「insights」の検索結果

Instrument a webapp to send useful telemetry data to Azure App Insights

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。

Erstellt aus einem YouTube-Video- oder Kanal-Link eine faktenbelegte Insight-Card (Video- und Kanal-Stats) per Websuche. Nutzen, wenn jemand Stats/Analyse zu einem YouTube-Link will. Gibt nur belegte Zahlen aus, nie geratene.

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。

Windowsのデスクトップアプリを実際に操作する自動テストを作るスキルです。画面要素の識別、待機処理、失敗時の記録、CIへの組み込みを扱います。

• Windowsアプリの操作テスト作成
• 不安定なGUIテストを調べたいとき
• 画面部品に安定した識別子を付けたいとき

脳波などを測る機器とNeuroSkillをつなぎ、集中・気分・眠気の指標を確認するスキル。過去の記録との比較や睡眠の振り返り、休憩の提案にも使えます。

• 現在の集中・リラックス指標の確認
• 記録したセッション間の集中比較
• 記録した睡眠段階の振り返り

社媒矩阵数据追踪面板模板（Social Media Matrix Tracker）。 Use when users ask for a cinematic, data-dense social media analytics dashboard with multi-platform metrics, interactive charts, hover insights, range compare, and dark/light theme switching in a single HTML artifact.

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。

Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.

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。

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', 'adversarial plan', 'hostile planning', 'cross-critique plan', '하이퍼플랜', '적대적 계획', '교차 비평'.

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 and manage the user's Threads account: profile, posts, feed, saved posts, activity, insights, social graph, search, trends, and a specific post by URL or ID. Can tune feed ranking and publish posts on request.

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 the user provides a Facebook URL or asks to read personal posts, comments, reactions, friends, timelines, profiles, stories, feeds, groups, events, or saved items, or to discover public events happening near a place, nearby, or in a local area on a date, or to create, edit, publish, or delete their own Marketplace listings. To find, browse, or buy Marketplace listings, use shopping instead. Use pages commands for managed Facebook Page discovery, insights, native draft editing/deletion, same-draft publication, approved posts and native scheduling.

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 Instagram profiles, followers, posts, comments, likes, stories, feed, saved content, and account insights. Answer questions about posts, reels, and Instagram links. Manage interests and profile details, and publish stories, reels, posts, or carousels on request.

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。

Manage the personal knowledge wiki. Use when the user shares articles, documents, or asks to organize knowledge; when a conversation produces insights worth preserving as structured knowledge; or when the user asks about the knowledge base.

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 users ask for World Cup or 世界杯 AI match predictions, WC assistant probabilities, World Cup news insights, master analysis, recomputing football match win rates with custom correction signals, or trading a related prediction market after reviewing the AI analysis.

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。

Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage.

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。

Compile one or more OpenViking knowledge bases or document collections into topic-organized, evidence-grounded high-level knowledge, including cross-source findings, trends, changes, drivers, comparisons, implications, and uncertainties. Use with ov compile when the user asks to distill or synthesize a knowledge base, compare multiple collections, or derive higher-order insights such as changes across financial reports; do not use for document-by-document summaries.

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。

Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery.

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。

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

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。

Add persistent graph-based memory via mnemon. Agents recall past context before responding and remember insights after each turn.

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。

Transform raw article text dumps in the brain into structured pages with executive summary, verbatim quotes, key insights, why-it-matters, and cross-references. Replaces walls-of-text with quotable, actionable brain pages.

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。

Review and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.

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。

Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw feedback into roadmap recommendations.

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。

Synthesize user research into themes, insights, and recommendations. Use when you have interview transcripts, survey results, usability test notes, support tickets, or NPS responses that need to be distilled into patterns, user segments, and prioritized next steps.

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 planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).

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 planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.

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 cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.

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。