Taobao Product Reviews
browser-act/skills
Fetch customer reviews for a Taobao or Tmall product by itemId, returning reviewer name, date, purchased variant, review text, and photo URLs.
Finds winning Amazon products with 14 battle-tested selection strategies & 6-dimension risk assessment.
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "apiclaw-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skill into .claude/skills/apiclaw-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "apiclaw-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skillType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/amazon-analysis-skill .agents/skills/apiclaw-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "apiclaw-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skill into .agents/skills/apiclaw-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "apiclaw-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/amazon-analysis-skill .cursor/skills/apiclaw-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "apiclaw-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skill into .cursor/skills/apiclaw-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "apiclaw-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/amazon-analysis-skill--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/amazon-analysis-skill .gemini/skills/apiclaw-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "apiclaw-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skill into .gemini/skills/apiclaw-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "apiclaw-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/amazon-analysis-skill .github/skills/apiclaw-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "apiclaw-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skill into .github/skills/apiclaw-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "apiclaw-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill apiclaw-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills apiclaw-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/amazon-analysis-skill .opencode/skills/apiclaw-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "apiclaw-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/amazon-analysis-skill into .opencode/skills/apiclaw-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "apiclaw-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
apiclaw-analysisFinds 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. 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.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
api.apiclaw.ioFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
APICLAW_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,840 words, ~5,143 tokens.
.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.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.
APICLAW_API_KEYhttps://api.apiclaw.ioAPICLAW_API_KEY (preferred, most secure)config.json in the skill root directory (fallback){ "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 | 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.
| 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.
Before running any Full-mode product selection or market analysis, complete this checklist:
--mode xxx). Do NOT manually piece together filters when a preset mode exists. Common mappings:--mode low-price--mode beginner--mode long-tail--mode emergingproduct --asin for the top 3-5 ASINs from results (see Realtime Data Supplementation below).analyze --asins for top ASINs to get consumer insights (especially painPoints, improvements, buyingFactors).📋 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.
Prioritize script execution for API calls. The script includes:
_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.
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)"
All commands output JSON. Progress messages go to stderr.
python3 scripts/apiclaw.py categories --keyword "pet supplies"
python3 scripts/apiclaw.py categories --parent "Pet Supplies"Common fields: categoryName (not name), categoryPath, productCount, hasChildren
python3 scripts/apiclaw.py market --category "Pet Supplies,Dogs" --topn 10Key output fields: sampleAvgMonthlySales, sampleAvgPrice, topSalesRate (concentration), topBrandSalesRate, sampleNewSkuRate, sampleFbaRate, sampleBrandCount
# 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 30Available 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"python3 scripts/apiclaw.py competitors --keyword "wireless earbuds"
python3 scripts/apiclaw.py competitors --asin B09V3KXJPBEasily 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
python3 scripts/apiclaw.py product --asin B09V3KXJPBReturns: title, brand, rating, ratingBreakdown, features, topReviews, specifications, variants, bestsellersRank, buyboxWinner
# 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,buyingFactorsReturns: totalReviews, avgRating, sentimentDistribution, ratingDistribution, consumerInsights (by labelType), topKeywords, verifiedRatio
Available labelType: scenarios, issues, positives, improvements, buyingFactors, painPoints, keywords, userProfiles, usageTimes, usageLocations, behaviors
python3 scripts/apiclaw.py report --keyword "pet supplies"Runs: categories → market → products (top 50) → realtime detail (top 1).
python3 scripts/apiclaw.py opportunity --keyword "pet supplies" --mode fast-moversRuns: categories → market → products (filtered) → realtime detail (top 3).
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:
products / competitors for sales, pricing, and competition datarealtime/product for review details, listing content, and seller infomarket for category-level aggregate metricsreviews/analyze for AI-powered review insights (sentiment, pain points, buying factors — covers all reviews, not just topReviews)products/competitors (quantitative) + realtime/product (qualitative) + reviews/analyze (consumer insights) as evidenceAll interfaces return .data as an array. Use .data[0] to get the first record, NOT .data.fieldName.
| 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 |
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 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% |
When atLeastMonthlySales is null: Monthly sales ≈ 300,000 / BSR^0.65
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.
---
📋 **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 resultsRules:
📋 **Data Source & Conditions**, ADD IT before replyingThis 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:
apiclaw.py execution as 1 call to the corresponding interface_credits.consumed from every API response for total consumed_credits.remaining from the last API response as remaining balance_credits fields are null, show "N/A"📊 **API Usage** at the bottom, ADD IT before replyingrealtime/product topReviews for specific review quotes)| 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 |
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 |
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 11 other files (scripts, references) in skills/amazon-analysis-skill of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Apiclaw Analysis this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~5.1k | Automated safety check: Pass | MIT | |
| Taobao Product Reviewsbrowser-act/skills | 6.1k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Competitive Pricing Strategynexscope-ai/eCommerce-Skills | 1.1k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Shopify Dropshippingnexscope-ai/eCommerce-Skills | 1.1k | — | ~465 | Automated safety check: Pass | MIT | |
| AI Product Pricingtech-leads-club/agent-skills | 7k | — | ~3.6k | Automated safety check: Pass | Custom licence | |
| Pricing Strategistalirezarezvani/claude-skills | 28k | — | ~2.3k | Automated safety check: Pass | MIT |
browser-act/skills
Fetch customer reviews for a Taobao or Tmall product by itemId, returning reviewer name, date, purchased variant, review text, and photo URLs.
nexscope-ai/eCommerce-Skills
Build an evidence-based competitive pricing strategy for ecommerce products.
nexscope-ai/eCommerce-Skills
Dropshipping setup and scaling — supplier integration, automation, pricing strategy, customer experience
tech-leads-club/agent-skills
Helps founders and product teams choose a charge metric, shape pricing tiers and set margin targets for AI products whose compute costs rise with usage.
alirezarezvani/claude-skills
A skill your agent uses when designing or revisiting product pricing — selecting a pricing model (subscription seat-based, usage-based, value-based, freemium, or hybrid), running Van Westendorp…
minhnv0807/ai-business-skills
A skill your agent uses when a price has to be set or changed — value-based pricing, anchoring, charm pricing, decoy tiers, good-better-best packaging, discount policy, and margin math, with…
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Categories
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.
Apiclaw Analysis fits situations like: user asks about: product selection; finding products to sell; competitor lookup; market opportunity.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.