apiclaw-analysis

name

apiclaw-analysis

description

Amazon product research and seller analytics for FBA and FBM businesses. Find winning products with 14 selection strategies, track competitors, monitor BSR trends, analyze reviews, estimate monthly sales, optimize listings, and assess market opportunities. Real-time ASIN lookup with 200M+ product database. Amazon seller tools, niche research, keyword analysis, pricing strategy, and category insights powered by APIClaw API. Use when user asks about: Amazon product selection, finding products to sell, ASIN lookup, BSR analysis, competitor tracking, market opportunity, risk assessment, FBA research, review analysis, or listing optimization. Requires 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
• Setup: Guide user to set the environment variable:

  export APICLAW_API_KEY='hms_live_xxxxxx'
  

• Fallback: The script also checks config.json in the skill root directory if the env var is not set.
• Do NOT write keys to disk files. Always recommend the environment variable approach.
• 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, call product --asin for the top 3-5 most relevant ASINs to get current real-time data. For bulk lookups (>3 ASINs), confirm with the user before proceeding.

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

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

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

# 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:

# ❌ 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

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

python3 scripts/apiclaw.py product --asin B09V3KXJPB

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

analyze — Review analysis (sentiment + consumer insights)

# 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)

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

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

opportunity — Product opportunity discovery (composite)

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)	❌

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

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)

---
📋 **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.

📊 **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