「analytics」の検索結果

Creates Cloud Storage (Google Cloud Storage, or 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.

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Stores, retrieves, and manages data as objects in Cloud Storage (Google Cloud Storage, or GCS) buckets. Use when you need to interact with Cloud Storage — create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes and tiering (Standard, Nearline, Coldline, Archive), manage cost and lifecycle, protect data (versioning, encryption/CMEK, retention and Bucket Lock, object holds, soft delete), host static websites, trigger Pub/Sub notifications on object changes, mount buckets as a file system (gcsfuse), or optimize storage performance at any scale. Covers the gcloud storage / gsutil CLI, JSON and XML APIs, client libraries, Terraform, and Cloud Storage MCP servers. Don't use for block storage (Persistent Disk), data warehousing/analytics (BigQuery), or databases (Cloud SQL, Spanner, Bigtable, Firestore).

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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.

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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).

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Locates and loads the right Google product skill on demand from a remote catalog index, instead of preloading every skill. Use at the START of any request touching a Google product, API, or developer platform - including Google Cloud (GKE, Cloud Run, IAM, BigQuery, Vertex AI, Spanner), Google Ads, Google Analytics, Google Workspace (Gmail, Drive, Admin SDK), Chrome and Chrome extensions, Android, Firebase, YouTube, Google Maps, Gemini and the Gemini API, Google Play, and Flutter. Consult the index before answering from memory or searching the web. Don't use for non-Google products.

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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.

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Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.

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Use when testing, evaluating, or building regression suites for Agentforce agents: conversation testing in Agent Builder, topic (now subagent) coverage and utterance testing, Testing API and AiEvaluationDefinition metadata, Agentforce DX CLI test runs (sf agent generate test-spec, sf agent test create/run/resume/results/list), evaluation metrics (containment rate, escalation rate, CSAT, topic activation accuracy), and post-deploy analytics via Enhanced Event Logs. Triggers: 'how do I test my Agentforce agent', 'agent routes to wrong subagent', 'write utterance tests', 'regression test after topic change', 'measure agent quality', 'agent containment rate', 'run agent tests from the CLI'. NOT for agent creation, topic design, or action contract design — use agentforce/agentforce-agent-creation, agentforce/agent-topic-design, or agentforce/agent-actions respectively.

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Configuring Salesforce In-App Guidance: floating, docked, and targeted prompts, multi-step walkthroughs, audience targeting, scheduling, and adoption analytics. Use when designing user onboarding or feature adoption programs in Lightning Experience. Covers the Prompt metadata type, promptVersions, displayType, uiFormulaRule, stepNumber, isPublished, PromptAction and PromptError. NOT for guidance on a record's Path — use admin/path-and-guidance. NOT for adoption and training — use admin/change-management-and-training.

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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.

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Design the engagement model for an Experience Cloud community: reputation levels, ideation, and content contribution strategy. Triggers: 'community engagement strategy', 'gamification Experience Cloud', 'reputation levels setup', 'ideation community portal', 'member recognition program'. NOT for blocking, flagging or approving what members post — use admin/experience-cloud-moderation. NOT for measuring logins, page views or engagement after launch — use data/community-analytics-data. Also covers: the Network reputation elements that carry the ladder (enableReputation, reputationLevels/level/label/lowerThreshold, reputationPointsRules/pointsRule/eventType/points), the 16 documented reputation eventType values and their default points, ManagedTopics / ManagedTopic (Navigational vs Featured, position 0-24), NetworkMember.ReputationPoints seeding, and why FeedItem cannot carry the engagement dashboard.

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Use this skill when planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use agentforce/agentforce-sales-ai-setup. NOT for rollout training with no AI in scope — use admin/change-management-and-training.

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Detects analytics or tracking SDKs present without the required App Tracking Transparency prompt, enforcing Guideline 5.1.2(i) which requires user consent before any cross-app or cross-site tracking.

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Content marketing and strategy expert for planning, creation, distribution, and analytics

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ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.

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Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks mention/citation rates over time, detects trends, and identifies opportunities. Uses Gemini API free tier (with grounding) as primary method, web search as fallback. Use when a user wants to: check if AI models mention their brand, track AI citation changes over time, measure AEO content effectiveness, monitor competitor AI visibility, or audit their brand's presence in AI-generated answers. Pairs with aeo-prompt-research-free (identifies prompts) and aeo-content-free (creates/refreshes content). This skill closes the loop by measuring results.

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Write and optimize Kusto Query Language (KQL) for Azure Data Explorer, Log Analytics, and monitoring workflows. Use this skill when users ask for KQL queries, telemetry analysis, incident investigation, or query optimization.

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dbt best practices for SQL modeling, testing, and analytics engineering workflows

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Analyze reviewed Grafana log evidence for event patterns, error-rate changes, and enrolled security markers. Use when a user needs bounded log analytics for an incident window, error-log comparison, anomaly triage, operational debugging, or a cautious check of approved security signals.

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Professional network graph analyst identifying Gladwellian superconnectors, mavens, and influence brokers using betweenness centrality, structural holes theory, and multi-source network reconstruction. Activate on 'superconnectors', 'network analysis', 'who knows who', 'professional network', 'influence mapping', 'betweenness centrality'. NOT for surveillance, discrimination, stalking, privacy violation, or speculation without data.

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Design dashboards for migrations and runtimes. Use for authority drift, verifier status, burn-down, runtime health, or pain panels. NOT for vanity analytics, ad hoc charts, or duplicate reporting.

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OpenLineage, DataHub, Marquez for data lineage tracking and impact analysis. Activate on: data lineage, OpenLineage, DataHub, Marquez, impact analysis, data catalog, column lineage, data discovery. NOT for: data quality validation (use data-quality-guardian), dbt documentation (use dbt-analytics-engineer).

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Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).

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。