Agent Skillsbinggandata/bggg-skills › bggg-data-amazon

bggg-data-amazon

GitHub

通过Woot接口批量采集亚马逊商品评论,进行重试、去重和标准化处理,输出JSONL格式数据。用于用户之声分析、竞品研究和投诉挖掘,无需Amazon账号。

bggg-data-amazon/SKILL.md binggandata/bggg-skills

Trigger Scenarios

需要采集亚马逊商品评论数据 进行竞品评论分析或VOC研究 批量获取ASIN的星级评价文本

Install

npx skills add binggandata/bggg-skills --skill bggg-data-amazon -g -y
More Options

Non-standard path

npx skills add https://github.com/binggandata/bggg-skills/tree/main/bggg-data-amazon -g -y

Use without installing

npx skills use binggandata/bggg-skills@bggg-data-amazon

指定 Agent (Claude Code)

npx skills add binggandata/bggg-skills --skill bggg-data-amazon -a claude-code -g -y

安装 repo 全部 skill

npx skills add binggandata/bggg-skills --all -g -y

预览 repo 内 skill

npx skills add binggandata/bggg-skills --list

SKILL.md

Frontmatter
{
    "name": "bggg-data-amazon",
    "description": "Collect Amazon.com written product reviews at scale through Woot's public review AJAX route, retain every attempt and error log, reconcile partial runs, and normalize exact review text into auditable JSONL. Use for VOC, competitor review mining, low-star complaint analysis, listing research, or batch ASIN review acquisition without Amazon credentials. Amazon data layer of the bggg VOC suite (shared project folder; orchestrated by industry-orchestrator, reported by bggg-voc-report)."
}

BGGG Amazon Data

Use the verified Woot review endpoint for Amazon US written reviews. This route needs no Amazon login, browser cookie, developer key, or paid scraper API.

VOC Project Layout(bggg 系列共用)

bggg VOC 系列 skill(bggg-data-amazon / bggg-data-reddit / bggg-data-x / bggg-voc-report / industry-orchestrator)共用一个项目文件夹,让多平台数据规整到同一处、下游分析零改路径。开工先确定项目根目录 <project>(用户指定,或新建 voc-<产品或主题slug>/),并从 <project> 根目录执行本 skill 的全部命令(下文相对路径都基于它):

<project>/
  PROJECT.md            # 研究简报 + 决策日志(编排 skill 维护;单独使用可省)
  config/               # 采集目标:amazon_targets.tsv / reddit_queries.tsv / x_queries.tsv / keywords.txt
  work/<platform>/…     # 各平台原始证据、attempt 日志、request plan、manifest
  data/raw/             # 各平台规范化 JSONL(统一行契约,分析共用层)
  data/clean|coded/     # 下游清洗与编码(industry-orchestrator 维护)
  output/               # 报告与交付物(bggg-voc-report 写 output/report/)

本 skill 的落点:config/amazon_targets.tsvwork/amazon/<run-date>/(证据与 manifest)→ data/raw/amazon_woot_<date>.jsonl

Prepare Targets

Create a tab-separated file:

asin	mode	lang	title
B08422NWYZ	full	EN	Product name
B0XXXXXXXX	basic	EN	Another product

Choose modes deliberately:

  • basic: one unfiltered route, usually up to about 100 written reviews.
  • full: five star filters, usually up to about 100 per star.
  • max: five star filters × four sort orders, then exact dedupe; slower and still subject to the endpoint's visible-result ceiling.

Use full for the highest-priority products and products where 1–3 star feedback matters. Use basic for broad competitive coverage. Do not infer written-review volume from Amazon's total ratings count.

Acquire and Reconcile

python3 scripts/run_batch.py \
  --targets config/amazon_targets.tsv \
  --run-dir work/amazon/2026-07-25 \
  --attempts 3 \
  --workers 2

The runner:

  • saves each attempt JSON plus stdout/stderr logs;
  • caps concurrency at two workers;
  • treats Error (filter= in stderr as a partial-run marker even when exit code is zero;
  • marks HTTP/request failures with no collected rows as failed instead of publishing an empty partial result;
  • marks a successful JSON response with no visible written reviews as complete_no_reviews;
  • retries with backoff;
  • unions every parseable attempt and exact-deduplicates review content;
  • writes acquisition_manifest.json and one reconciled ASIN_mode.json per target.

Never delete failed attempts. They are source evidence and can contain genuine reviews missing from a later retry.

Normalize

python3 scripts/normalize_reviews.py \
  --run-dir work/amazon/2026-07-25 \
  --targets config/amazon_targets.tsv \
  --output data/raw/amazon_woot_2026-07-25.jsonl \
  --summary work/amazon/2026-07-25/normalize_summary.json

Use --keywords keywords.txt to add literal hit labels.

Quality Rules

  • Preserve Title and Text exactly in upstream JSON. The normalized text_raw concatenates them without translation.
  • Build a stable SHA-256 content key from author, title, and body because this route often returns Id=null.
  • Parse the human-readable date in OriginDescription. Treat epoch-like SubmissionDate values as unreliable unless independently verified.
  • Merge the same content across ASIN variants while preserving every observed ASIN in context.asins.
  • Keep star rating, helpful votes, verified-purchase flag, Vine flag, and media URLs.
  • Skip empty bodies and count them. Never fabricate native IDs, dates, authors, or review totals.
  • Record selected mode, attempt health, input rows, unique rows, duplicates, parse failures, and per-ASIN counts.
  • Validate the first live ASIN before launching a large batch. If the route changes schema, stop and inspect rather than emitting empty success files.
  • Use no more than two workers and conservative retries. Back off on timeouts or HTTP errors.

Known Limits

  • Amazon US written reviews only; star-only ratings are unavailable.
  • Each filter/sort combination exposes a limited window, commonly around 100.
  • A high-volume five-star bucket can remain truncated even in max mode.
  • Review author/title/body exact dedupe can merge syndicated variant reviews; retain the ASIN list so the merge remains auditable.
  • The Woot route is public but not a completeness guarantee. Describe results as collected written reviews, not all customer ratings.
  • A valid Amazon ASIN can still be unavailable through Woot. Treat its HTTP 404 as an unsupported target, not proof that the route is globally unavailable.

Resources

  • scripts/amazon_review_scraper.py: verified stdlib Woot scraper, retained from mrlong0129/amazon-review-scraper.
  • scripts/run_batch.py: retries, partial detection, checkpointing, and attempt reconciliation.
  • scripts/normalize_reviews.py: cross-ASIN dedupe and normalized JSONL export.
  • references/schema.md: target file, raw evidence, and output contract.
  • references/source_and_limits.md: provenance, tested behavior, and limits.
  • references/upstream_LICENSE: retained MIT license for the bundled upstream scraper.

Version History

  • 1034ee5 Current 2026-08-16 06:52

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Metadata

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Version
1034ee5
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Indexed
2026-08-16 06:52

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