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: INLINECODE1
• Scope: used only for INLINECODE2
• 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
INLINECODE4 (this file)	Start here — covers 80% of tasks
INLINECODE5

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.

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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.

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⚠️ 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.

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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
• INLINECODE23 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.

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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)"

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Script Usage

All commands output JSON. Progress messages go to stderr.

categories — Category tree lookup

CODEBLOCK1

Common fields: categoryName (not name), categoryPath, productCount, INLINECODE36

market — Market-level aggregate data

CODEBLOCK2

Key output fields: sampleAvgMonthlySales, sampleAvgPrice, topSalesRate (concentration), topBrandSalesRate, sampleNewSkuRate, sampleFbaRate, INLINECODE43

products — Product selection with filters

CODEBLOCK3

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, INLINECODE57

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:
CODEBLOCK4

competitors — Competitor lookup

CODEBLOCK5

Easily confused fields (products/competitors shared):

❌ Wrong	✅ Correct	Note
INLINECODE64	INLINECODE65	Review count
INLINECODE66

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

CODEBLOCK6

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

analyze — Review analysis (sentiment + consumer insights)

CODEBLOCK7

Returns: totalReviews, avgRating, sentimentDistribution, ratingDistribution, consumerInsights (by labelType), topKeywords, INLINECODE85

Available labelType: scenarios, issues, positives, improvements, buyingFactors, painPoints, keywords, userProfiles, usageTimes, usageLocations, INLINECODE96

report — Full market analysis (composite)

CODEBLOCK8

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

opportunity — Product opportunity discovery (composite)

CODEBLOCK9

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

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⚠️ Interface Data Differences

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

Data	INLINECODE97	INLINECODE98/INLINECODE99	INLINECODE100	INLINECODE101
Monthly Sales	INLINECODE102	✅ INLINECODE103	❌	❌
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.

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Intent Routing

User Says	Run This	Scenario File?
"which category has opportunity"	INLINECODE145 + INLINECODE146	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"	INLINECODE199	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 |

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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

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⚠️ 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)

CODEBLOCK10

Rules:

1. 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.

CODEBLOCK11

Tracking rules:

1. 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

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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 |

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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
INLINECODE226	Use .data[0].fieldName (.data is array)
Empty INLINECODE229

Use categories to confirm category exists |
| atLeastMonthlySales: null | BSR estimate: 300,000 / BSR^0.65 |