「what」の検索結果

Create or refresh AEO-optimized content that gets cited by AI assistants (Gemini, ChatGPT, Perplexity) using only free tools. Two modes: CREATE new content targeting a specific prompt, or REFRESH existing content to improve AI citation-worthiness. Researches what AI models currently cite, builds a competitive brief, and produces citation-worthy content. Use when a user wants to: write content optimized for AI citations, create articles that show up in AI answers, refresh/update existing content for better AI visibility, build authority content for answer engines, or produce AEO content without paid tools. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only. Pairs with aeo-prompt-research-free (which identifies WHAT to write about; this skill handles HOW to write or refresh it).

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Find question-based Google Autocomplete suggestions for any topic. Prepends question modifiers (what, how, why) to a seed topic and returns real autocomplete suggestions — useful for AEO prompt research, content ideation, and understanding what people ask about a topic. Use when the user wants to discover questions people search for, find content angles, or do keyword/prompt research for a topic.

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Analyze ad creatives (images and videos) extracted from competitor research. Use when given a directory of ad images, video files, or transcripts to evaluate ad quality, score visual and messaging effectiveness, assign a scale score for viral/engagement potential, and generate a cross-creative pattern summary. Triggered by requests like "analyze these ads", "score these creatives", "what hooks are competitors using", "evaluate the ad library", "give me a scale score", "analyze the ad folder", or "what's working in these ads".

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Personal knowledge base powered by Ensue for capturing and retrieving understanding. Use when user wants to save knowledge, recall what they know, manage their toolbox, or build on past learnings. Triggers on "save this", "remember", "what do I know about", "add to toolbox", "my notes on", "store this concept".

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案件预算和持续的 WIP/偏差监控。在案件设立时构建分阶段费用估算，按法域或工作流运行自下而上预算，计算应急准备金，并构建 AFA 安排（固定费用、上限费用、分阶段固定费用）。持续监控：对照预算的 WIP 跟踪、比例性评估（支出与进度）、带根本原因分析的偏差评述、完成预测、变现率监控、核销分析。触发词：'build a budget'、'fee estimate'、'what will this cost'、'WIP review'、'budget vs actual'、'how are we tracking against budget'、'we're over budget'、'realisation is poor'、'what's our ETC'、'budget for the German workstream'、'model the financial impact of this scope change'、'draft a fee adjustment'、'write-off analysis'、'how much contingency'、'AFA structure'、'fixed fee estimate'、'budget update'、'forecast to complete'。

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Use for analytical thinking, or when a question needs breaking down and evidence: "why did metric X drop?", "what drives this?", "break this down", "what data do we need?". Builds a tree, hypotheses and evidence, ends with an answer and key uncertainty. Skip for quick verdicts.

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塔罗牌占星：把「抽三张塔罗牌」与「西方占星本命盘」合为一次解读。 用户带着一个具体问题来 → 抽三张牌（可复现的 seed）→ 若愿意提供生日，则用脚本 确定性地排出星座与星盘 → 用黄金黎明的塔罗↔占星对应把两者接起来（牌落在黄道哪一旬， 你的哪颗行星正站在那里）→ 给出一张交叉对照表、对所问之事的审慎取向与可执行建议。 塔罗层：牌阵位置语义、牌与牌的组合叙事、元素尊贵、正逆位、直觉对话、治疗性解读。 占星层：本命盘、相位与宫位、必然尊贵、行运盘推运、比较盘/组合盘（合盘）、太阳弧推运。 当用户说：塔罗、塔罗牌、抽牌、抽三张牌、占卜、算一卦（西洋）、牌阵、正逆位、 星盘、本命盘、星座、上升星座、月亮星座、占星、行运、流年、合盘、比较盘、 太阳弧、推运、我该不该……、这段关系会怎样、我适不适合换工作 时使用。 English triggers: "tarot", "tarot reading", "draw three cards", "what do the cards say", "spread", "reversed card", "natal chart", "birth chart", "astrology", "rising sign", "transits", "synastry", "composite chart", "solar arc", "progressions", "should I take this job", "what does my chart say". 内置离线脚本：抽牌洗牌（可复现 seed）、本命盘（行星/四轴/宫位/相位/元素统计）、 必然尊贵、牌↔盘共鸣、元素数字统计、行运盘、比较盘+组合盘、太阳弧+次限推运。 边界：要**周公解梦**请用 `zhougong-dream-interpretation`；要**四柱八字**请用 `bazi`； 要**中式黄历择日/测吉凶**请用 `zhanshi-jixiong`。本技能只做西洋塔罗与西洋占星。 即使用户只是随口说「帮我抽张牌看看」「我最近水逆吗」，也应主动使用本技能。

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Search and analyze X (Twitter) using xAI Grok 4 via OpenRouter with Live Search. Trigger on prompts that explicitly or implicitly ask to "search Twitter/X", "what's trending", "tweets from @handle", "hashtag #…", "what are people saying", or that require tweet-level activity/engagement from X.

