amazon-review-intelligence-extractor

amazon-review-intelligence-extractor is a skill for Claude Code, Codex from SerendipityOneInc/ZooData-Skills. It costs 125 tokens per session (5,668 once invoked), scanned C, original, MIT.

An Amazon review analysis tool that extracts consumer insights from pre-analyzed reviews. It identifies pain points, buying factors, user profiles, usage patterns, competitor sentiment differences, and opportunities to stand out.

In plain words
What is it for?
Use it to compare customer sentiment across products, find unmet needs and differentiation ideas, and create suggestions for Amazon listing copy.
Why use it?
It helps sellers understand what customers value and complain about without manually reading large numbers of reviews. Those findings can guide product positioning and listing wording.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to compare customer sentiment across products, find unmet needs and differentiation ideas, and create suggestions for Amazon listing copy.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor
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 SerendipityOneInc/ZooData-Skills --skill amazon-review-intelligence-extractor
Clone the repo
git clone --depth 1 https://github.com/SerendipityOneInc/ZooData-Skills

Made for: Claude Code, 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 amazon-review-intelligence-extractor

README.md
[![agentmods](https://agentmods.dev/badge/skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor/github.svg)](https://agentmods.dev/skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor)
Your own site
<a href="https://agentmods.dev/skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor"><img src="https://agentmods.dev/badge/skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor/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 amazon-review-intelligence-extractor

Your own site · 80×15
<a href="https://agentmods.dev/skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor"><img src="https://agentmods.dev/badge/skills/serendipityoneinc/zoodata-skills/amazon-review-intelligence-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,668 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00125 $0.05668
Opus 5 $0.00063 $0.02834
Sonnet 5 $0.00025 $0.01134
Haiku 4.5 $0.00013 $0.00567

Measured 12d ago against content hash c30eedb27ef5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade C, and why

amazon-review-intelligence-extractor scanned grade C with 1 finding 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 1 executable file (scripts/zoodata.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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

Remove the directory (`rm -rf "$WORK"`) after Step 4 succeeds or the workflow aborts.
amazon-review-intelligence-extractor/SKILL.md · 389 lines

How it starts

The opening of the file, as written. The whole thing — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Amazon Review Intelligence Extractor — 11 Dimensions, 1B+ Reviews

Pre-analyzed consumer insights. Pain points, buying factors, user profiles, differentiation gaps.

Files

  • Script: {skill_base_dir}/scripts/zoodata.py — run --help for params
  • Reference: {skill_base_dir}/references/reference.md (field names & response structure)

Credential

Required: ZOODATA_API_KEY. Get free key at zoodata.ai/api-keys

Capabilities & Data Flow

  • Network: only https://api.zoodata.ai (Bearer ZOODATA_API_KEY). Setting ZOODATA_BASE_URL to an untrusted host (anything other than api.zoodata.ai / *.zoodata.ai / localhost) makes the CLI refuse the request and withhold the key — the Bearer token is never sent to an untrusted host.
  • Execution: bundled shared ZooData CLI {skill_base_dir}/scripts/zoodata.py (Python 3, stdlib-only). This skill allows analyze, review-deepdive, product, categories, check, plus the review fallback toolkit (reviews-raw / review-tag-prompt / review-reduce-prompt / review-aggregate). Do not invoke unrelated subcommands for this skill's tasks — the bundled manifest {skill_base_dir}/scripts/allowed-commands.json enforces this: the CLI refuses out-of-scope subcommands with a structured COMMAND_NOT_ALLOWED error before any API request.
  • Local files: a private temporary working dir (created with mktemp -d, removed when the fallback completes) during the review fallback; reads the optional credential store ~/.zoodata/config.json.
  • Sent to the API: keywords, category paths, ASINs, marketplace/date and numeric filter values only. Never sent: budget, experience level, risk tolerance, or any other user-profile text — profile inputs map client-side to numeric filters.
  • Credits: every API call consumes account credits. For broad or ambiguous requests, state the estimated credit cost and confirm with the user before running multi-call scans. The composite review-deepdive command executes ~14+ API calls (~10-20 credits) in ONE invocation and has NO skip/trim flags — under a credit cap, use the granular commands instead.

Read the full file on GitHub · 389 lines

Files

What ships with it

6 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 · 389 lines · 125 tokens per session scan C c30eedb27ef5

Subscribe to this mod's changes

amazon-review-intelligence-extractor is a skill published in the GitHub repository SerendipityOneInc/ZooData-Skills (71 stars, last pushed 2d ago), licensed MIT. It adds 125 tokens to every session and 5,668 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

amzscout-research

Amazon-seller research using AMZScout tools — analyze a product by ASIN, evaluate a niche / category, pull keyword & PPC data, and explore a brand's Amazon catalog. Use whenever the user asks whether an Amazon product is worth selling, to analyze a niche or its competition, find/discover products, check a listing's…

amzscout-corp/amzscout-skill-mcp · 125 tokens

pilot-api-gateway

Expose local APIs to the Pilot Protocol network. Use this skill when: 1. You need to expose local APIs to remote Pilot agents 2. You want to provide API access without public internet exposure 3. You're building API-based agent services on Pilot Do NOT use this skill when: - APIs are already publicly accessible - You…

TeoSlayer/pilot-skills · 92 tokens

amazon-product-ranking

Invoke for ANY Amazon seller query about organic search ranking or product visibility in Amazon search results. Trigger signals: ASIN (B0...) plus ranking intent, page 1 goals, search position tracking, rank drops or recovery, ranking campaign setup, SFB (search-find-buy) parameters, cost or budget estimates for…

listingbureau/listingbureau-mcp · 144 tokens

non-json-content-types

Handle FormData, file uploads, Blob, Uint8Array, and ReadableStream inputs in tRPC mutations. Use octetInputParser from @trpc/server/http for binary data. Route non-JSON requests with splitLink and isNonJsonSerializable() from @trpc/client. FormData and binary inputs only work with mutations (POST).

trpc/trpc · 75 tokens

horse-integration-tests

Guide for writing automated integration tests for Horse endpoints using DUnit/DUnitX and THTTPClient.

HashLoad/horse · 25 tokens

smoke-test

Run smoke tests against a deployed or local app based on your git diff. Each test uses Skyvern browser tools (navigate, act, validate, screenshot) with Chrome DevTools MCP as fallback. Posts screenshot evidence as PR comments.

Skyvern-AI/skyvern · 50 tokens