「feedback」の検索結果

Structural analysis of complex systems — Iceberg model, Causal Loop feedback diagrams, archetype matching, Meadows leverage points, and concept maps — grounded in the premise that behavior is generated by structure. USE WHEN systems thinking, causal loop, feedback loops, archetypes, leverage points, iceberg model, fix the system, why does this keep happening, recurring problem, second-order effects. NOT FOR incident causal chains (use RootCauseAnalysis).

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Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

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当用户问怎么真诚沟通/拒绝别人/给反馈/不想说场面话、或发现自己在隐瞒时调用。 核心理念: 撒谎会先骗到自己(费曼: 你最容易愚弄的人是你); 彻底诚实=所想与所说一致; 具体表扬、一般批评; 秘密越少快乐越多。 不适用于: 需要外交辞令的谈判策略(可以诚实但不必全说)。 Triggers: 诚实/沟通/拒绝/批评/表扬/秘密/场面话/honesty/communication/feedback

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Structural feedback on a legal writing draft (memo, brief, paper, exam essay) — organization, analysis depth, clarity, citation form. NEVER rewrites the draft. Use when the user says "feedback on my memo", "read my draft", or "critique my brief".

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Design or implement responsive audio feedback for a Three.js or web game. Use for action sounds, combat layers, music states, spatial audio, mix priorities, mute controls, accessibility, mobile audio unlock, and audio performance.

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Extract customer reviews from any e-commerce product page or reviews page. Returns reviewer name, star rating, date, review title, review body, verified purchase status, and helpful votes per review. Works on Amazon, WooCommerce, Shopify, and any site with standard review markup. Supports pagination for multi-page review sections. Use when: product reviews, customer feedback, review scraping, get reviews, sentiment analysis data, review extraction, customer ratings, extract customer opinions, product feedback, user reviews, review mining, bulk review collection, review analysis, scrape ratings and comments, ecommerce review data.

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Extracts full item detail from any open eBay item URL, returning JSON with url, itemNumber, title, subTitle, categories, price, priceWithCurrency, currency, wasPrice, available, availableText, sold, image, images, seller, sellerUrl, sellerFeedbackCount, sellerPositiveRating, itemLocation, brand, type, mpn, upc, ean, condition, shippingCost, shipping, whyToBuy, and itemSpecifics (full label/value map). Works across all eBay regional TLDs. Use when user mentions eBay item page, eBay listing detail, eBay product page, eBay itm, ebay.com/itm, eBay item scraper, extract eBay item details, eBay item specifics, scrape eBay item, eBay sold count, eBay availability, eBay brand MPN UPC EAN, eBay item location, eBay seller feedback, eBay wasPrice, eBay hi-res images, eBay itemNumber, eBay per-item enrichment. Also applies to enriching item URLs from a listing, monitoring price and stock, competitive product research, brand catalog audits, and pulling full field set from an individual eBay listing.

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Extract seller or merchant profile data from marketplace platform seller pages. Returns seller name, rating, review count, positive feedback percentage, joined date, and return policy. Works on Amazon seller pages, eBay seller pages, and any e-commerce site with seller profiles. Use when: seller information, merchant profile, seller rating, marketplace seller data, seller details, vendor profile, store information, seller feedback, merchant rating, seller review count, get seller info, ebay seller page, amazon seller profile, marketplace vendor analysis, seller research.

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This skill is designed to help users automatically extract reviews from Google Maps via the Google Maps Reviews API. Agent should proactively apply this skill when users request to find reviews for local businesses (e.g., coffee shops, clinics), monitor customer feedback for a specific brand or location, analyze sentiment of reviews for competitors, extract reviews for a chain of stores or services, track reputation of a local restaurant, gather user testimonials for a specific venue, conduct market research on service quality of local businesses, monitor reviews for a new retail location, collect feedback on public attractions or parks, identify common complaints for a specific service provider, research the best-rated places in a city, analyze recurring themes in reviews for a specific industry.

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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.

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Submit feedback about a Weave Router decision or model performance.

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Alias for router-feedback — submit feedback about a Weave Router decision or model performance.

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Comprehensive manuscript review covering argument structure, econometric specification, citation completeness, and potential referee objections. Make sure to use this skill whenever the user wants substantive academic feedback on a paper — not just surface edits. Triggers include: "review my paper", "give me feedback on this draft", "what would a referee say", "anticipate referee objections", "act as a referee", "check my identification strategy", "is my argument convincing", "review this manuscript", "critique my paper", "will this pass review", or any request for deep critique of academic writing beyond typos and grammar.

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Process screenshot-based UI/UX feedback to fix visual issues — use when users share screenshots of bugs

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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

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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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Use when the user asks to process, triage, fetch, view, count, list, or resolve review feedback in a GitHub PR. Supports both CodeRabbit and Codex review workflows. In this workflow, “real review feedback” is strictly defined as actionable inline comments; for CodeRabbit, exclude review summaries and nitpicks, and for Codex, exclude review summary cards and use PR main-thread reactions only as status signals.

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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

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Use when the user asks to "triage our comments, DMs, and mentions", "draft replies to this thread", "can we repost this fan post", or "set up inbox SLAs and an escalation path"; produces a ranked triage queue with register detection (sincere / ironic / performative / parasocial, sentiment-inversion table included — "this is so bad" under a comedy register is praise), a commenter taxonomy (troll monitor-only / rager / misguided / unhappy-customer / advocate) with a response-tier ladder and per-channel SLAs, an escalation matrix ending at the crisis path, a moderation ladder plus house rules for owned spaces, and a UGC curation-and-rights mode whose dated permission entries route to the channel registry — every reply is a ranked DRAFT a human posts; nothing is ever auto-sent. Not for launch-window feedback triage — use launch-feedback-synthesizer. 评论私信提及分诊/语域识别/回复草稿/UGC授权

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Structured approach to workplace conflicts, performance discussions, and challenging feedback using preparation-delivery-followup framework. Use when preparing for tough conversations, addressing conflicts, giving critical feedback, or navigating sensitive workplace discussions.

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Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user-research questions, interpreting ambiguous feedback, or designing customer-discovery that avoids false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

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Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

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Runs performance systems that change behavior — expectations, feedback, review cycles, calibration, and handling underperformance. Use this to design or fix a review process, run calibration, write or coach on feedback, address sustained underperformance, or work out why reviews consume weeks and change nothing.

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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。