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Agent Skill · 研究检索

apiclaw-analysis(顶端法则分析)

apiclaw-analysis 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

apiclaw-analysis 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 21,368。

总安装 4,892周安装 208GitHub Stars 公开资料未说明下载 1,714核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.2.2

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:apiclaw-analysis(顶端法则分析)
来源仓库:https://github.com/christine-srp/apiclaw-analysis
安装命令:
openclaw skills install apiclaw-analysis
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install apiclaw-analysis

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

针对 FBA/FBM 业务的亚马逊产品研究与卖家分析工具。适用于寻找爆款产品、跟踪竞争对手与监控 BSR 排名。内置 14 种选品策略与多维度风险评估模型。安装命令:openclaw skills install apiclaw-analysis。结果仅供参考,实际销售表现可能存在偏差。

apiclaw-analysis 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install apiclaw-analysis;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

apiclaw-analysis 怎么安装?

在终端执行 openclaw skills install apiclaw-analysis。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

apiclaw-analysis 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

apiclaw-analysis 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

apiclaw-analysis 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

apiclaw-analysis 用的人多吗?

公开数据是累计安装 4,892 次,近一周安装 208 次,下载 1,714 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

apiclaw-analysis 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
apiclaw-analysis

顶端法则分析 · 本页

4,892—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
Amazon Product Research & Seller Analytics
version
1.1.5
description
>
author
SerendipityOneInc
homepage
https://github.com/SerendipityOneInc/Amazon-analysis-skill
metadata
{"openclaw": {"requires": {"env": ["APICLAW_API_KEY"]}, "primaryEnv": "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

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, 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.

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

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✅ 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

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.

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.


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)

---
📋 **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

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

平台分布

OpenClaw

94.37%
按下载量换算1,618

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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