19.3K skills · sorted by relevance
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.
Review development plans with external AI tools, then revise and review them again while actionable concerns remain. Choose reviewers and a cycle limit to work toward a plan with resolved review findings.
Review pull requests at a specific commit using existing discussions, review plans, and executed checks. Produce a detailed Chinese review and a concise English verdict, then publish and read back the result to confirm its contents.
Collect Google Maps reviews through BrowserAct using search keywords, language, and country settings. Retrieve reviewer names, ratings, review text, dates, and likes as structured data for reputation research, competitor comparisons, and customer feedback analysis.
Draft, audit, or revise responses to reviewers for Nature-family journals. Links each reviewer concern to manuscript evidence or a documented change, and identifies unanswered comments, missing author input, and issues that need resolution before submission.
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.
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.
Review a pull request using repository checks, verified code references, and current official documentation. Assess correctness, security, structure, and unnecessary code, then post one consolidated GitHub review with inline findings and an evidence-based verdict.
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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Review code using shared OpenClaw instructions and a Python helper, while also following the target repository’s review requirements. This skill points to a common installation that supplies the actual review procedure.
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.
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.
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
Plan revenue growth for B2B SaaS businesses by analyzing sales opportunities, pricing, customer retention, and sales capacity. Develop scenario forecasts, churn intervention plans, hiring models, and revenue reporting for board discussions.
Run Polly AI Review and Open Code Review repeatedly on a pull request. Track each finding, fix relevant issues, document justified dismissals, and verify that review evidence and recorded decisions cover the latest commit.
Ask external AI command-line tools to independently review a development phase plan. Collect feedback by reviewer in REVIEWS.md, then feed the results into GSD planning to support revisions.
Review medium to large code changes using checks selected for the actual patch. Analyze the change and surrounding code, assign focused reviews by function, file, or feature, and combine findings with locations, confidence levels, and supporting evidence.
Extract the same fields from a batch of contracts or other documents into a comparison spreadsheet. Each answer includes a source quote and location, while missing, ambiguous, and review-required information is clearly distinguished for human verification.
Review code using shared OpenClaw instructions and a Python helper, while also following the target repository’s review requirements. This skill points to a common installation that supplies the actual review procedure.
Create a literature review from a topic, journals, and publication period. Multiple agents search academic databases, collect available PDFs and citation records, organize findings by theme, and draft, review, revise, and compile a LaTeX document.
Coordinate research, drafting, citation and data verification, AI review, revision, and final formatting through connected skills. Start from a research topic, an existing manuscript, or reviewer comments, with progress tracking and user checkpoints.
Review code for bugs, security vulnerabilities, API misuse, consistency issues, simplicity problems, or test coverage gaps and low-value tests by running internal reviews and a peer review in parallel and returning combined findings. Single-concern with a type argument, or full review with no argument. Use when the user asks to "review my code", "full code review", "review my changes", "check for bugs", "scan for bugs", "review correctness", "security audit", "find vulnerabilities", "review security", "check API usage", "verify against docs", "check for cross-file duplication", "review consistency", "check for code reuse", "review simplicity", "find untested code", "find redundant tests", or "review test coverage".
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Drive a GitHub Copilot (or any bot reviewer) pull-request review to a clean pass without babysitting it: fix each finding in its own commit, reply to the review thread with the fix sha, resolve the thread, re-request the bot, and poll for the async re-review. Covers the thread node-id (PRRT_...) vs comment databaseId distinction, the resolveReviewThread GraphQL mutation, the copilot-pull-request-reviewer[bot] slug, and how to read the bot's verdict (COMMENTED plus a "human review recommended" banner is not a blocking finding). Use when Copilot has left review comments on your PR, when bot review threads must be closed out with an auditable fix-reply-resolve trail before merge, or when you need to verify that a re-review actually landed on the new HEAD rather than the old one.
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