「ai」の検索結果

Expert ISO 42001 AI Management System (AIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 42001:2023, AI governance, AI management systems, AI risk assessment, AI system impact assessment, Annex A controls for AI, Statement of Applicability for AI systems, AI policy, responsible AI, AI lifecycle management, AI incident management, AI transparency, AI bias, AI certification readiness, or any topic related to implementing or auditing an AI Management System. Also trigger for questions like "how do I become ISO 42001 certified?", "what controls does ISO 42001 require?", "how do I assess AI risk under 42001?", "what is an AIMS?", or any request involving organisational governance of AI systems, responsible AI frameworks, or AI regulatory compliance aligned to an ISO standard.

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。

Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.

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。

Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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。

Email marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup — plus the contact budget: a cross-channel frequency cap across every sender (marketing, sales sequences, customer success, in-app). Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate, BIMI, brand logo in inbox, VMC, Verified Mark Certificate, Common Mark Certificate, transactional email, password reset emails going to spam, DMARCbis, RFC 9989, DMARC pct tag, DMARC t=y test mode, DMARC rollout, move DMARC to reject, frequency cap, email fatigue, customers get too many emails, too many messages, sales and marketing both emailing the same person, communication limits, send frequency safeguard, global frequency capping, quiet period, message precedence.

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。

Designing Campaign Hierarchy for program-level ROI tracking, configuring Customizable Campaign Influence (CCI) attribution models, and interpreting multi-touch attribution from MCAE B2B Marketing Analytics. Trigger keywords: campaign ROI, attribution model, campaign hierarchy, first-touch, last-touch, multi-touch, CCI, campaign influence, revenue attribution, campaign member status. NOT for deciding which attribution model and KPIs to adopt before any config - use admin/marketing-reporting-requirements. NOT for MCAE connector setup or the campaign sync that writes Campaign Member records - use admin/mcae-pardot-setup. Also covers: CampaignSettings, enableCampaignInfluence2, enableAutoCampInfluenceDisabled, enableB2bmaCampaignInfluence2, enableAccountsAsCM, CampaignInfluenceModel metadata, isDefaultModel, isModelLocked, recordPreference, AllRecords, RecordsWithAttribution, CampaignInfluence.RevenueShare, ModelType, CampaignMemberStatus IsDefault HasResponded SortOrder, HierarchyActualCost, HierarchyAmountWonOpportunities, TotalNumberofResponses, Opportunity.CampaignId primary campaign source.

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。

Interactive Domain-Driven Design modeling sessions based on DDD Distilled (Vaughn Vernon) and Domain Modeling Made Functional (Scott Wlaschin). Guides users through strategic and tactical design via 13 phases: discover, storming, contexts, mapping, aggregates, events, validate, glossary, workflows, types, simulate, publish, sync. Phase 13 (sync) reconciles divergences found by --analyze between the domain model and the implementation — judging whether the model or the code is authoritative, then planning and implementing the fix and re-aligning the model. Phases 9-11 take the conceptual model from phases 1-8 and produce workflow pipeline designs, compilable type definitions (TypeScript, Kotlin, Scala, Rust, C#, F#), workflow verification via type-level tests, and automatic UI field enumeration from the domain model. Phase 12 (publish) renders all docs/domain artifacts into one self-contained, cross-linked HTML site (separate files joined by a shared nav). Each phase is an interactive dialogue where AI acts as facilitator and domain expert challenger. Use when: "DDD", "ドメイン設計", "ドメインモデリング", "Event Storming", "Bounded Context", "集約設計", "ユビキタス言語", "コンテキストマップ", "ワークフロー設計", "パイプライン設計", "ステップ分割", "中間型", "型駆動フロー", "型駆動設計", "Domain Modeling Made Functional", "Railway Oriented Programming", "入力画面項目", "フォーム項目洗い出し", "UI 項目抽出", "HTMLにまとめる", "ドキュメントサイト化", "成果物を1つのHTMLに", "publish", "モデルと実装の差異", "差異解消", "実装との乖離", "モデルとコードを一致", "差異の実装計画", "sync", "複数フェーズを逐次実行", "フェーズをまとめて実行", "/ddd run", "サブコマンド一覧", "/ddd list", "/ddd", "ドメイン分析したい", "モデリングしたい", or any domain design / type modeling activity.

