Agent skill

Apiclaw Analysis

by LeoYeAI in LeoYeAI/openclaw-master-skills

Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment.

MITAuto-check passedSales & Support

Install Apiclaw Analysis

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/amazon-analysis-skill .claude/skills/apiclaw-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
apiclaw-analysis
GitHub stars
2.2k
Token cost
~5.1k tokens
SKILL.md length
1,840 words
Files
12 (incl. scripts, references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment.

  • Works in 2 steps: Environment variable APICLAW_API_KEY… → Config file config.json in the skill…
  • User asks about: product selection
  • SKILL.md covers Credentials, File Map, Execution Mode and ⚠️ Pre-Execution Checklist…, plus 10 more sections
  • Runs Python scripts from its folder; calls python3; reaches api.apiclaw.io; needs APICLAW_API_KEY

What it does

Apiclaw Analysis is an agent skill from LeoYeAI/openclaw-master-skills. Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment. Backed by 200M+ product database. Use when user asks about: product selection, finding products to sell, ASIN lookup, BSR analysis, competitor lookup, market opportunity, risk assessment, category research, pricing strategy, review analysis, listing optimization, or any Amazon seller data needs. Powered by APIClaw API (requires APICLAWAPIKEY).

Its SKILL.md is about 5.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts and reference files (for example `README.md`, `SECURITY.md` and `_meta.json`).

It sits in Sales & Support, covering E-commerce operations, Customer feedback analysis and Pricing strategy. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • User asks about: product selection
  • Finding products to sell
  • Competitor lookup
  • Market opportunity

Example prompts

  • “Use the apiclaw-analysis skill to find winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment”
  • “/apiclaw-analysis”

Requirements

  • Python 3
  • A credential in APICLAW_API_KEY

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Environment variable APICLAW_API_KEY (preferred, most secure)
  2. Config file config.json in the skill root directory (fallback)

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.apiclaw.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • APICLAW_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Apiclaw Analysis loads about 5.1k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 1,840 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~117
When it runs · the whole SKILL.md, loaded when a task matches
~5.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~16k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,840 words, ~5,143 tokens.

Download SKILL.mdSave it as .claude/skills/apiclaw-analysis/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
apiclaw-analysis
description
Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment. Backed by 200M+ product database. Use when user asks about: product selection, finding products to sell, ASIN lookup, BSR analysis, competitor lookup, market opportunity, risk assessment, category research, pricing strategy, review analysis, listing optimization, or any Amazon seller data needs. Powered by APIClaw API (requires APICLAW_API_KEY).
version
1.1.4
author
SerendipityOneInc
homepage
https://github.com/SerendipityOneInc/Amazon-analysis-skill

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

FileWhen to Load
SKILL.md (this file)Start here — covers 80% of tasks
scripts/apiclaw.pyExecute for all API calls (do NOT read into context)
references/reference.mdNeed exact field names or filter parameter details
references/scenarios-composite.mdComprehensive recommendations (2.10) or Chinese seller cases (3.4)
references/scenarios-eval.mdProduct evaluation, risk assessment, review analysis (4.x)
references/scenarios-pricing.mdPricing strategy, profit estimation, listing reference (5.x)
references/scenarios-ops.mdMarket monitoring, competitor tracking, anomaly alerts (6.x)
references/scenarios-expand.mdProduct expansion, trends, discontinuation decisions (7.x)
references/scenarios-listing.mdListing writing, optimization, content creation (8.x)

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


Execution Mode

Task TypeModeBehavior
Single ASIN lookup, simple data queryQuickExecute command, return key data. Skip evaluation criteria and output standard block.
Market analysis, product selection, competitor comparison, risk assessmentFullComplete 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.

ScenarioSupplement?How many ASINs
Single ASIN lookup (Quick mode)Already using realtime—
Market overview (no specific ASINs)❌ No—
Product selection / competitor analysis✅ YesTop 3 by sales
Risk assessment✅ YesTarget ASIN + top 2 competitors
Multi-product comparison✅ YesAll compared ASINs (max 5)
Listing analysisAlready using realtime—

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

FieldUse fromReason
Pricerealtime (buyboxWinner.price)Changes frequently
BSRrealtime (bestsellersRank)Updates hourly
Rating / ratingCountrealtimeMore current
Monthly Salesproducts/competitorsRealtime doesn't have this
Profit Margin / FBA Feeproducts/competitorsRealtime 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✅ CorrectNote
reviewCountratingCountReview count
bsrbsrRankBSR ranking (integer, only in products/competitors)
monthlySales / salesMonthlyatLeastMonthlySalesMonthly sales (lower bound estimate, NOT in realtime/product)
bestsellersRankbsrRankbestsellersRank is realtime/product only (array format); use bsrRank for products/competitors
price (in realtime)buyboxWinner.pricerealtime/product nests price inside buyboxWinner object
profitMargin (in realtime)❌ N/Arealtime/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.

Datamarketproducts/competitorsrealtime/productreviews/analyze
Monthly SalessampleAvgMonthlySales✅ atLeastMonthlySales❌❌
RevenuesampleAvgMonthlyRevenuesalesRevenue❌❌
PricesampleAvgPricepricebuyboxWinner.price❌
BSRsampleAvgBsrbsrRank (integer)bestsellersRank (array)❌
RatingsampleAvgRatingratingratingavgRating
Review CountsampleAvgReviewCountratingCountratingCounttotalReviews
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.


