Results for “review-responses”

Structure responses to referee reports for R&R submissions

Address PR review comments by fetching, evaluating, fixing, replying, pushing, and re-requesting review.

Load this skill when the user says "resolve comments", "resolve-comments", or "/resolve-comments". Checks open PR review comments, addresses valid ones in a single batched commit, and resolves all threads with appropriate replies.

Code Review Reception

Use after Cell reviews arrive to triage the decision, prioritize the (often substantial) new experiments by impact × feasibility, and draft a point-by-point response that is respectful, evidence-led, and closes the "completeness" gaps reviewers demand. Do not run before the experiments and revision are actually done.

Processes a PR's review comments (Copilot or human), classifies them, applies the fixes that belong, replies to each thread, and pushes. Use whenever the user says "address the review", "fix what Copilot said", "handle the comments", mentions Copilot already reviewed a PR, or passes a PR number/URL with pending reviews — or in Spanish "revisá los comentarios", "arreglá lo que dijo Copilot", "atendé el review".

Reviews and tailors Product Manager resumes against 10 best practices including the XYZ+S formula, keyword optimization, and job-specific customization.

Use after NEJM reviews arrive — often including a dedicated statistical reviewer and an editor letter — to triage the decision, answer statistical comments rigorously, and draft a point-by-point response that quotes each comment, the response, and the revised manuscript text. Do not run before the main text is actually revised.

任意のリポジトリ（アプリ/ライブラリ/運用スクリプト/コンテンツ等）の種別を判定し、 種別に応じた評価軸（品質・テスト・依存、または検証ゲート等）で**実測ベース**評価して優先度付き改善案を出すスキル。 言語非依存（Python は AST 詳細・その他は近似と明記）。ベンダーコード混入・除外の空振り・偽の成功終了コードなど、 「黙って間違った数値を出す」失敗を構造的に封じる。 「リポジトリレビューして」「コード品質を評価して」「このプロジェクトを診断して」「包括レビュー」「repo-review」 「アーキテクチャ評価して」等で発火。/repo-review でも起動。

Classify reply sentiment and determine next action

Turn interactive commit reviews back on for the current herdr workspace.

Use after PNAS reviews arrive to triage the decision, prioritize experiments, and draft a point-by-point response that is respectful, evidence-led, and honest about limits. Do not run before the main text is actually revised.

Use when responding to a Revise & Resubmit decision from AER, AER:Insights, or an AEJ, and a point-by-point response letter plus aligned manuscript revisions are needed. Handles triage, the concede / clarify / push-back decision, and the response-letter format that editors actually read.

Decide which pull-request review comments are actionable. Use when addressing review feedback, monitoring a PR, or filtering already-handled comments.

Requesting Code Review

Resolve GitHub PR feedback end-to-end for the branch you are on. Use when you need to fetch all comments on the branch PR, evaluate whether each comment still applies, decide whether it should be addressed, implement fixes, run compile/tests with retry-on-failure, then commit and push the result.

Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.

Provides a structured protocol for receiving and evaluating code review feedback, emphasizing technical verification over performative agreement.

Post-epic review to extract lessons and assess success. Use when the user says "run a retrospective" or "lets retro the epic [epic]"

Simulates a NeurIPS/SC/ICSE-style peer review with five reviewer personas (HPC, ML, Stats, Reproducibility, Devil's Advocate) that verify every claim against actual result data before submission.

Guides technical evaluation of code review feedback, emphasizing verification before implementation and reasoned pushback over performative agreement.

Revisa criticamente as especificações geradas pelo reversa-writer — encontra inconsistências, reclassifica confiança e gera perguntas para validação humana. Use na fase de revisão de uma análise de engenharia reversa.

Guides technical verification of code review feedback before implementation, emphasizing reasoned pushback over performative agreement.

Routes quality reviews to specialized critic agents based on file type or flags, covering peer review, code review, and manuscript polish.

Reviews pull request diffs for correctness, security, maintainability, and test coverage, providing severity-ranked findings and a close-out recommendation.

Dispatch a code reviewer subagent to catch issues before they cascade, with precisely crafted context for evaluation.

Remove AI writing patterns from prose. Use this skill when writing, drafting, editing, reviewing, or revising any text to eliminate predictable AI tells, slop, and formulaic patterns. Trigger this skill whenever the user asks to "deslop", "de-AI", "make it sound human," "remove AI patterns," "remove AI tropes," "clean up AI writing," fix "slop," "deslop" text, or review prose for authenticity. Also use when the user asks you to write or draft anything and wants it to sound natural rather than AI-generated. Common use cases include scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses), blog posts, newsletters, memos, reports, and any other substantial prose.