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.
npx skills add binggandata/bggg-skills --skill bggg-data-amazongit clone --depth 1 https://github.com/binggandata/bggg-skillsWrote 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.
[](https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-amazon)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.01394 |
| Opus 5.5 | $0.00042 | $0.00558 |
| Sonnet 5.5 | $0.00021 | $0.00279 |
| Haiku 4.5 | $0.00010 | $0.00139 |
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 2mo ago.
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.
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.tsv → work/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
failedinstead 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.jsonand one reconciledASIN_mode.jsonper target.
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.
- agents/openai.yaml 278 B
- references/schema.md 1016 B
- references/source_and_limits.md 1022 B
- references/upstream_LICENSE 1.0 KB
- scripts/amazon_review_scraper.py 11 KB runs code
- scripts/normalize_reviews.py 6.6 KB runs code
- scripts/run_batch.py 11 KB runs code
- tests/test_failure_states.py 2.2 KB runs code
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.
- 2mo ago First seen · 108 lines · 104 tokens per session scan A 497e31b94105
bggg-data-amazon is a skill published in the GitHub repository binggandata/bggg-skills (612 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 1,394 once invoked, about $0.0004 per session on Opus 5.5. 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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