Trustpilot Reviews logo

Trustpilot Reviews

OrganizationPopular
browser-act
trustpilot-reviews

Trustpilot customer reviews scraper for any company listed on trustpilot.com — given a company domain (e.g. shopify.com, apple.com, shopwagandtail.com) plus optional filters (page number, single star rating 1-5, single language ISO code, verified-only flag, has-company-reply flag, date period last30days/last3months/last6months/last12months, free-text keyword search, and reviewer countryCode client-side filter), extracts paginated reviews with 20+ fields per review: reviewId, reviewUrl, reviewTitle, reviewDescription, reviewRatingScore (1-5), reviewDate (ISO publishedDate), reviewDateOfExperience, reviewLabel, isReviewVerified, reviewer (display name), reviewerId, reviewersCountry (ISO Alpha-2), reviewLanguage, reviewCompanyResponse (the company reply text), plus pagination metadata (currentPage, totalPages, totalCount). Optional flags add reviewer profile metadata (reviewerNumberOfReviews, reviewerProfileIsVerified), reply analysis dates (companyReplyPublishedDate, companyReplyUpdatedDate), extended metadata (reviewSource, reviewVerificationSource, reviewLikes), and review photos. Use when user mentions Trustpilot reviews, scrape Trustpilot, get reviews from Trustpilot, Trustpilot review scraper, Trustpilot customer reviews, extract Trustpilot reviews, Trustpilot review data, download Trustpilot reviews, paginate Trustpilot, paginated Trustpilot reviews, filter Trustpilot reviews by star, filter Trustpilot by language, verified reviews Trustpilot, has reply Trustpilot, company response Trustpilot, Trustpilot reply analysis, brand monitoring Trustpilot, sentiment analysis Trustpilot, competitor reviews Trustpilot, bulk reviews from a Trustpilot page, batch download reviews, trustpilot.com reviews scraping, fake review research Trustpilot, Trustpilot dataset, persona research from Trustpilot reviews, Trustpilot keyword search reviews, Trustpilot date range reviews, review export Trustpilot. Also applies to comparing review trends across competitors, training data for LLM sentiment classifiers, monitoring competitor

Overview

Publisherbrowser-act
Repositoryskills
Skill nametrustpilot-reviews
Stars
6.1K
Forks
310
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by browser-act on GitHub. Read the source before you install it.

Installation

Install the Trustpilot Reviews AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/browser-act/skills/tree/main/solutions/social-listening/trustpilot-reviews
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/browser-act/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/solutions/social-listening/trustpilot-reviews .claude/skills/trustpilot-reviews
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trustpilot Reviews in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Trustpilot Reviews on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Trustpilot Reviews is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Trustpilot — Reviews

Input a company domain plus optional filters → output paginated review records with reviewer info, ratings, dates, company replies, and 20+ structured fields per review.

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Extract one page of customer reviews from trustpilot.com/review/{domain} with the chosen filters applied. Designed to be called in a loop across pages to collect the full filtered review set.

Prerequisites

  • Target page is reachable on the public web: https://www.trustpilot.com/review/{domain}
  • No login required — Trustpilot review pages are publicly accessible

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; it is recommended to use the bash tool for execution.

URL Pattern (server-side filters)

Trustpilot accepts filter values as URL query parameters. Build the URL before navigating:

https://www.trustpilot.com/review/{domain}?page={page}&languages={lang}&sort=recency&stars={stars}&verified={true|false}&replies={true|false}&date={period}&search={keyword}

All parameters except domain are optional. Recommended construction rules:

  • page: integer ≥ 1 (omit for page 1; pagination defaults to 20 per page)
  • languages: single ISO code (e.g. en, da, de, es, fr); omit or use all for no language filter
  • sort: only recency is currently honoured by the public review page (older relevancy values are stripped on redirect)
  • stars: single value 1, 2, 3, 4, or 5 — only one star tier can be selected
  • verified: true to keep only verified reviews
  • replies: true to keep only reviews that received a company response
  • date: one of last30days, last3months, last6months, last12months
  • search: URL-encoded keyword to search inside review text

Country-of-reviewer filtering is not supported by Trustpilot's URL — it is applied client-side after extraction using --filter-country (see below).

DOM: extract reviews from current page (SSR JSON)

The Trustpilot review page is server-rendered with Next.js; the full reviews array is embedded in <script id="__NEXT_DATA__">. Extraction reads it directly — no separate API call needed.

Steps:

  1. Build the URL as described above.
  2. navigate {built-url}
  3. wait stable --timeout 30000
  4. eval "$(python scripts/extract-reviews.py [options])"

Parameters (all optional flags / values):

  • --include-reviewer-metadata: adds reviewerNumberOfReviews, reviewerProfileIsVerified.
  • --include-reply-analysis: adds companyReplyPublishedDate, companyReplyUpdatedDate (null when no reply).
  • --include-extended-metadata: adds reviewSource (e.g. Organic, Shopify, InvitationApi), reviewVerificationSource (e.g. invitation, complianceDocumentation), reviewLikes.
  • --include-review-photos: adds reviewPhotoUrls array via best-effort DOM scan of the review card; returns [] when no photos.
  • --no-experience-date: omit the default reviewDateOfExperience field.
  • --filter-country {ISO2}: client-side filter — keep only reviews whose reviewer countryCode matches the given ISO Alpha-2 code (e.g. US, GB, DK). Case-insensitive. Applied after the page-level filters from the URL.