Show full SKILL.md (774 more words)Show less

Intent Routing

User SaysRun ThisScenario File?
"which category has opportunity"market + categoriesNo
"check B09XXX" / "analyze ASIN"product --asin XXXNo
"Chinese seller cases"competitors --keyword XXX --page-size 50scenarios-composite.md → 3.4
"pain points" / "negative reviews" / "consumer insights"analyze --asin XXX + product --asin XXXscenarios-eval.md → 4.2
"category pain points" / "category user portrait"analyze --category XXXscenarios-eval.md → 4.6
"compare products"competitors or multiple productscenarios-eval.md → 4.3
"risk assessment" / "can I do this"product + market + competitorsscenarios-eval.md → 4.4
"monthly sales" / "estimate sales"competitors --asin XXXscenarios-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) + marketscenarios-composite.md → 2.10
"pricing strategy" / "how much to price"market + productsscenarios-pricing.md → 5.1
"profit estimation"competitorsscenarios-pricing.md → 5.2
"listing reference"product --asin XXXscenarios-pricing.md → 5.3
"market changes" / "recent changes"market + productsscenarios-ops.md → 6.1
"competitor updates"competitors --brand XXXscenarios-ops.md → 6.2
"anomaly alerts"market + productsscenarios-ops.md → 6.4
"what else can I sell" / "related products"categories + marketscenarios-expand.md → 7.1
"trends"products --growth-min 0.2scenarios-expand.md → 7.3
"should I delist"competitors --asin XXX + marketscenarios-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 + competitorsscenarios-listing.md → 8.3
Need exact filters or field names—Load reference.md

Product Selection Mode Mapping (14 types):

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

Quick Evaluation Criteria

Market Viability (from market output)
MetricGoodMediumWarning
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)> 5020–50< 20
Product Potential (from product output)
MetricHighMediumLow
BSRTop 10001000–5000> 5000
Reviews< 200200–1000> 1000
Rating> 4.34.0–4.3< 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
ScenarioCoverageSuggestion
Market data: Popular keywords✅ Has dataUse --keyword directly
Market data: Niche/long-tail keywords⚠️ May be emptyUse --category instead
Product data: Active ASIN✅ Has data—
Product data: Delisted/variant ASIN❌ No dataTry parent ASIN or realtime
Real-time data: US site✅ Full support—
Real-time data: Non-US sites⚠️ PartialCore 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

ErrorFix
Cannot index array with stringUse .data[0].fieldName (.data is array)
Empty data: []Use categories to confirm category exists
atLeastMonthlySales: nullBSR estimate: 300,000 / BSR^0.65

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in skills/amazon-analysis-skill of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • SECURITY.md
  • _meta.json
  • references/reference.md
  • references/scenarios-composite.md
  • references/scenarios-eval.md
  • references/scenarios-expand.md
  • references/scenarios-listing.md
  • references/scenarios-ops.md
  • references/scenarios-pricing.md
  • scripts/apiclaw.py

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Apiclaw Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Apiclaw Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Apiclaw Analysis this skillLeoYeAI/openclaw-master-skills2.2k—~5.1kAutomated safety check: PassMIT
Taobao Product Reviewsbrowser-act/skills6.1k—~1.8kAutomated safety check: PassMIT
Competitive Pricing Strategynexscope-ai/eCommerce-Skills1.1k—~2.8kAutomated safety check: PassMIT
Shopify Dropshippingnexscope-ai/eCommerce-Skills1.1k—~465Automated safety check: PassMIT
AI Product Pricingtech-leads-club/agent-skills7k—~3.6kAutomated safety check: PassCustom licence
Pricing Strategistalirezarezvani/claude-skills28k—~2.3kAutomated safety check: PassMIT

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Categories

Questions about Apiclaw Analysis

What does Apiclaw Analysis do?

Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment. Apiclaw Analysis is an agent skill from LeoYeAI/openclaw-master-skills. Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment.

When should I use Apiclaw Analysis?

Apiclaw Analysis fits situations like: user asks about: product selection; finding products to sell; competitor lookup; market opportunity.

How do I install Apiclaw Analysis in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a claude-code`. Or copy the skill folder (skills/amazon-analysis-skill in LeoYeAI/openclaw-master-skills) into .claude/skills/apiclaw-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Apiclaw Analysis in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a codex`. Or copy the skill folder (skills/amazon-analysis-skill in LeoYeAI/openclaw-master-skills) into .agents/skills/apiclaw-analysis in your project. Codex loads it when a task matches its description.

Can I use Apiclaw Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/apiclaw-analysis, .gemini/skills/apiclaw-analysis, .github/skills/apiclaw-analysis and .opencode/skills/apiclaw-analysis in your project.

What does Apiclaw Analysis need to run?

Going by SKILL.md and its folder, Apiclaw Analysis needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named APICLAW_API_KEY. Our summary lists: Python 3; A credential in APICLAW_API_KEY.

Does Apiclaw Analysis access the network?

SKILL.md names 1 domain. In commands or code: api.apiclaw.io; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Apiclaw Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Apiclaw Analysis use?

Apiclaw Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Apiclaw Analysis use?

About 5.1k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 10k tokens, read only when the agent opens those files.

What are the alternatives to Apiclaw Analysis?

Skills that share tags, products or a category with Apiclaw Analysis: Taobao Product Reviews (browser-act/skills, 6.1k stars), Competitive Pricing Strategy (nexscope-ai/eCommerce-Skills, 1.1k stars), Shopify Dropshipping (nexscope-ai/eCommerce-Skills, 1.1k stars) and AI Product Pricing (tech-leads-club/agent-skills, 7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Apiclaw Analysis?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.