“review”的搜索结果

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 response time, exporting reviews for BI dashboards, lead generation from active reviewers, ML / fake-review detection, and aggregator sites that display third-party reviews.

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Review changes of 1,000 lines or more by dividing work across files, focused areas, and commits. Combine several review methods, check consistency across subsystems, verify bug fixes, and report validated findings alongside a checklist showing review coverage.

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• Reviewing feature branches with 1,000 or more changed lines
• Checking a large change before merging
• Finding inconsistencies across commits and subsystems

Walmart product reviews scraper: given a walmart.com product item ID, navigate to the reviews page and extract paginated customer reviews including reviewId, rating, title, review text, author nickname, submission date, verified purchase status, helpful votes, variant selected (color/size), badges, fulfilled by, seller name, and photo count. Use when user mentions walmart reviews, walmart product reviews, walmart customer reviews, scrape walmart reviews, extract walmart reviews, walmart review scraper, walmart ratings and reviews, walmart review data, walmart review text, walmart review pagination, get reviews from walmart, walmart review collector, walmart verified purchase reviews, walmart review analysis, walmart sentiment analysis, walmart review export, walmart buyer feedback. Also applies to bulk collection of walmart product reviews across multiple items, sentiment analysis on walmart reviews for market research, competitor product review benchmarking on walmart, and monitoring new walmart reviews over time.

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Simulate peer review through five perspectives covering journal fit, methods, subject expertise, interdisciplinary implications, and counterarguments. Receive separate review reports, an editorial decision, and a revision roadmap, or check whether revisions address earlier comments.

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• Simulate peer review before journal submission
• Check research methods and statistical validity
• Test core arguments against counterarguments

Fetch and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.

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5つの専門エージェント（backend-reviewer, frontend-reviewer, test-reviewer, security-reviewer, ux-reviewer）がチームでコードレビューするスキル。 git diff ベースで変更ファイルを検出し、各専門家が並列でレビューして 統合レポート（Markdown）を docs/reviews/ に出力する。 Use when: コードレビューしたい、変更をレビュー、レビューチーム、 チームレビュー、実装をチェック、品質チェック。 Triggers: "レビュー", "review", "コードレビュー", "code review", "チームレビュー", "team review", "変更をチェック", "品質チェック"

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Extract customer reviews from any e-commerce product page or reviews page. Returns reviewer name, star rating, date, review title, review body, verified purchase status, and helpful votes per review. Works on Amazon, WooCommerce, Shopify, and any site with standard review markup. Supports pagination for multi-page review sections. Use when: product reviews, customer feedback, review scraping, get reviews, sentiment analysis data, review extraction, customer ratings, extract customer opinions, product feedback, user reviews, review mining, bulk review collection, review analysis, scrape ratings and comments, ecommerce review data.

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Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.

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Turn a Markdown code review into a single HTML file with code changes beside severity-labeled comments. Readers can jump to findings, inspect line numbers, and identify the named human reviewer.

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• Turning a lengthy Markdown code review into HTML
• Reading code changes beside review comments
• Jumping directly to findings by severity

Review vendor agreements, NDAs, and SaaS contracts against an established contract playbook. Identify the main agreement and attachments, select the relevant review skills, and combine deviations, proposed wording, and required approvers into one memo.

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• Reviewing a vendor agreement against internal contract standards
• Selecting the appropriate review workflow for an NDA
• Reviewing a SaaS contract together with its order form and SLA

Runs repeated PR reviews and fixes on the directories changed by the current branch. Uses an installed reviewer, checks editing scope after each round, and reports whether blocking findings were resolved or the loop stopped.

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• Cleaning up a branch before opening a pull request
• Fixing review findings before updating a pull request
• Repeating reviews after each fix

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

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修正完了後に agent-review-kit でレビューHTMLを生成し、ユーザーがブラウザで書いた差分コメントを Codex または Claude Code の同一セッションで受け取り、回答・修正・resolve を未解決0件まで繰り返すレビューループ。AIレビューの指摘を add-comment でブラウザ上のコメントとして表示し、ユーザーが返信した指摘だけ修正する「AIレビューモード」も含む。実装プラン・設計書などの任意HTMLを publish-html でレンダリング済みのまま表示し、要素クリック・テキスト範囲でコメントを受け取る「HTMLレビューモード」も含む。他のレビュースキルの結果の出力先として agent-review-kit が指定された場合も、このスキルを読んで AI レビューモードの手順に従うこと。Use when the user wants to review changes in a browser (GitHub-like diff review), review a rendered HTML doc (plan/design doc) in a browser, iterate on fixes with inline comments, or display AI review findings as browser comments (output target: agent-review-kit).