Output example (one page, with all optional flags):

json
{
  "reviews": [                                              // array of review objects (≤ perPage items)
    {
      "companyName": "Wag + Tail",
      "companyPageUrl": "https://www.trustpilot.com/review/shopwagandtail.com?page=1&languages=en",
      "businessUnitId": "624c24851220e2743a4d7916",         // Trustpilot internal company ID
      "reviewId": "65fd6a055de8c560404349e1",
      "reviewUrl": "https://www.trustpilot.com/reviews/65fd6a055de8c560404349e1",
      "reviewTitle": "Great product looks so cute on my girl",
      "reviewDescription": "Great product looks so cute on my girl and fits so well",
      "reviewRatingScore": 5,                                // 1-5
      "reviewDate": "2024-03-22T13:22:45.000Z",              // when review was posted
      "reviewLabel": "verified",                             // "verified" or "not-verified"
      "isReviewVerified": true,
      "reviewer": "Angela Rosalie",
      "reviewerId": "656cc367ad3fb4001307cba8",
      "reviewersCountry": "US",                              // ISO Alpha-2
      "reviewLanguage": "en",
      "reviewCompanyResponse": "",                           // empty string when no reply
      "scrapedDateTime": "2026-06-26T04:13:03.026Z",
      "scrapedAtReviewPageNumber": 1,
      "reviewDateOfExperience": "2024-01-27T00:00:00.000Z",  // default; remove with --no-experience-date
      "reviewerNumberOfReviews": 2,                          // --include-reviewer-metadata
      "reviewerProfileIsVerified": false,                    // --include-reviewer-metadata
      "companyReplyPublishedDate": null,                     // --include-reply-analysis (null when no reply)
      "companyReplyUpdatedDate": null,                       // --include-reply-analysis
      "reviewSource": "Shopify",                             // --include-extended-metadata
      "reviewVerificationSource": "invitation",              // --include-extended-metadata
      "reviewLikes": 0,                                      // --include-extended-metadata
      "reviewPhotoUrls": []                                  // --include-review-photos
    }
  ],
  "pagination": {
    "currentPage": 1,
    "perPage": 20,
    "totalPages": 12,
    "totalCount": 231                                        // total reviews matching the active filters
  },
  "activeFilters": {                                         // what Trustpilot actually applied (after URL normalization)
    "languages": "en",
    "sort": "recency",
    "stars": null,
    "verified": false,
    "replies": false,
    "date": null,
    "search": null,
    "countryFilter": null                                    // mirrors --filter-country when set
  },
  "companyContext": {                                        // company snapshot for joining with company-info output
    "businessUnitId": "624c24851220e2743a4d7916",
    "companyName": "Wag + Tail",
    "identifyingName": "shopwagandtail.com",
    "trustScore": 4.7,
    "stars": 4.5,
    "totalReviews": 258
  },
  "totalFiltered": 231,                                      // duplicate of pagination.totalCount, kept for clarity
  "scrapedDateTime": "2026-06-26T04:13:03.026Z",
  "isCompanyFound": true
}

Output example (company not found):

json
{
  "isCompanyFound": false,
  "companyPageUrl": "https://www.trustpilot.com/review/nonexistent-xyz-9999.com",
  "searchedDomain": "nonexistent-xyz-9999.com",
  "scrapedDateTime": "2026-06-26T04:13:45.151Z",
  "reviews": [],
  "pagination": null
}

Error handling: when the script fails to find __NEXT_DATA__ or businessUnit (and the page is not the 404 page) it returns {"error": true, "message": "..."}. Resolution sequence: (1) confirm wait stable finished; (2) re-run the extraction; (3) if Trustpilot served an anti-bot challenge, switch to a stealth browser with proxy and retry once.

Enum Parameters

[DOM] languages — read from the __NEXT_DATA__ SSR data on any Trustpilot review page:

eval "(() => { const s=document.querySelector('script#__NEXT_DATA__'); const d=JSON.parse(s.textContent); const langs=d.props.pageProps.filters.reviewStatistics.reviewLanguages; return JSON.stringify(langs.map(l => ({ code: l.isoCode, name: l.displayName, count: l.reviewCount }))); })()"

Each entry: { code, name, count }. The literal all is also accepted as the URL value to disable the language filter.

[Static] sort: recency (currently the only honoured value on the public review page; older relevancy is stripped on redirect).

[Static] stars: 1, 2, 3, 4, 5 — only one value at a time.

[Static] date: last30days, last3months, last6months, last12months.

[Static] verified, replies: true or false.

