「feedback」の検索結果

Expert blueprint for simulation and tycoon games (SimCity, RollerCoaster Tycoon, Factorio, Two Point Hospital) covering economy management, time progression, interconnected systems, NPC simulation, and feedback loops. Use when building management sims, tycoon games, city builders, or resource optimization games. Keywords tycoon, economy system, resource management, time scale, feedback loop, progression unlock, simulation tick.

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。

Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.

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。

Beta testing strategy for iOS/macOS apps. Covers TestFlight program setup, beta tester recruitment, feedback collection methodology, user interviews, signal-vs-noise interpretation, and go/no-go launch readiness decisions. Use when planning a beta, setting up TestFlight, collecting user feedback, or deciding if ready to launch.

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。

Deep Amazon review analysis for competitive intelligence and product improvement. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from customer feedback. Turn reviews into actionable product development and marketing strategies. Use when the user asks about review analysis, customer feedback, product complaints, sentiment analysis, or what customers think about products.

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 real run evidence, validation failures, source drift, platform drift, and user feedback to plan and, only after explicit approval, apply structural upgrades to an existing skill. Use when the user asks to improve an existing skill from recent runs, recurring failures, outdated sources, excessive bloat, changed platform behavior, or validated workflow feedback. Do not use to create a brand-new skill or to execute the business workflow itself.

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。

Process and implement code review feedback systematically. Use when user provides reviewer comments, PR feedback, code review notes, or asks to implement suggestions from reviews.

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 maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.

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。

Dung khi da co THIET KE va can duyet truoc khi dang — cham diem 4 nhom brand consistency, hierarchy va do doc, yeu to chuyen doi, phu hop nen tang, moi nhom 10 diem, kem feedback theo dang van de - tai sao - cach sua va verdict duyet / sua nhe / lam lai. Kich hoat khi user nhac 'review design', 'duyet thiet ke', 'feedback thiet ke', 'cham diem design', 'anh nay ok chua', 'nhin sao sao ma khong biet sai cho nao', 'check brand', 'approve creative'. Dung ca khi user chi gui anh va hoi 'xem giup'. Khong dung cho — duyet CHU nhu content brief va ads copy thi dung skill 62-marketing-review; do xem trang co ra don khong thi dung skill 68-cro-audit-trang.

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 a finished DESIGN needs scoring and feedback — brand consistency, hierarchy plus readability plus accessibility, conversion elements, and platform fit, 10 points each, with per-issue feedback in issue, why, and how-to-fix form and a verdict of approve, minor fix, or redo. Trigger on 'design review', 'review this design', 'score this creative', 'is this design good', 'brand check on this banner', 'can we ship this or not'. Also use when the user just sends an image and asks for thoughts. Not for — reviewing copy and briefs, see `62-marketing-review-global`; writing the brief before production, see `42-image-brief-global`; the brand rules being checked against, see `46-brand-guideline-global`.

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。

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。

Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.

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。

Search and analyze Reddit content using semantic AI search via reddit-insights.com MCP server. Use when you need to: (1) Find user pain points and frustrations for product ideas, (2) Discover niche markets or underserved needs, (3) Research what people really think about products/topics, (4) Find content inspiration from real discussions, (5) Analyze sentiment and trends on Reddit, (6) Validate business ideas with real user feedback. Triggers: reddit search, find pain points, market research, user feedback, what do people think about, reddit trends, niche discovery, product validation.

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。

Iterative worker-reviewer cycle that spawns a critic subagent to score work 1-10 and provide actionable feedback, then revises until a quality gate is met. Use when implementing features, writing specs, reviewing existing code, or completing any task where quality matters more than speed. Trigger phrases: "use review-loop", "polish this", "iterate on this", "/review-loop", "review with feedback loop".

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, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using the rationale recalled from past Codex session history). Use when the user asks to "resolve PR comments", "fix review comments", "address PR feedback", "handle review comments", "address review feedback", "respond to PR comments", "answer review questions", or "address code review".

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。

Fetch and summarize review feedback and conversation from a GitHub PR (unresolved review threads, review bodies, and PR conversation comments) without making changes. Use when the user asks to "fetch PR comments", "show PR comments", "check PR for unresolved comments", "list review comments", "what comments are on the PR", "show unresolved threads", or "summarize PR feedback".

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, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using the rationale recalled from past Claude Code transcripts). Use when the user asks to "resolve PR comments", "fix review comments", "address PR feedback", "handle review comments", "address review feedback", "respond to PR comments", "answer review questions", or "address code review".

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。

Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".

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。

When the user needs to analyze, categorize, or extract actionable insights from customer feedback across multiple sources, especially feature requests.

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 critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes

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。

Aggregate and analyze customer reviews from G2, Capterra, Trustpilot, App Store, and other platforms. Performs sentiment analysis, identifies pain points, extracts feature feedback, generates marketing claims, and compares competitor reviews. Use when users need review analysis, competitive intelligence, or customer feedback insights.

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。

This skill should be used when the user asks for a "sensitivity reader", "authenticity reader", "cultural review", "is this portrayal okay", "real people in my novel", "defamation", "can I use song lyrics", "epigraph permission", "permissions", "quote permission", "fair use", "AI disclosure", "do I need to disclose AI", "send to my editor", "editorial round", "Word file for my editor", "editor review copy", "co-author", "collaborate on a book", "shared world", "back up my book", "does this echo my source", "similarity check", "check overlap with my earlier books", or wants to run human editorial, ethics, permissions, or collaboration workflows around a story project. NOT for contracts or selling rights (use publishing), or reader feedback rounds and review copies for readers, including the GitHub review-copy setup (use feedback-triage).

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。

This skill should be used when the user asks for a "simulated beta read", "reader panel", "persona read", "first read before my beta readers", "how would a genre reader react", "would a reader keep going", "pre-beta read", "mock beta readers", or wants structured persona reads of a chapter range before human readers see it. NOT for real reader feedback (use feedback-triage), a paid sensitivity or authenticity read (use editorial-review), or the agent's own line edit (use line-editing).

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。

Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.

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。