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Agent skill

Bggg Data Amazon

by binggandata in binggandata/bggg-skills

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…

MITAuto-check passedSales & Support

Install Bggg Data Amazon

skills CLI
$ npx skills add binggandata/bggg-skills --skill bggg-data-amazon -a claude-code

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

GitHub CLI
$ gh skill install binggandata/bggg-skills bggg-data-amazon --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/binggandata/bggg-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/bggg-data-amazon .claude/skills/bggg-data-amazon && 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
bggg-data-amazon
GitHub stars
604
Token cost
~1.4k tokens
SKILL.md length
525 words
Files
9 (incl. scripts, references)
Skills in repo
13
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Competitor review mining
  • SKILL.md covers VOC Project Layout（bggg 系列共用）, Prepare Targets, Acquire and Reconcile and Normalize, plus 3 more sections
  • Runs Python scripts from its folder; calls python3
  • Low-star complaint analysis

What it does

Bggg Data Amazon is an agent skill from binggandata/bggg-skills. 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).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/schema.md` and `references/source_and_limits.md`).

It sits in Sales & Support, covering Customer feedback analysis. The repository describes itself as: Open-source Codex skills from BGGG. The licence is MIT.

When your agent uses it

  • Competitor review mining
  • Low-star complaint analysis
  • Listing research
  • Batch ASIN review acquisition without Amazon credentials

Example prompts

  • “/bggg-data-amazon”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 1034ee5. 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 3 files 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

    No URLs in SKILL.md.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Bggg Data Amazon loads about 1.4k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 126 tokens; SKILL.md has 525 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~126
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.2k

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 binggandata/bggg-skills at commit 1034ee5, republished under its MIT licence (© binggandata). 525 words, ~1,405 tokens.

Download SKILL.mdSave it as .claude/skills/bggg-data-amazon/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
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）：

text
<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.tsv → work/amazon/<run-date>/（证据与 manifest）→ data/raw/amazon_woot_<date>.jsonl。

Prepare Targets

Create a tab-separated file:

text
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

bash
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

bash
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.
Show full SKILL.md (143 more words)Show less

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.

© binggandata, 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 8 other files (scripts, references) in bggg-data-amazon of binggandata/bggg-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/schema.md
  • references/source_and_limits.md
  • references/upstream_LICENSE
  • scripts/amazon_review_scraper.py
  • scripts/normalize_reviews.py
  • scripts/run_batch.py
  • tests/test_failure_states.py

Open the folder on GitHubat commit 1034ee5

Compare with similar skills

Bggg Data Amazon 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.

Bggg Data Amazon compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bggg Data Amazon this skillbinggandata/bggg-skills604—~1.4kAutomated safety check: PassMIT
Review Analysisliangdabiao/amazon-sorftime-research-MCP-skill9531 repos~2.5kAutomated safety check: PassNone
Zsxqunnoo/zsxq-skill304—~3.8kAutomated safety check: PassMIT
Roadtrip NavigatorWaybox-AI/roadtrip-skill126—~3.4kAutomated safety check: PassMIT
Always Compareai-analyst-lab/ai-analyst304—~1.4kAutomated safety check: PassMIT
Account Deletiongustavscirulis/snapgrid1171 repos~2.5kAutomated safety check: NotesCustom licence

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Categories

Questions about Bggg Data Amazon

What does Bggg Data Amazon do?

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…. Bggg Data Amazon is an agent skill from binggandata/bggg-skills.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.

When should I use Bggg Data Amazon?

Bggg Data Amazon fits situations like: competitor review mining; low-star complaint analysis; listing research; batch ASIN review acquisition without Amazon credentials.

How do I install Bggg Data Amazon in Claude Code?

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

How do I install Bggg Data Amazon in Codex?

Run `npx skills add binggandata/bggg-skills --skill bggg-data-amazon -a codex`. Or copy the skill folder (bggg-data-amazon in binggandata/bggg-skills) into .agents/skills/bggg-data-amazon in your project. Codex loads it when a task matches its description.

Can I use Bggg Data Amazon 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 binggandata/bggg-skills --skill bggg-data-amazon -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bggg-data-amazon, .gemini/skills/bggg-data-amazon, .github/skills/bggg-data-amazon and .opencode/skills/bggg-data-amazon in your project.

What does Bggg Data Amazon need to run?

Going by SKILL.md and its folder, Bggg Data Amazon needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Bggg Data Amazon access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bggg Data Amazon 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 Bggg Data Amazon use?

Bggg Data Amazon 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 Bggg Data Amazon use?

About 1.4k tokens (SKILL.md is roughly 5.6k 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 774 tokens, read only when the agent opens those files.

What are the alternatives to Bggg Data Amazon?

Skills that share tags, products or a category with Bggg Data Amazon: Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 953 stars), Zsxq (unnoo/zsxq-skill, 304 stars), Roadtrip Navigator (Waybox-AI/roadtrip-skill, 126 stars) and Always Compare (ai-analyst-lab/ai-analyst, 304 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bggg Data Amazon?

binggandata (a GitHub user) maintains it in binggandata/bggg-skills, which has 604 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 13, 2026.

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