-tail` | Sales≤300, BSR 10K-50K, ≤$30, sellers≤1 |
| "underserved" / "has pain points" | `--mode underserved` | Sales≥300, rating≤3.7, ≤180d |
| "new products" / "new release" | `--mode new-release` | Sales≤500, NR tag, FBA+FBM |
| "FBM" / "self-fulfillment" / "low stock" | `--mode fbm-friendly` | Sales≥300, FBM, ≤180d |
| "low price" / "cheap" | `--mode low-price` | ≤$10 |
| "broad catalog" / "cast wide net" | `--mode broad-catalog` | BSR growth≥99%, reviews≤10, ≤90d |
| "selective catalog" | `--mode selective-catalog` | BSR growth≥99%, ≤90d |
| "speculative" / "piggyback" | `--mode speculative` | Sales≥600, sellers≥3, ≤180d |
| "top sellers" / "best sellers" | `--mode top-bsr` | Sub-category BSR≤1000 |

---

## Quick Evaluation Criteria

### Market Viability (from `market` output)

| Metric | Good | Medium | Warning |
|--------|------|--------|---------|
| Market value (avgRevenue × skuCount) | > $10M | $5–10M | < $5M |
| Concentration (topSalesRate, topN=10) | < 40% | 40–60% | > 60% |
| New SKU rate (sampleNewSkuRate) | > 15% | 5–15% | < 5% |
| FBA rate (sampleFbaRate) | > 50% | 30–50% | < 30% |
| Brand count (sampleBrandCount) | > 50 | 20–50 | < 20 |

### Product Potential (from `product` output)

| Metric | High | Medium | Low |
|--------|------|--------|-----|
| BSR | Top 1000 | 1000–5000 | > 5000 |
| Reviews | < 200 | 200–1000 | > 1000 |
| Rating | > 4.3 | 4.0–4.3 | < 4.0 |
| Negative reviews (1-2★ %) | < 10% | 10–20% | > 20% |

### Sales Estimation Fallback

When `atLeastMonthlySales` is null: **Monthly sales ≈ 300,000 / BSR^0.65**

---

## ⚠️ Output Standards (Full Mode — MANDATORY, DO NOT SKIP)

> **Two blocks are REQUIRED at the end of every Full-mode analysis: ① Data Source & Conditions, ② API Usage. Missing either one = violating the skill contract.**

### ① Data Source & Conditions (Full Mode Only)

```markdown
---
📋 **Data Source & Conditions**
| Item | Value |
|----|-----|
| Data Source | APIClaw API |
| Interface | [interfaces used] |
| Category | [category path] |
| Time Range | [dateRange] |
| Sampling | [sampleType] |
| Top N | [topN value] |
| Sort | [sortBy + sortOrder] |
| Filters | [specific parameter values] |

**Data Notes**
- Monthly sales are **lower bound estimates** (Amazon displays "10,000+ bought"), actual may be higher
- Database data has ~T+1 delay; realtime/product is current real-time data
- Concentration metrics based on Top N sample; different topN → different results
```

**Rules**:
1. Every Full-mode analysis MUST end with this block
2. Filter conditions MUST list specific parameter values
3. If multiple interfaces used, list each one
4. If data has limitations, proactively explain
5. ⚠️ **Self-check:** scan your response — if you don't see `📋 **Data Source & Conditions**`, ADD IT before replying

### ⚠️ API Usage Summary (All Modes — MANDATORY, DO NOT SKIP)

> **This block is NON-NEGOTIABLE.** Every single response — Quick or Full mode — MUST end with this table. No exceptions. If you forget, you are violating the skill contract.

```markdown
📊 **API Usage**
| Interface | Calls |
|-----------|-------|
| categories | 1 |
| markets/search | 1 |
| products/search | 2 |
| realtime/product | 3 |
| reviews/analyze | 1 |
| **Total** | **8** |
| **Credits consumed** | **8** |
| **Credits remaining** | **492** |
```

**Tracking rules:**
1. Count each `apiclaw.py` execution as 1 call to the corresponding interface
2. Sum `_credits.consumed` from every API response for total consumed
3. Use `_credits.remaining` from the **last** API response as remaining balance
4. If `_credits` fields are null, show "N/A"
5. ⚠️ **Self-check before sending:** scan your response — if you don't see `📊 **API Usage**` at the bottom, ADD IT before replying

---

## Limitations

### What This Skill Cannot Do

- Keyword research / reverse ASIN / ABA data
- Traffic source analysis
- Historical sales trends (14-month curves)
- Historical price / BSR charts
- Raw individual review text export (use `realtime/product` topReviews for specific review quotes)

### API Coverage Boundaries

| Scenario | Coverage | Suggestion |
|----------|----------|------------|
| Market data: Popular keywords | ✅ Has data | Use `--keyword` directly |
| Market data: Niche/long-tail keywords | ⚠️ May be empty | Use `--category` instead |
| Product data: Active ASIN | ✅ Has data | — |
| Product data: Delisted/variant ASIN | ❌ No data | Try parent ASIN or realtime |
| Real-time data: US site | ✅ Full support | — |
| Real-time data: Non-US sites | ⚠️ Partial | Core fields OK, sales may be null |

---

## Error Handling

HTTP errors (401/402/403/404/429) are handled by the script with structured JSON output.
Self-check: `python3 scripts/apiclaw.py check`

| Error | Fix |
|-------|-----|
| `Cannot index array with string` | Use `.data[0].fieldName` (`.data` is array) |
| Empty `data: []` | Use `categories` to confirm category exists |
| `atLeastMonthlySales: null` | BSR estimate: 300,000 / BSR^0.65 |