Skills Amazon Analysis — Full-Spectrum Research & Seller Intelligence

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/apiclaw/amazon-analysis" ~/.claude/skills/clawdbot-skills-amazon-analysis-full-spectrum-research-seller-intelligence && rm -rf "$T"

manifest: skills/apiclaw/amazon-analysis/SKILL.md

source content

APIClaw — Amazon Seller Data Analysis

AI-powered Amazon product research. Respond in user's language.

Files

File	Purpose
`{skill_base_dir}/scripts/apiclaw.py`	Execute for all API calls (run `--help` for params)
`{skill_base_dir}/references/reference.md`	Load when you need exact field names or filter details

Credential

Required:

APICLAW_API_KEY

. Get free key at apiclaw.io/api-keys. Stored in

{skill_base_dir}/config.json

in skill root.

Input

User provides: keyword, category, ASIN, or brand — depending on intent. Use intent routing below.

API Pitfalls (CRITICAL)

Category first: keyword search is broad → MUST lock
```
categoryPath
```
via
```
categories
```
endpoint before other calls
Brand + category: Brand queries MUST include
```
--category
```
to avoid cross-category contamination
Use API fields directly: revenue=
```
sampleAvgMonthlyRevenue
```
(NEVER calculate price×sales), sales=
```
monthlySalesFloor
```
(lower bound), opportunity=
```
sampleOpportunityIndex
```
reviews/analysis: needs 50+ reviews per ASIN; try category mode first (single call returns all dimensions), ASIN mode only if category call fails. Filter by
```
labelType
```
client-side from the
```
consumerInsights
```
array.
Aggregation without categoryPath: produces severely distorted data
.data
is array: use
```
.data[0]
```
, not
```
.data.field
```
labelType: NOT an API request parameter — it is a field in the response
```
consumerInsights
```
array, used for client-side filtering
history empty: try oldest-listed ASINs first, up to 3 rounds of different ASINs before giving up
Sales null fallback: Monthly sales ≈ 300,000 / BSR^0.65

14 Product Selection Modes

Mode	One-line Description
`hot-products`	High sales + strong growth momentum
`rising-stars`	Low base + rapid growth trajectory
`underserved`	Monthly sales≥300, rating≤3.7 — improvable products
`high-demand-low-barrier`	Monthly sales≥300, reviews≤50 — easy entry
`beginner`	$15-60, FBA, monthly sales≥300 — new seller friendly
`fast-movers`	Monthly sales≥300, growth≥10% — quick turnover
`emerging`	Monthly sales≤600, growth≥10%, ≤6 months old
`single-variant`	Growth≥20%, 1 variant, ≤6 months — small & rising
`long-tail`	BSR 10K-50K, ≤$30, exclusive sellers — niche
`new-release`	Monthly sales≤500, New Release tag
`low-price`	≤$10 products
`top-bsr`	BSR≤1000 best sellers
`fbm-friendly`	Monthly sales≥300, self-fulfilled
`broad-catalog`	BSR growth≥99%, reviews≤10, ≤90 days

Modes can combine with explicit filters (

--price-max

--sales-min

, etc). Overrides win.

Composite Commands

```
report --keyword X
```
→ categories + market + products(top50) + realtime(top1)
```
opportunity --keyword X [--mode Y]
```
→ categories + market + products(filtered) + realtime(top3)

Analysis Framework

Every analysis should address these dimensions where data is available:

Market Health Assessment

Indicator	Good	Caution	Warning
Monthly demand (sampleAvgMonthlySales)	>1,500 units 📊	500-1,500 📊	<500 📊
Brand concentration (CR10)	<40% 📊	40-60% 📊	>60% 📊
New entrant rate (sampleNewSkuRate)	>15% 📊	5-15% 📊	<5% 📊
Avg review count (sampleAvgRatingCount)	<500 📊	500-5,000 📊	>5,000 📊
FBA rate (sampleFbaRate)	>60% 📊	40-60% 📊	<40% 📊

Competitive Position Assessment

Price vs category avg: >20% above = premium positioning, >20% below = value play 🔍
Rating vs category avg: ≥0.3 above = quality advantage, ≥0.3 below = quality risk 🔍
Review count vs Top 10 avg: <10% of leaders = high barrier, >50% = competitive 🔍
BSR trend (30d): Improving = momentum, stable = holding, declining = losing share 🔍

Opportunity Viability

When user asks "should I sell X" or "is this a good niche":

ALL of: demand >500, CR10 <60%, avgReviewCount <5,000 → Likely viable 🔍
ANY of: demand <200, CR10 >80%, avgReviewCount >10,000 → Likely not viable 🔍
Mixed signals → Present data, let user decide with their domain knowledge 💡

Sales Estimation Notes

```
monthlySalesFloor
```
is a lower-bound estimate 📊
Null sales fallback: Monthly sales ≈ 300,000 / BSR^0.65 🔍
Revenue =
```
sampleAvgMonthlyRevenue
```
directly — NEVER calculate price × sales 📊

Output Spec

Sections: Analysis findings → Data Source & Conditions table (interfaces, category, dateRange, sampleType, topN, filters) → Data Notes (estimated values, T+1 delay, sampling basis).

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 APIClaw 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 💡. 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

Limitations

Cannot do: keyword research, reverse ASIN, ABA data, traffic source analysis, historical price/BSR charts. Niche keywords may return empty — use category path instead.