Chatfaiの操作をAIエージェントに任せるため、Rube MCP経由で利用可能なツールと最新の入力形式を調べ、接続確認から実行まで進めるスキル。

• Chatfaiの対応操作を調べたいとき
• 作業前に接続状態を確認したいとき
• Chatfaiの複数操作をまとめたいとき

When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. Emails avoid AI tells like 'it's not X, it's Y' reveals, 'no X, no Y, no Z' lists, and fake 'Re:' subject lines. For cold outreach and outbound sequences, see cold-email. For in-app onboarding, see onboarding.

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。

A.I.G Scanner — AI security scanning for infrastructure, AI tools / skills, AI Agents, and LLM jailbreak evaluation via Tencent Zhuque Lab AI-Infra-Guard. Uses built-in exec + Python script, no plugin required. Requires AIG_BASE_URL to be configured. Triggers on: scan AI service, AI vulnerability scan, scan AI infra, check CVE, audit AI service, scan MCP, scan skills, audit AI tools, scan agent, red-team LLM, jailbreak test, 扫描AI服务, 检查AI漏洞, 扫描AI工具, 检查MCP安全, 审计Agent, 越狱测试.

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。

Make a custom AI agent — Python or TypeScript/JavaScript, on a framework or hand-built — report what it did to Failproof AI, and run your own evaluator worker (the "eval pod") that scores those runs. Reach for it on vague phrasing too: "add observability to my agent", "why isn't my agent showing up?", "run an LLM judge on our own infra". Trigger when the user wants to: • plan an integration — which points in the agent loop to record; • instrument — add `failproofai-sdk` (Python) or `@failproofai/sdk` (Node, Bun, Deno, Next.js): turn on an adapter (LangChain/LangGraph, CrewAI, LlamaIndex, Pydantic AI, Vercel AI SDK, Mastra) or wire a hand-built loop; • verify — confirm events are written, or debug an integration that produces nothing; • evaluate — write, deploy or debug an Evaluator worker in Python or TypeScript. NOT for reading telemetry or scores that already landed (that's `fp-cloud-cli`), or deciding what is worth evaluating (that's `failproofai-eval-brainstorm`).

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 ISO 42001 AI Management System (AIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 42001:2023, AI governance, AI management systems, AI risk assessment, AI system impact assessment, Annex A controls for AI, Statement of Applicability for AI systems, AI policy, responsible AI, AI lifecycle management, AI incident management, AI transparency, AI bias, AI certification readiness, or any topic related to implementing or auditing an AI Management System. Also trigger for questions like "how do I become ISO 42001 certified?", "what controls does ISO 42001 require?", "how do I assess AI risk under 42001?", "what is an AIMS?", or any request involving organisational governance of AI systems, responsible AI frameworks, or AI regulatory compliance aligned to an ISO standard.

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。

The orchestration layer for AI-native creative production. This skill coordinates multiple AI tools—video, image, audio, digital humans, effects—into cohesive campaigns, productions, and creative systems. As AI tools proliferate, the challenge shifts from "can we create this?" to "how do we orchestrate these capabilities into something coherent?" The AI Creative Director thinks in systems, not tools. In pipelines, not one-offs. In brand consistency across AI-generated assets. This is where creative vision meets technical orchestration. The AI Creative Director doesn't just use AI tools—they compose them into creative instruments that produce at scales and speeds previously impossible. Use when "AI creative director, orchestrate AI, AI campaign, multi-tool, AI workflow, AI pipeline, coordinate AI, AI production, AI creative system, full AI production, AI at scale, orchestration, creative-direction, ai-production, workflow, pipeline, multi-tool, scale, quality-control" mentioned.