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Review code changes or documents through configured review lenses, each representing a different perspective. Findings identify the location, triggering condition, suggested fix, and potential consequence, with reports available as JSON, Markdown, or both.

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• Reviewing a pull request
• Checking uncommitted changes
• Reviewing specifications or documents

Reviews Cherry Studio code and documentation against product decisions, architecture rules, implementation checks, and conventions. It selects a review approach based on scope and available agents, then reports findings with guidance for fixes.

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• Reviewing local changes or a branch
• Checking a pull request before merging
• Reviewing architecture documents and project skills

Review changes since the last npm release through parallel change analysis, hands-on quality checks, and an overall release assessment. Receive a readiness verdict, blocking issues, version recommendations, and a draft changelog with compatibility concerns.

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• Assess readiness before an npm release
• Check breaking changes and migration needs
• Review version bump recommendations

Turn previously fetched pull request reviews into an ordered action plan. Separate distinct findings, track deferred work in Linear, validate that every triage decision is complete, and produce JSON and Markdown files for the implementation phase.

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• Planning responses to fetched PR review feedback
• Separating multiple findings within a single review
• Tracking deferred review work in Linear

Iteratively improve research by having Claude Code review code, results, and drafts while Codex implements fixes. Repeat until the assessment meets the stopping criteria or the round limit is reached, preserving feedback and unresolved issues.

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• Repeating research reviews and fixes before submission
• Checking whether claims match code and experiment results
• Tracking how reviewer concerns are addressed across rounds

VS-Enhanced Literature Review Strategist - Comprehensive support for multiple review methodologies Full VS 5-Phase process: Prevents Mode Collapse and presents creative search strategies Supports: Systematic Review (PRISMA 2020), Scoping Review (JBI/PRISMA-ScR), Meta-Synthesis, Realist Synthesis, Narrative Review, Rapid Review Use when: conducting any type of literature review, systematic reviews, meta-analyses, scoping reviews, finding prior research Triggers: literature review, PRISMA, systematic review, scoping review, meta-synthesis, realist synthesis, narrative review, rapid review

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Produce honest product reviews and buying guides without fabricated first-hand experience, using disclosed evidence tiers: verified specs, owner-experience synthesis at scale, expert triangulation, and hands-on only when true. Use this skill whenever the user wants to write product reviews or buying guides without hands-on access, set up a review methodology, add evidence disclosure to review content, align reviews with Google's reviews system or FTC affiliate disclosure expectations, or decide when Review and Product schema are honest to use. Triggers on product review, buying guide, best-of list, review methodology, evidence basis, hands-on testing, we tested, affiliate review site, review disclosure, methodology block, original research, reviews system. Also triggers when review content claims testing that did not happen, or when an affiliate site needs a trust mechanism that survives scrutiny.

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Drive a PR to an approved code review by looping OCR's multi-agent review and address steps. Runs /ocr:review then /ocr:address repeatedly until the review verdict is APPROVE, then one final /ocr:address for leftover suggestions, posting every review and every address round to the GitHub PR as comments. Use when the user asks to 'review and address in a loop', 'iterate review until approved', 'auto review-and-fix this PR', 'loop /ocr:review and /ocr:address', or to take a changeset all the way to a clean APPROVE with the default reviewer team. Wraps the /ocr:review (.ocr/commands/review.md) and /ocr:address (.ocr/commands/address.md) skills; needs a feature branch with an open GitHub PR and the gh CLI for posting.

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使用共享的OpenClaw审查流程和辅助脚本检查代码，同时遵循目标仓库的专属要求。实际审查流程来自统一安装的共享技能。

• 按统一流程审查代码
• 检查仓库专属审查要求
• 集中更新共享审查流程

Review code changes with several independent reviewers working in parallel, then receive organized findings. Request a quick or thorough review, choose to hand off fixes, or run without interaction when another workflow or CI job initiates the review.

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• Review code changes from multiple independent perspectives
• Choose a quick or thorough review
• Receive findings with fixes handed off

Review Git changes with the ocr command-line tool and a configured AI model. Get line-level findings grouped by severity for working changes, commits, or branch comparisons, with code fixes applied when explicitly requested.

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• Reviewing changes before a commit
• Checking a pull request or branch comparison
• Inspecting a specific commit for issues