「review」の検索結果

Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.

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Professor's review queue — student output waits here for professor approval before going to clients or courts. Only active if "formal review queue" supervision style was chosen at setup; otherwise dormant. Use when the professor wants to see what's waiting for review, approve, edit-then-approve, or return an item.

日本語の概要は準備中です。原文の説明を表示しています。

First-pass privilege log review — make the obvious privilege calls and flag the hard ones for attorney review without making close calls. Use when the user says "review the privilege log", "priv log", "check privilege on these docs", or has a log to QA before production.

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Review Apache DataFusion pull requests following the project's PR review guide. Use whenever asked to review a DataFusion PR or PR URL, and whenever creating a PR, to check the changes against the same criteria before submitting.

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Triages a repository's open GitHub issues and pull requests via the gh CLI. Optionally reviews and merges ready PRs — incrementally merging passing automated/bot PRs and maintainer-approved ones, and spawning review subagents for never-reviewed ones — then closes already-resolved issues with comments citing the resolving PR or commit, cross-links issues with their pending fix PRs, and assigns local-only priority and change-size estimates for everything outstanding. Use when triaging, grooming, or reviewing a repository's open issues and PRs.

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Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase to be audited by someone else, getting a repository review-ready before an external security review, deciding what to fix before auditors start, or asking what assessors need from a project. For understanding unfamiliar code you are about to audit, use audit-context-building instead.

日本語の概要は準備中です。原文の説明を表示しています。

Reviews code changes using CodeRabbit AI. Use when user asks for code review, PR feedback, code quality checks, security issues, or requests fix-review cycles.

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Analyze PR review comments from a GitHub PR URL. Fetch review comments, verify each finding against the actual codebase, assess validity (correct/incorrect/partial), present a structured summary with recommended actions, and optionally reply to each comment on GitHub. Use when given a PR review URL or when asked to check/analyze PR feedback.

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Interactive system flow tracing across CODE, API, AUTH, DATA, NETWORK layers with SQLite persistence and Mermaid export. Use for security audits, compliance documentation, flow tracing, feature ideation, brainstorming, debugging, architecture reviews, or incident post-mortems. Triggers on audit, trace flow, document flow, security review, debug flow, brainstorm, architecture review, post-mortem, incident review.

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Publication Specialist - Writing, Review, Pre-registration & Quality Assurance Light VS applied: Avoids template-based writing + audience-specific message design Absorbed G3 (Peer Review Strategist), G4 (Pre-registration Composer), F1-F3 (Quality functions) capabilities Use when: writing abstracts, creating summaries, peer review response, pre-registration, reporting checklists, reproducibility Triggers: abstract, plain language, press release, summary, communication, peer review, revision, pre-registration, OSF, PRISMA, CONSORT, reproducibility

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Structured literature review using a parallel fleet of Librarian agents. Searches top journals, working paper repositories (NBER, SSRN, IZA), and traces citation chains from key papers. Make sure to use this skill whenever the user wants to survey existing research on a topic — not to find datasets or write a paper. Triggers include: "review the literature", "find related papers", "what's been done on X", "search for papers on", "do a lit review", "find papers about", "what papers should I cite", "who has written about this", "survey the literature", "find prior work on", or any request to locate and summarize academic publications on a topic.

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Generate master's thesis review reports (硕士论文评阅意见) calibrated to a given score. Outputs academic evaluation and shortcomings/suggestions in Chinese. Trigger when user says "master thesis review" / "硕士论文评阅" / "论文评阅" / "评阅意见" / "thesis review".

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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 9-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

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Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.

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Queue microsoft/apm pull requests labelled `panel-review` (or an explicit named list) and fan them out through an isolated pool (default 2) of autopilot-pr-review-worker sessions. Advisory only. Never implements. Never composes autopilot-pr-merge-worker. Never review every open PR. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow. Does not triage issues or open greenfield PRs.

日本語の概要は準備中です。原文の説明を表示しています。

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Cross-model second opinion from Google Gemini — a different AI reviewing the same changes, with deep Google ecosystem knowledge. Three modes: review (pass/fail gate for Google Ads campaigns, SEO metadata, or code), challenge (adversarial stress-test that tries to break your changes), and consult (open Q&A with Gemini on Google Ads strategy, SEO best practices, or implementation questions). Use when the user says "gemini review", "ask gemini", "gemini challenge", "second opinion from gemini", "consult gemini", "stress test with gemini", "what would gemini say", "cross-model review", or "get another opinion". Voice aliases: "gem", "gemini check". Especially useful for Google Ads changes, SEO metadata updates, campaign structure decisions, keyword strategies, and bid/budget changes — Gemini has native Google ecosystem knowledge that complements Claude's analysis.

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Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.

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Review how the lean-ctx ctx_* MCP tools performed in the current session and file upstream issues for confirmed problems. Use when the user asks to review/audit lean-ctx tool performance, says "lean-ctx review", asks why ctx_* tools failed or fell back to native tools, or wants to report a lean-ctx bug.

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Review a PipesHub pull request or diff for correctness, scalability, null safety, reuse, design and tests. Use when asked to review a PR, a branch, a commit or the current diff, and when CI runs the automated review.

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Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.

日本語の概要は準備中です。原文の説明を表示しています。

Review-feedback handling route for CodeRabbit, GitHub, PR, or human reviewer comments. Use before implementing suggestions to verify each finding. Do not use for a fresh code review, security audit, TDD, or final completion evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Default code-quality route for broad code review, PR review, maintainability, correctness, and regression-risk checks. Do not use as the primary route for dedicated OWASP/security audits, review-feedback handling, completion verification, AI-code cleanup, or TDD/test-first work.

日本語の概要は準備中です。原文の説明を表示しています。