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。

Configuring Salesforce Email-to-Case: Standard vs On-Demand mode selection, routing address setup, email threading via Lightning tokens, auto-response rules, attachment limits, and per-address case field defaults. Trigger keywords: email-to-case, routing address, on-demand email-to-case, email threading, case from email, email agent, routing address setup, Case.settings-meta.xml, EmailToCaseRoutingAddress, emailServicesAddress, isVerified, caseOrigin, enableThreadTokenInBody, useEmailHeadersForThreading, overEmailLimitAction, unauthorizedSenderAction, enableE2CAttachmentAsFile, saveEmailHeaders, newEntityRecordType, casePriority, EmailMessage.EmailRoutingAddressId, auto-response loop. NOT for the wider case layer (queues, escalation rules, case teams, entitlements) - use admin/case-management-setup. NOT for routing case work items to agents after creation - use admin/omni-channel-routing-setup. NOT for the email templates or letterheads themselves - use admin/email-templates-and-alerts.

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。

macOSのFindMy.appを操作し、登録済みのApple製品やAirTagの位置を画面から読み取ります。定期的な撮影で持ち物の位置の変化を記録する手順も扱います。

• iPhoneやAirPodsの位置を確認したいとき
• AirTag付きの鍵やバッグを探したいとき
• AirTagの位置の変化を記録したいとき

ブランドやキャラクター、画風に合わせた画像生成のために、fal.aiで独自のLoRAを学習します。目的に応じたカスタムAIモデルを用意するスキルです。

• ブランド向けの画像モデルを学習したいとき
• キャラクター用の独自モデルを用意したいとき
• 特定の画風に合わせたLoRAの学習

Agent Mailの操作をAIエージェントに任せるスキル。Rube MCP経由で利用可能なツールと入力条件を調べ、接続状態を確認して作業を実行します。

• Agent Mailの対応操作を調べたいとき
• 実行前に接続状態を確認したいとき
• 最新の入力条件で操作を実行したいとき

EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.

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 you want to brainstorm and check available .com domains for a new project — brand naming, aftermarket pricing (HugeDomains / Afternic / Sedo / Dan), USPTO trademark screening, and social handle availability. Built on Laura Roeder's "work backwards from availability" method. Combines Vercel CLI, whois, Domainr, Namecheap, and agent-browser, each for what it reliably does. Workflow: budget → brainstorm → availability check → whois cross-check → price → aftermarket sweep (plus liveness probe and drop-watch) → bucket → negotiate → trademark + socials → buy. Triggers on "/domain," "find a domain," "check domain availability," "brainstorm a domain," "what .com is available for X," "domain hunt," "name my project," "is X.com available," "aftermarket price on X.com," "trademark check for X."

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 run a full email marketing channel — list building, deliverability, segmentation, newsletter strategy, campaign types, A/B testing, and email design. Use when the user says "email marketing", "email strategy", "email list", "newsletter", "email campaigns", "email list building", "email deliverability", "email open rates", "email segmentation", "email design", "email calendar", "grow my email list", "email marketing plan", "broadcast emails", "email engagement", or wants to use email as a primary owned marketing channel beyond just automated flows.

