「insights」の検索結果

Extract AI customer insights. TRIGGERS - Use when user needs help with ai-customer-insights related tasks.

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。

Extract actionable insights from blog posts, web articles, and practitioner content - assess credibility, run security checks, and either improve existing skills or apply to the current project. Load when the user asks to learn from an article, extract insights from a blog post, apply a practitioner's findings, or process engineering blog content. Also triggers on "learn from this article", "learn from this blog post", "extract insights from this post", "what can we learn from this article", "apply this article", or when the user links to a blog, Medium, Substack, dev.to, or engineering blog post.

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 this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic 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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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。

Capture Unreal Engine traces, inspect Trace Store files, keep Unreal Insights GUI open, and export/summarize timing/counter data.

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。

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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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。

Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for 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。

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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。

Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".

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。

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. Triggers: "AzureMonitorExporter java", "opentelemetry azure java", "application insights java otel", "azure monitor tracing java". Note: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.

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。

Implement and validate Algolia click, conversion, and view events while separating them from source-to-index synchronization. Use when adding Insights, query attribution, or event-driven record updates. Trigger with "Algolia Insights", "track search conversion", or "sync database to Algolia".

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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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 PostHog analytics: your user, projects, saved insights. Trigger phrases: posthog, analytics, posthog 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。

Use when building or trimming the exhibits of an American Economic Review: Insights (AER: Insights) short-format manuscript to the hard five-exhibit budget, one page each, no significance asterisks. Designs the minimum exhibit set; it does not run the analysis behind them.

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 positioning the contribution of an American Economic Review: Insights (AER: Insights) short-format manuscript against the closest published work in one or two tight paragraphs. Stakes the gap concisely; it does not write the full literature review (there is no room for one).

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 running the final pre-submission preflight for American Economic Review: Insights (AER: Insights) via the AEA/ScholarOne submission system — the word-count and exhibit-count gates, abstract limit, fees, author metadata, declarations, and the AEA Data and Code Availability Policy. Final checks; it does not draft 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。

Use when the identification argument is the bottleneck for an American Economic Review: Insights (AER: Insights) short-format manuscript — causal identification in an empirical design, parameter identification in a structural model, or treatment-effect identification in an experiment. Stress-tests the strategy so it is clean enough to defend in a few pages, before exhibits are finalized.

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 calibrating expectations for the fast, decisive American Economic Review: Insights (AER: Insights) review process — conditional-accept-or-reject decisions, the single-round model, and pre-empting the objections a short paper cannot afford. Explains the process and stress-tests the paper; it does not write the response letter (see aeri-rebuttal).

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 revising an American Economic Review: Insights (AER: Insights) short-format manuscript to lead with the result, fit the hard word cap (7,000 words minus 200 per exhibit), and write the ≤100-word abstract. Enforces the length budget and the lead-with-the-result arc; it does not change the 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。

Use when assembling the data and code deposit for an American Economic Review: Insights (AER: Insights) short-format manuscript to pass the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check. Builds the package and README; it does not run the 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。

Use when deciding which aeri-* sub-skill to invoke next, or when sequencing work on a short-format manuscript from topic selection through the conditional-accept response for an American Economic Review: Insights (AER: Insights) submission. Routes — it does not replace — the specialized skills.

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。