「exit」の検索結果

AI citation readiness audit ONLY (does not touch Google rankings, use blog-rewrite for combined Google+AI work). Use whenever the user wants their content to rank in ChatGPT, Perplexity, Claude, Gemini, or Google AI Overviews. AI citation optimization audit scoring blog posts for ChatGPT, Perplexity, and Google AI Overview citability. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation".

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 about "code complexity", "cyclomatic complexity", "cognitive complexity", "code metrics", "maintainability index", "code coverage", or when measuring code quality quantitatively. Provides metrics thresholds and measurement techniques.

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。

Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

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 free built-in .NET maintainability analyzers and code metrics configuration to find overly complex methods and coupled code. Use when a repo needs cyclomatic complexity checks, maintainability thresholds, or complexity-driven refactoring gates.

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。

Eliciting and documenting a sales process before it is built in Salesforce: stage sequencing, entry/exit criteria per stage, win/loss analysis requirements, and stage transition rules. Trigger keywords: sales process design, stage discovery, entry criteria, exit criteria, stage gate, win/loss categorisation, sales methodology, pipeline stages, opportunity stage mapping, stage ladder, probability ladder, forecast category mapping, required fields per stage, stage swim lane, discovery workshop, handoff brief. NOT for configuring OpportunityStage, Sales Processes or Path in Setup — use admin/opportunity-management. NOT for cross-functional swim-lane maps — use admin/process-flow-as-is-to-be.

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 setting up, configuring, or troubleshooting Marketing Cloud Journey Builder — including entry sources, activities, decision splits, wait activities, goals, exit criteria, journey versions, test mode, or journey analytics. Triggers: 'journey builder setup', 'entry source configuration', 'decision split not routing', 'exit criteria not removing contacts', 'goal not tracking conversion', 'journey version publishing', 'journey re-entry settings', 'wait activity date field'. NOT for coding a custom activity or entry source — use apex/marketing-cloud-custom-activities. NOT for business units and sender profiles — use admin/marketing-cloud-engagement-setup.

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。

Domain-aware complexity routing: scans the task for domain keywords, matches domain-complexity.csv, and returns complexity level, workflow intensity, and execution tier. Extends Phase 0 orchestration.

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。

Post-implementation simplification pass. Reduces complexity without changing behavior. Use after Phase 3 (Build GREEN) and before Phase 4 (Review). Catches dead code, unnecessary abstractions, over-engineering, and redundant patterns. Triggers on "simplify", "clean up", "reduce complexity", "too complex". NOT for style/convention enforcement (see mk:clean-code); NOT for reviewing diffs or PRs (see mk: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。

Evaluate Jira ticket complexity + inconsistencies via the jira-evaluator agent. Read-only. Triggers: 'evaluate KEY', 'analyze ticket complexity for KEY', 'check ticket quality for KEY'. NOT for estimation (mk:jira-estimator); NOT for full RCA (mk:jira-analyst).

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。

Heart rate variability biometrics and emotional awareness training. Expert in HRV analysis, interoception training, biofeedback, and emotional intelligence. Activate on 'HRV', 'heart rate variability', 'alexithymia', 'biofeedback', 'vagal tone', 'interoception', 'RMSSD', 'autonomic nervous system'. NOT for general fitness tracking without HRV focus, simple heart rate monitoring, or diagnosing medical conditions (only licensed professionals diagnose).

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。

Two-phase code quality skill: (1) review codebase for structural smells — file LOC, method LOC, accidental complexity, test readability — producing a structured report, then (2) fix all smells via provable refactorings with ACN micro commits and green-bar-at-all-times discipline. Use this skill when the user says 'sweep', 'code review', 'find code smells', 'check method lengths', 'review for complexity', 'tidy up the codebase', 'refactor smells', or asks about long methods, test readability, or accidental complexity. Also trigger when users ask to 'clean up' or 'improve code quality' across multiple files.

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。

Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.

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。

呼び出し側が覚える使い方を小さく保ち、複雑な処理を内部にまとめる設計を支援します。共通の用語と判断基準で、モジュールの使いやすさやテストのしやすさを検討するスキルです。

• モジュールの使い方を簡単にしたいとき
• 散らばった複雑な処理をまとめたいとき
• 実装を差し替える場所の検討

React / Next.jsのボタン、モーダル、通知、ページ遷移に動きを加え、表示・消去やスクロールに連動するアニメーションを実装するスキル。

• ボタンの操作に動きを付けたいとき
• モーダルや通知の表示・消去
• Next.js App Routerのページ遷移

LLM APIの費用を抑えるため、作業の複雑さに応じたモデル選択、予算の記録、一時的なエラーの再試行、共通プロンプトのキャッシュを組み合わせる設計例を示します。

• LLMアプリのAPI費用を見直したいとき
• 一括処理の難易度でモデルを選びたいとき
• 予算に対する累積費用の確認

Burn-rate trend over time with optional drift-alert exit code. Bins session spend into buckets, surfaces window-over-window delta, and can exit 1 when latest bucket exceeds prior mean by a configurable %. Distinct from `cost-trend` (benchmark drift); this tracks PRODUCTION spend trajectory.

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 wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.

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。

Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT for library/framework docs (use Context7) or workspace questions.

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。

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

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。

Brain-augmented web research. Sends brain context about a topic to Perplexity, which searches the web with citations and returns what is NEW vs what the brain already knows. Use for entity enrichment, current-state checks, deal monitoring, and freshness deltas. NOT for simple URL fetches (use web_fetch) or brain-only queries (use gbrain query).

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。

Reduce voluntary and involuntary churn through cancel flow design, save offers, exit surveys, and dunning sequences. Use when designing or optimizing a cancel flow, building save offers, setting up dunning emails, or reducing failed-payment churn. Trigger keywords: cancel flow, churn reduction, save offers, dunning, exit survey, payment recovery, win-back, involuntary churn, failed payments, cancel page. NOT for customer health scoring or expansion revenue — use customer-success-manager for that.

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。

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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。

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

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。

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

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。