[Static] filterByCountryOfReviewers: any ISO Alpha-2 country code (e.g. US, GB, DE, DK). Applied client-side via --filter-country.

Pagination

URL Pagination: URL pattern https://www.trustpilot.com/review/{domain}?page={N}&{otherFilters}. Next page = increment page by 1. Termination conditions: (a) pagination.currentPage >= pagination.totalPages, OR (b) reviews.length === 0 on the current page, OR (c) caller-supplied endAtPageNumber reached.

Recommended loop pattern (bash):

PAGE=1
END=0   # 0 means "until totalPages"
while : ; do
  URL="https://www.trustpilot.com/review/${DOMAIN}?page=${PAGE}&languages=en&sort=recency"
  browser-act --session {S} navigate "$URL"
  browser-act --session {S} wait stable --timeout 30000
  RESP=$(browser-act --session {S} eval "$(python scripts/extract-reviews.py --include-extended-metadata)")
  # append RESP.reviews to a JSONL file
  TOTAL=$(echo "$RESP" | python -c "import sys,json; print(json.loads(sys.stdin.read())['pagination']['totalPages'])")
  PAGE=$((PAGE+1))
  if [ "$PAGE" -gt "$TOTAL" ]; then break; fi
  if [ "$END" -gt 0 ] && [ "$PAGE" -gt "$END" ]; then break; fi
  sleep 2
done

Success Criteria

isCompanyFound === true && Array.isArray(reviews) && pagination.currentPage === expected_page && (reviews.length > 0 || pagination.totalCount === 0) — for an existing company; isCompanyFound === false && searchedDomain !== null && reviews.length === 0 — for a not-found domain.

Known Limitations

  • Trustpilot caps URL filters to one star tier and one language code per request — combining multiple values is not supported by the public URL and silently stripped on redirect.
  • sort=relevancy is no longer honoured on the public review page; only recency is applied. Build URLs accordingly.
  • filterByCountryOfReviewers is client-side only: a page may return up to perPage reviews of which fewer match the country filter, so the returned list per page can be sparse. To collect a fixed-size sample by country, page deeper or remove other restrictive filters.
  • pagination.totalCount represents reviews after server-side filters and may differ from companyContext.totalReviews (Trustpilot's official count may include hidden / machine-filtered reviews).
  • reviewPhotoUrls is best-effort: Trustpilot does not consistently expose review photos in the SSR JSON, so the script falls back to scanning known photo-host URL patterns inside the review card DOM. Reviews without photos return []; some valid photos may be missed on layout changes.
  • Trustpilot redirects page=1 to the bare URL — pagination.currentPage still reports 1, so callers should trust that field rather than the URL.
  • Very large companies (thousands of reviews) may take many pages; budget at least one hour of wall-clock time for full-set extraction and consider rotating sessions if rate-limited.

Execution Efficiency

  • Batch orchestration: Write a bash script to loop through pages serially within a single session; do not parallelize within one browser (prone to triggering anti-scraping restrictions). Refer to rate information in "Known Limitations" above to add appropriate intervals. To increase throughput, open multiple stealth browser sessions and distribute work across them — each session has an independent fingerprint so rate limits apply per session
  • Test before batch execution: After writing a batch script, you must first test with 1-2 items to verify the script runs correctly; only then run the full batch. Never skip testing and execute in batch directly
  • Reduce redundant pre-operations: When multiple steps depend on the same prerequisite state, complete them in batch under that state to avoid repeatedly establishing the same state
  • Error resumption: Save results item by item during batch processing; on failure, resume from the breakpoint rather than starting over

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/trustpilot-reviews-scraper-trustpilot-reviews.memory.md (working directory is determined by the Agent running the Skill, typically the project root or current working directory)

Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.

After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line: {YYYY-MM-DD}: {what happened} → {conclusion}

Normal execution does not write to the file. Do not record what keywords were used or how many results were returned — those are task outputs, not experience.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Trustpilot Reviews AI skill do?

Trustpilot customer reviews scraper for any company listed on trustpilot.com — given a company domain (e.g. shopify.com, apple.com, shopwagandtail.com) plus optional filters (page number, single star rating 1-5, single language ISO code, verified-only flag, has-company-reply flag, date period last30days/last3months/last6months/last12months, free-text keyword search, and reviewer countryCode client-side filter), extracts paginated reviews with 20+ fields per review: reviewId, reviewUrl, reviewTitle, reviewDescription, reviewRatingScore (1-5), reviewDate (ISO publishedDate), reviewDateOfExper...

Why use Trustpilot Reviews on TypingMind?

Because you install it once and use it with any model. Trustpilot Reviews is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Trustpilot Reviews in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/browser-act/skills/tree/main/solutions/social-listening/trustpilot-reviews. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Trustpilot Reviews?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Trustpilot Reviews?

As many as you like. As long as a model supports skills, you can use Trustpilot Reviews with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Trustpilot Reviews AI skill free?

Yes. It is published on GitHub by browser-act under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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