trustpilot-reviews

Mechanism

When It Triggers · How It Works · What It Produces

Breaks down how the skill works and what it produces, so you can quickly judge whether it fits your scenario and value.

Trigger

When a user wants to extract, monitor, or analyze customer reviews from a company's Trustpilot page (trustpilot.com/review/{domain}), with optional filters by star, language, date, verified status, reply presence, keyword, or reviewer country.

Working Mechanism

Build a filtered Trustpilot review URL from the company domain and optional query params.
Navigate via browser-act, wait for stable render, then run scripts/extract-reviews.py through bash to parse the embedded __NEXT_DATA__ SSR JSON.
Script outputs 20+ structured fields per review plus pagination; optional flags add reviewer metadata, reply dates, extended metadata, photos, and client-side country filter.
Loop over page param to collect full filtered review set.

Result

A paginated JSON of Trustpilot reviews with rich structured data (rating, text, reviewer, company reply, etc.) usable for sentiment analysis, brand monitoring, competitor research, BI export, or fake-review detection.

Use Cases

Two Typical Use Cases

Scenario 01

Weekly brand reputation monitoring on Trustpilot

A consumer brand wants to track its Trustpilot reputation weekly. Using this skill, they loop through review pages with `date=last7days` and `--include-reply-analysis`, exporting all new reviews and company response times to a BI dashboard. This helps the CRM team spot dropping TrustScores early, measure response SLA, and prioritize at-risk customer outreach — turning scattered public feedback into actionable reputation KPIs.

Scenario 02

Competitor review analysis for SaaS teardown

A market analyst compares two competing SaaS tools by pulling filtered Trustpilot reviews (`stars=1` and `stars=5`, `languages=en`) for both domains. Using the extracted review text and `reviewLabel`/`reviewSource` fields, they train a sentiment classifier and identify feature gaps mentioned in negative reviews. The structured dataset accelerates competitive teardown and product positioning without manual copy-paste.

Skill Relationships

Which Skills I Use · Which Skills Use Me

Dependency relationships read as "the upper tier points to the lower tier." The current Skill sits in the middle tier — above are Skills that depend on it, below are Skills it depends on.

Tier 1 · These Skills Use Me

No explicit dependency

Tier 2 · Current Skill

trustpilot-reviewsWorkflow

Tier 3 · I Use These Skills

browser-act-skill-forgeWorkflow

Full Relationship Graph

Browse this skill's relationships within its skillset. Click a node to switch the side panel; use the search box to jump to any skill.

Skill File

SKILL.md

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Skill Composition

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SKILL.md50.0%
scripts50.0%

▸skills/trustpilot-reviews/
▸root
- ·SKILL.md
- ▸scripts
  - ·extract-reviews.py

Per the Agent Skills spec, SKILL.md is the only required file. The colored bars classify the rest into the three spec-aligned optional directories (scripts / references / assets) and an 'other' bucket. The full directory tree is below.

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Cli Manual

/trustpilot-reviews

When It Triggers · How It Works · What It Produces

Trigger

Working Mechanism

Result

Two Typical Use Cases

Weekly brand reputation monitoring on Trustpilot

Competitor review analysis for SaaS teardown

Which Skills I Use · Which Skills Use Me

Full Relationship Graph

SKILL.md

Skill Composition

You May Also Need These Skills

amazon-asin-lookup-api-skill

amazon-product-api-skill

airbnb-search-listing

airbnb-listing-detail

When It Triggers · How It Works · What It Produces

Trigger

Working Mechanism

Result

Two Typical Use Cases

Weekly brand reputation monitoring on Trustpilot

Competitor review analysis for SaaS teardown

Which Skills I Use · Which Skills Use Me

Full Relationship Graph

SKILL.md

Skill Composition

You May Also Need These Skills

amazon-asin-lookup-api-skill

amazon-product-api-skill

airbnb-search-listing

airbnb-listing-detail