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Find 10x product opportunities and high-leverage improvements. Use when user wants strategic product thinking, mentions '10x', wants to find high-impact features, or says 'what would make this 10x better', 'product strategy', or 'what should we build next'.

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Plan a content strategy, topic clusters, editorial roadmap, and content mix for traffic, authority, and lead generation. Use when deciding what to publish, what topics to prioritize, or how to structure a content program.

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Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

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Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".

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Prove — or honestly fail to prove — a specific technical claim: "the fix works", "it's deployed", "the service is running", "the site is done", "there are no vulnerabilities". Atomizes the claim, picks the shortest decisive signal on the evidence ladder, and returns a verdict from a fixed vocabulary (pass / partial / fail / skip) with an explicit coverage boundary. NOT code review, NOT a fix, NOT orchestration — only "what is proven, and what we merely hope". Triggers — "prove it works", "is it really done?", "are you sure?", "we deployed — is it live?", "verify that", "did we actually check?".

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SOLID design review rules (SRP, OCP, LSP, ISP, DIP) for code reviews: what counts as a real design finding, what is only taste, how to cite it and how severe it is. Use when reviewing a diff, a pull request or recent changes for design problems, when asked for a "SOLID review", "design review", "is this class doing too much", or when a review checklist asks for SOLID. Pair it with the dry skill for duplication and with owasp-asvs for security.

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Turn a raw idea into ONE honest recommendation: interview one question at a time until the problem, requirements, goals, non-goals, and constraints are CONFIRMED, then deliver a single verdict — do, don't, cheaper alternatives, trade-offs, ordered checklist, success metrics. Required: the user wants exactly ONE recommendation — not options, not a debate — and their framing is open to challenge (advise may answer a different question than the one asked). Triggers on "what should I do about X", "advise me on X", "am I approaching this right", "is this the right move". Do NOT use to be interrogated with no recommendation (→ mk:grill) — advise also interviews one question at a time, but only to reach a verdict. Do NOT use when the framing is settled and only the options are open (→ mk:brainstorming). Do NOT use to decide whether something is worth building (→ mk:office-hours). Do NOT use when you want several perspectives to argue it out (→ mk:party).

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コード・テストコード・コミットログ・コードコメントのどこに何を書くかを決めるときに使う。コードには How、テストコードには What、コミットログには Why、コードコメントには Why not を書き分ける。実装時とコミット・PR 作成時に使う。

Write the spec before the code, then check the built thing against it — so agents build what was agreed, not what they invented.

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Close out a session — summarise what changed, capture what was learned, and prepare the commit.

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Guide pump setting adjustments for the MiniMed 780G system based on CareLink data, using Medtronic's 3-step follow-up workflow. Use when a clinician has CareLink data for a 780G patient and wants to know what to change — which setting, by how much, and how to program it. Triggers include: "what do I adjust", "how to fix high TIR", "how to reduce hypos", "adjust ICR/ISF/basal", "SmartGuard settings", "780G pump settings review", or any question about optimising 780G therapy after reviewing a download.

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Set absolute LDL-C, non-HDL-C, and ApoB treatment goals, PLUS percent LDL-C reduction, for a primary-prevention patient on lipid-lowering therapy per the re-introduced treatment goals in the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers goals by PREVENT-ASCVD category (low / borderline / intermediate / high), by CAC score, in severe hypercholesterolaemia / FH, in diabetes with multiple ASCVD risk factors, and the ApoB targets once LDL-C and non-HDL-C goals are met. Use when a clinician asks what LDL goal to aim for, LDL target for intermediate risk, non-HDL goal, ApoB target, dual goals on statin, patient at goal or not, how low should LDL go, statin not enough what next. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.

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Synthesise a status, standup, or situation-report from current work state. Trigger when a team member needs a concise summary of where things stand, what is blocked, and what is next.

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List the local NeatContext Contexts available to Codex. Use when the user asks what contexts exist, what can be connected, or explicitly invokes this skill.

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List the local NeatContext Contexts available to the current Kimi Code session. Use when the user asks what contexts exist, what can be connected, or explicitly invokes this skill.

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。