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Clarify unresolved product decisions before implementation, such as scope, supported platforms, or API agreements. Use a small set of focused questions to establish the intended outcome, while leaving routine technical choices to the agent.
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Work through design or implementation choices one at a time, with concise context and clear alternatives. You can also review completed code change by change, recording decisions, approvals, and requested revisions in a final table.
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Work through major choices such as changing jobs, moving, or choosing a partner using simple decision rules. Examine whether to decline an uncertain opportunity, compare equally viable options, and reserve time for decisions with lasting consequences.
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[omh] Uncertain technical choice for a spike: bounded decision prototype workflow: resolve one uncertain interaction, API, performance, or integration choice with a disposable, isolated experiment whose observed result feeds planning. Use when the user says: decision-prototype, decision prototype, prototype this uncertain choice before planning, prototype before planning, prototype the uncertain choice, run a small spike, small spike, spike solution.
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Review visual design choices before creating or styling web pages, components, apps, dashboards, or interactive reports. It flags repetitive colors, typography, layouts, and motion patterns to avoid, while keeping explicit user requests and other specified styles in control.
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Explore difficult decisions, research trade-offs, or creative challenges through several simulated perspectives. Separate facts, assumptions, and values, compare competing priorities, and produce a conditional recommendation with unresolved questions, a next step, and conditions that would change the choice.
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Create ADRs, short records of architectural decisions, that explain what was chosen, why, and with what trade-offs. Choose among three formats and organize dates, status, numbering, and related links for future team members.
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Amazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg. Use when user mentions Amazon product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.
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Audit the choices an implementing agent made, not its diff — a pure decision audit that traces the session's history into a choices ledger, changes no code, and never blocks an unsupervised run. Working code still embeds architecture the user never chose; surface it because future work inherits it. Use when the user wants to review the decisions the AI made on their behalf, before merging or committing AI-implemented work, when integrating a delegated subagent's pass, or when a fix "works" but might be a point fix.
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Design an uninterrupted work cycle with choice-based activities structured within a realistic time block. Use when planning Montessori-style independent work periods or extended choice time.
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Bevy 0.20 dialogue data: line tables in a resource, the current line index on a component, and choice flags filtered before the write. Use when lines or choices advance. Keywords: Bevy, dialogue, line index, choices, ECS.
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This skill should be used when the user asks to "write a branching story", "interactive fiction", "choose your own adventure", "plan the branches", "choice graph", "add a choice", "gamebook", "Twine", "Ink", "draft a branch", "where the branches rejoin", "add an ending", "unreachable chapter", "path continuity", "state-differs-by-path", or wants to plan, draft, or revise a story project whose chapters carry `choices`. NOT for turning a finished linear book into an interactive edition in Ink or Twine (use adaptation).
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Use when writing or editing Pixi'VN story content — defining labels (scenes) with newLabel, writing dialogue steps, adding player choices with newChoiceOption/newCloseChoiceOption, conditional/branching labels, or wiring up narration.call/jump/continue and Game.start to progress the story.
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Design an uninterrupted work cycle with choice-based activities structured within a realistic time block. Use when planning Montessori-style independent work periods or extended choice time.
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Maintain clear business definitions and relationships in an existing glossary or CONTEXT.md. Flag unresolved meanings and refer lasting design choices to the existing architecture decision record workflow, keeping terminology separate from decision rationale and task status.
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帮助构建和维护MCP服务器，让AI助手调用外部操作、读取数据并使用指令模板，涵盖Node/TypeScript功能注册、输入验证、本地与远程通信选择及故障排查。
为主要由AI智能体实施的开发工作制定完成条件，拆分可独立验证的任务，并比较实施前后的评估结果；同时按难度选择模型，记录费用、耗时与重试情况。
Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.
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Run sequential reviews of a plan from business, design, engineering, and developer experience perspectives. Apply six principles to intermediate decisions, then bring close choices, scope questions, and reviewer disagreements to a final approval step.
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Review accumulated code edits through a patch and a readable file-by-file summary. An interactive choice screen captures acceptance, rejection, or partial approval, records the decision, and rolls back rejected changes within the planned scope.
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Turn a design request into a concrete specification covering colors, fonts, layout, mood, spacing, and constraints. Generate DESIGN.md and an HTML preview, with a clear report of defaults applied to unspecified choices.
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Review form validation so users, including people using screen readers, can understand and correct errors. Check field descriptions, required states, and grouped choices in rendered HTML, while accounting for React forms that handle submission in the browser.
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Review and implement dark mode using CSS variables and the user's operating system preference. Check fixed colors, contrast, responsive layouts, and interaction states, with guidance for adding a manual theme switch that remembers the user's choice.
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Prepare a local browser app or standalone browser file for handoff. Verify its start behavior where supported, then provide exact commands and a reachable URL, or the artifact path, with an honest account of what was checked.
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