「analytics」の検索結果

Central router for the marketing skill ecosystem. Use when unsure which marketing skill to use, when orchestrating a multi-skill campaign, or when coordinating across content, SEO, CRO, channels, and analytics. Also use when the user mentions 'marketing help,' 'campaign plan,' 'what should I do next,' 'marketing priorities,' or 'coordinate marketing.'

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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-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。

Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.

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 complete transactional email systems: React Email templates, provider integration (Resend, Postmark, SendGrid, AWS SES), preview server, i18n support, dark mode, spam optimization, analytics tracking. Use when adding transactional email to a new product, migrating between email providers, refactoring legacy email templates for accessibility, or adding internationalization to existing templates.

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。

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

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。

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.

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。

Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring 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。

Orchestrate the live-ops team — live-ops-designer, economy-designer, analytics-engineer, community-manager, writer — for a season or live event.

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。

Creates Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

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。

Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

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。

Google platform decision and setup guidance, loaded on demand from Google's skill catalog. Use when a developer is choosing or setting up part of their stack, such as where to run a service, a database, storage, messaging, authentication, analytics, ads, or AI model serving, and a Google product is a reasonable candidate - whether or not a vendor is named - or when a request names a Google product or API. Brings in the matching Google skill so the answer can weigh Google options, their trade-offs, and when they are not the right fit. Skip when the stack is already settled on another provider and no Google product is named, or the task involves no platform choice.

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Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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。

Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", or "real CWV 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。

Live SEO data via DataForSEO MCP server: SERP analysis, keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis, competitor and content analysis, business listings, AI visibility (LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "AI visibility check", or "real search 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。

Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

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。

Turn a Wren project's context layer into a shareable, browser-side GenBI web app and deploy it to the user's Vercel or Cloudflare account. Orchestrates the full flow: `wren genbi build` returns a project-hydrated build instruction, the agent authors the app from scratch into apps/<name>/, then register → verify → deploy produce a shareable URL. Use this skill whenever the user wants to: build a dashboard from their Wren project, make a shareable analytics app, deploy their context layer as a web app, host a GenBI app on Vercel or Cloudflare Pages, or asks for a 'genbi app'.

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 installing or configuring the OpenSearch extension for KubeSphere, which provides distributed search and analytics engine for storing logs, events, auditing, and notification history

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。

Automate customer engagement workflows including broadcast triggers, message analytics, segment management, and newsletter tracking through Customer.io via Composio

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 writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming 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。

Database schema and business logic for sales data analysis including customers, orders, and revenue.

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。

平台切换 + 粉丝/互动 KPI + 增长曲线 + Top post + 热门话题

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。

固定侧栏 + 顶栏 + KPI 网格 + 1-2 张图

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 Mixpanel product analytics, event data, funnel analysis, retention curves, cohort analysis, segmentation queries, user behavior, conversion rates, churn, DAU/MAU, ARPU, revenue metrics, feature adoption, A/B test results, user paths, flow analysis, or any request to query, explore, visualize, or analyze Mixpanel data using Python. Also use when the user asks to read, write, or manage Mixpanel "business context" — the markdown documentation that grounds AI assistants in an organization's structure and goals.

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。