「analytics」の検索結果

Shop analytics — traffic sources, conversion rates, search terms, top listings performance

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。

Export Claude Code session and analytics data in JSON, CSV, or Markdown formats. Supports exporting sessions, events, costs, and analytics for external analysis or reporting. Use for data backup or integration.

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 or review source-backed descriptive, diagnostic, predictive, and prescriptive analysis for consequential decisions. Use when an agent must profile and safely prepare uploaded data, turn a real dataset or research question into a reproducible study, investigate drivers without overstating causality, validate a model, compare feasible actions under dependent uncertainty and tail risk, trace every parameter to evidence and approval, or produce an answer-first analytical report across health, business, finance, policy, engineering, operations, behavioral science, AI, or planning.

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 covering campaign analysis, attribution and marketing mix modeling, ROI measurement, and performance reporting. Use when analyzing campaign ROI, comparing attribution models, or optimizing budget allocation.

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。

Design the analytics layer with script-backed benchmarks: KPI trees, report templates, MMM, cookieless. "define our KPIs"

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 blueprint for player mortality loops: progress-index checkpoints, soul graves, ghost/spirit layers, I-frame restitution, world-progress persistence, and death analytics. Use when implementing respawn, shrine checkpoints, corpse-run retrieval, or second-chance mechanics. Keywords revival, respawn, checkpoint, soul grave, ghost mode, I-frames, death analytics, progress index.

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。

Best-practices reference for preparing and instrumenting applications on Temps. Covers the app runtime contract (`.temps.yaml` health, HOST/PORT, readiness, SIGTERM, replicas, migrations) and production observability (errors, traces, metrics, logs, analytics, privacy, sampling, cardinality, credential boundaries). Use whenever building or reviewing an app for Temps, configuring health checks, adding telemetry, diagnosing missing/noisy signals, or checking OTLP/Sentry/analytics ingestion. Triggers include "temps best practices", "prepare this app for Temps", ".temps.yaml health", "temps health check", "ignore health checks in otel", "temps observability", "wire up telemetry", and "instrument this app for temps". Prefer focused setup skills for a single SDK. Use temps-cli for executing deploy and resource-management commands.

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。

Interpret app metrics and make data-driven decisions. Covers DAU/MAU, retention, LTV, ARPU, App Store Connect analytics, AARRR funnel analysis, cohort analysis, and diagnostic decision trees. Use when user wants to understand their metrics, diagnose problems, or build a data-driven growth plan.

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 knowledge for Azure SQL Database development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Hyperscale, serverless tiers, Always Encrypted, geo-replication, or elastic pools, and other Azure SQL Database related development tasks. Not for Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Cosmos DB (use azure-cosmos-db), Azure Synapse Analytics (use azure-synapse-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。

Expert knowledge for Azure Open Datasets development including limits & quotas. Use when handling non-Spark dataset downloads, throttling behavior, quota limits, retry logic, or rate-limit workarounds, and other Azure Open Datasets related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).

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 knowledge for Azure Quantum development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using QDK with Python/Q#, OpenQASM or hybrid jobs, Rigetti/IonQ targets, QIR jobs, or Bicep/CLI workspace deploys, and other Azure Quantum related development tasks. Not for Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning), Azure Synapse Analytics (use azure-synapse-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。

Expert knowledge for Azure AI Metrics Advisor development including decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alerts/hooks, or calling Metrics Advisor REST/SDKs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Stream Analytics (use azure-stream-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。

Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AutoML, Prompt Flow, online/batch endpoints, vector stores/RAG, or MLflow/ONNX deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Science Virtual Machines (use azure-data-science-vm), Azure HDInsight (use azure-hdinsight).

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 knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with Unity Catalog, Lakehouse/Lakeflow, Lakebase, AI Runtime/model serving, or external connectors, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

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 knowledge for Azure Energy Data Services development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring ADME metrics/partitions, choosing tiers, securing auth/ACLs/legal tags, or integrating ACZ/DDMS with Databricks/Fabric, and other Azure Energy Data Services related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Data Manager for Agriculture (use azure-data-manager-for-agri).

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 knowledge for Azure Data Manager for Agriculture development including limits & quotas, security, configuration, and integrations & coding patterns. Use when setting up BYOL creds/Private Link, ag data ingestion/IoT, AI/nutrient APIs, throttling, or Event Grid logs, and other Azure Data Manager for Agriculture related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks).

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 knowledge for Azure Analysis Services development including troubleshooting. Use when resolving server connectivity, firewall/VNet, DNS, client connection, or network error issues, and other Azure Analysis Services related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

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 knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building indexes, skillsets, indexers, vector/semantic search, or RAG knowledge bases with Azure AI Search, and other Azure AI Search related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Cosmos DB (use azure-cosmos-db).

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 YouTube channel and video performance using the YouTube Data API. Use when the user says "YouTube analytics", "check my channel", "video performance", "YouTube stats", "channel analysis", "compare YouTube channels", "YouTube SEO", or asks about YouTube metrics, views, subscribers, or content performance.

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 website traffic, global rank, engagement metrics, and traffic sources for any domain. Use this skill to evaluate affiliate program websites, compare competitor traffic, assess advertiser strength, or understand where an audience comes from. Triggers on: "analyze traffic for [domain]", "how much traffic does [site] get", "compare traffic between [site A] and [site B]", "is [program] worth promoting based on traffic", "traffic analysis", "website analytics for [domain]", "where does [site] get traffic", "check if [advertiser] is legit", "evaluate [program] website health", "SimilarWeb analysis", "traffic sources for [domain]", "how popular is [site]", "website rank", "domain authority check", "compare affiliate program websites".

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 logging analytics events, setting user properties, configuring default event parameters, building funnels, or adding screen-view tracking.

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。

Data & Analytics Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

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。

SaaS analytics event taxonomy, metric formulas (MRR, churn, LTV), provider-agnostic tracking, funnel analysis, cohort setup, and privacy-respecting 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。

Product analytics - event taxonomy, funnel analysis, A/B testing, retention metrikleri.

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。