bggg-data-amazon

bggg-data-amazon is a skill for Codex from binggandata/bggg-skills. It costs 104 tokens per session (1,394 once invoked), scanned A, original, MIT.

A data-collection skill for written Amazon.com product reviews, using a public review web route. It keeps attempts and errors, reconciles incomplete runs, and stores normalized review text as JSONL, a file format with one JSON record per line.

In plain words
What is it for?
It is for voice-of-customer research, competitor review mining, finding low-star complaints, and researching product listings by ASIN, Amazon’s product identifier.
Why use it?
It removes much of the manual work involved in gathering and organizing large batches of reviews. The saved attempt logs and source data make incomplete or failed collection runs easier to audit.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit It is for voice-of-customer research, competitor review mining, finding low-star complaints, and researching product listings by ASIN, Amazon’s product identifier.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/binggandata/bggg-skills/bggg-data-amazon
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add binggandata/bggg-skills --skill bggg-data-amazon
Clone the repo
git clone --depth 1 https://github.com/binggandata/bggg-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for bggg-data-amazon

README.md
[![agentmods](https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-data-amazon/github.svg)](https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-amazon)
Your own site
<a href="https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-amazon"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-data-amazon/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for bggg-data-amazon

Your own site · 80×15
<a href="https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-amazon"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-data-amazon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,394 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00104 $0.01394
Opus 5 $0.00052 $0.00697
Sonnet 5 $0.00021 $0.00279
Haiku 4.5 $0.00010 $0.00139

Measured 12d ago against content hash 497e31b94105, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

bggg-data-amazon scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/amazon_review_scraper.py, scripts/normalize_reviews.py, scripts/run_batch.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

bggg-data-amazon/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 108 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 108 lines · 104 tokens per session scan A 497e31b94105

Subscribe to this mod's changes

bggg-data-amazon is a skill published in the GitHub repository binggandata/bggg-skills (594 stars, last pushed 29d ago), licensed MIT. It adds 104 tokens to every session and 1,394 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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