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概要と使いどころ
Build and verify Feedback submission widget for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Feedback submission widget for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Feedback submission widget for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Feedback submission widget for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Recent activity feed for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Recent activity feed for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Recent activity feed for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Recent activity feed for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Build and verify Recent activity feed for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.
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。
Iterate on a PR until actionable CI passes and high/medium review feedback is addressed. Use for PR CI failures, review feedback, or green-check loops; do not wait for human approval, draft status, or merge gates.
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。
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage 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。
Use to process automated PR review feedback (Copilot, Greptile, Codex) and iterate until all AI threads are addressed, checks are green, and the PR is merge-ready. Trigger when the user says "address review feedback", "recycle reviews", or references an automated reviewer.
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。
taskboard の MCP tool（health / board_* / task_* / work_* / doc_* / ask_* / review_request / feedback_list / comment_reply / schedule_* / session_* / orchestrator_* / settings_* / inbox_* / prs_* / usage_get）でタスク・作業項目・文書と、案件・定期起動・セッション・受信箱を扱う。自分のセッションに紐づくタスクの列を進める・起票する時、計画の承認後に作業項目を登録し着手・完了・待ちを更新する時、計画書・設計提案を人に読ませて採否やレビューを求める時、ボードの一覧・状態を確認する時、オーケストレーターとして全体の health を見回る時、案件・定期起動・セッションを作る・変える・止める・消す時、依頼に taskboard・タスクボード・kanban・作業項目の語がある時に使用。境界: taskboard の外で始まったセッションをタスクに移すのは taskboard-adopt、pane・tab・agent の操作は herdr、作業ログ・調査記録はメモリディレクトリ、ボードの画面操作はユーザーの領域、新しい定期起動の中身（仕組みの選択・手順書・プロンプト・状態ファイル）の設計は scheduling-jobs。
Use CodeRabbit CLI to receive external code review feedback and address identified issues. Invoke this skill when the user requests CodeRabbit review, or proactively after implementing significant code changes to get feedback and improve code quality.
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。
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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。
複数回のフィードバック・複数パートのレビュー結果を統合管理。対応状況を追跡し、重複を排除して優先度順のチェックリストを生成。Use for "track review comments", "merge feedback", "consolidate reviews".
Transform review analysis into constructive feedback with actionable improvements. Mentor/shepherd mode - strict but supportive. Use for "give constructive feedback", "shepherd this paper". 旧名 review-shepherd。
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。
Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.
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。
Monta peça única de feed — quote card, card de dado, capa, anúncio estático, imagem de link. Use quando o usuário disser "post", "card", "peça do feed", "imagem do post", "arte para o LinkedIn", "criativo estático", "quote card", "card de dado". Para sequência ver carousel-builder; para vertical ver story-reels.
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。
In-app notifications: AdwToast/ToastOverlay with undo buttons, when to use toasts vs banners vs dialogs. Use when giving feedback after user actions in a GNOME app.
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。
Babysit a GitHub pull request during a user-approved monitoring window by polling review comments, CI checks/workflow runs, and mergeability state. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and report fresh review feedback. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open PR.
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。
Derive and implement first-principles audio-coupled haptic and kinetic UI feedback. Use when designing, reviewing, or coding haptics/audio for mobile apps, native iOS/Android, React Native/Expo, SwiftUI, games, detent pickers, toggles, sliders, page curls, scroll gestures, wallet/signature flows, Solana transactions, backend/realtime state changes, confirmations, settlements, and semantic UI events where feedback should follow signal math instead of ad hoc vibration calls.
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。
Design a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).
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。
Early-user discovery and feedback trials. Use when reviewing unfamiliar AI Matrx accounts, classifying owner relationships, recommending trial plans, or running approved outreach and reply checks.
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。