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。

🇺🇸 SEO & GEO Dual-Engine Playbook 2026 — Rank on Google AND get cited by AI search (ChatGPT, Perplexity, Claude, Gemini). Battle-tested from AFFiNE (60k GitHub stars in 24 months) and 150+ AI startup consultations. E-E-A-T writing voice system, keyword funnel strategy, JSON-LD schema templates, comparison page SOP, IndexNow setup, Generative Engine Optimization content patterns. **Optimized for dev tools / OSS / B2B SaaS. 2C products (education, apps, games): see 2c-adaptation guide in references/.** 🇨🇳 SEO & GEO 双引擎增长手册 —— 同时拿下 Google 排名和 AI 搜索引用（ChatGPT、Perplexity、Claude、文心一言）的完整方法论。AFFiNE 60k stars 与 150+ AI 创业公司咨询实战验证。E-E-A-T 写作声音系统、关键词漏斗、JSON-LD 结构化数据、竞品对比页 SOP、IndexNow 推送、生成式引擎优化内容模式。**默认适用于开发者工具 / 开源项目 / B2B SaaS，2C 产品（教育/应用/游戏）请参考 references/2c-adaptation.md。** 🇯🇵 SEO & GEO デュアルエンジングロースプレイブック — Google検索とAI検索（ChatGPT、Perplexity、Claude）の両方で評価される手法。AFFiNE 60k starsと150社以上のAIスタートアップコンサルティングで検証。E-E-A-Tライティング、キーワードファネル、Schema.org構造化データ、比較ページSOP。**デフォルトは開発者ツール／OSS／B2B SaaS向け。2C製品（教育・アプリ・ゲーム）はreferences/2c-adaptation.mdを参照。** 🇰🇷 SEO & GEO 듀얼 엔진 성장 플레이북 — 구글 검색과 AI 검색(ChatGPT, Perplexity, Claude) 양쪽 모두에서 인용되는 방법론. AFFiNE 60k 스타와 150+ AI 스타트업 컨설팅으로 검증. E-E-A-T 글쓰기, 키워드 퍼널, Schema.org 구조화 데이터, 비교 페이지 SOP. **기본값은 개발자 도구/OSS/B2B SaaS용. 2C 제품(교육·앱·게임)은 references/2c-adaptation.md 참조.** Triggers: "SEO" | "GEO" | "Generative Engine Optimization" | "AI search optimization" | "ChatGPT SEO" | "Perplexity SEO" | "Claude SEO" | "search optimization" | "content SEO" | "technical SEO" | "schema markup" | "JSON-LD" | "structured data" | "IndexNow" | "E-E-A-T" | "keyword strategy" | "keyword funnel" | "comparison page" | "programmatic SEO" | "SaaS SEO" | "搜索优化" | "AI 搜索" | "内容 SEO" | "GEO 优化" | "生成式引擎优化"

アプリケーションに LLM API(OpenAI / Anthropic / Gemini 等)を組み込む際のコスト最適化・セキュリティ・運用の必須パターン集。 「AI機能を付けて」「AIで読み取り」「画像認識」「PDFから抽出」「OCR」「自動分類」「レコメンド」「AI連携」「OpenAI APIを使う」 「チャットボット」「LLMを組み込む」などの文脈で、新規アプリ・既存アプリを問わず必ず使用する。 ユーザーが「AI」と明示しなくても、非構造データ(画像/PDF/自由文)からの情報抽出・分類・判別の要件が含まれるなら必ずこのスキルを読むこと。 AI機能のコスト見積・APIキー管理・利用量ダッシュボードの設計・レビューでもトリガーする。 実測グラウンディング付きの費用試算が必要な場合は Skill llm-cost-simulator を併用。 既定スタック(社内業務アプリ、mvp-first)の必須スキルではない optional 群。上記の AI 要件が要件定義で明示された場合、 または app-orchestrator の要件フラグ「AI機能あり」で指示された場合のみロードする。

AI（LLM）が生成した日本語テキストの「AI臭」を検出・診断し、人間らしい文章にリライトするスキル。 Use when: 「AIっぽい文章を直して」「人間らしくリライトして」「AI臭を消して」「この文章をもっと自然にして」 「テキストのAI感を減らして」「機械っぽさを取りたい」 "make this sound more human", "remove AI tone", "humanize this text", "detect if this is AI-generated". 6つのAI特有パターン（視覚的マーカー、単調なリズム、マニュアル的構成、非コミット姿勢、抽象語の濫用、 定型メタファー）を正規表現ベースで検出し、0-100のAI臭スコアを算出。3つの人間化技法 （バランスを崩す・客観を崩す・論理を崩す）でリライトを実行する。 Note: 検出スクリプトは日本語テキスト専用。英語テキストの場合はClaude自身がreferences/を参照して分析・リライトする。

企業のAI導入を検討するためのHTMLスライドを、暗紫色のグラデーションを使って作ります。経営層やIT・セキュリティ担当者に向け、リスク管理や投資対効果、展開計画を整理します。

• 経営層へAI導入案を説明したいとき
• IT部門とリスク管理を検討
• 投資対効果と展開計画の整理