القائمة
Get a quick overview of the connected Fivetran account.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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Answer marketing automation questions using Fivetran-synced data via BigQuery, Snowflake, or Databricks. Today supports Marketo via the fivetran/marketo dbt package; designed to extend to HubSpot Marketing, Pardot, Iterable, Braze, etc. as additional connector options. Analyzes funnel velocity (MQL/SAL/SQL transitions), nurture stream performance, email engagement (sends/opens/clicks/unsubscribes), campaign and program performance, lead source attribution, and lead engagement scoring using marketo__leads, marketo__lead_history, marketo__email_sends, marketo__campaigns, marketo__programs, and marketo__email_templates. Use when someone asks about lead funnel, MQL/SAL/SQL transitions, nurture performance, email engagement rates, campaign performance, lead source quality, or any marketing- automation metric. Trigger on: "funnel velocity", "MQL", "SAL", "SQL", "lead funnel", "nurture", "email open rate", "email click rate", "unsubscribe rate", "bounce rate", "campaign performance", "lead source", "attribution", "l
Answer ad performance questions using data from Fivetran's ad connectors via BigQuery, Snowflake, or Databricks. Cross-channel analysis across Google Ads, Facebook Ads, Microsoft Ads, LinkedIn Ads, TikTok Ads, Pinterest Ads, and Snapchat Ads. Use when someone asks about ad spend, impressions, clicks, conversions, CPC, CPM, ROAS, CTR, or any advertising metrics. Supports cross-channel comparison, campaign drill-down, trend analysis, keyword performance, and anomaly detection. Trigger on: "how are our ads performing", "ad spend", "campaign performance", "cost per click", "ROAS", "impressions", "CTR", "ad performance", "marketing analytics", "compare channels", "cross-channel", "Facebook vs Google", "budget allocation", "LinkedIn ads", "TikTok ads", "Pinterest ads", "Snapchat ads", "LinkedIn performance", "TikTok performance", "Pinterest performance", "Snapchat performance".
Answer HubSpot sales pipeline and funnel questions using Fivetran-synced HubSpot data via BigQuery, Snowflake, or Databricks. Analyzes deal stages, pipeline conversion rates, win/loss outcomes, rep performance, deal velocity, and stage aging using the hubspot dbt package models. Use when someone asks about pipeline health, stage conversion, funnel drop-off, deal velocity, win rates, rep activity, stalled deals, or any HubSpot CRM metric. Trigger on: "pipeline", "stage conversion", "win rate", "funnel", "deal velocity", "how long to close", "stalled deals", "deals by stage", "pipeline by rep", "won deals", "lost deals", "deal aging", "stage drop-off", "pipeline health", "sales cycle", "closed this quarter", "which stage has lowest conversion", "rep performance", "who's closing the most", "where are we losing deals".
Answer e-commerce store performance questions using Fivetran-synced Shopify connector data. Computes GMV, net revenue, AOV, order count, repeat customer rate, refund rate, top products, BFCM and seasonality detection, new vs returning revenue, and customer cohorts — directly from raw Shopify tables (no dbt quickstart required). Use when someone asks about store performance, GMV, AOV, refunds, top sellers, repeat customers, cohorts, BFCM/Q4 performance, or any e-commerce metric. Trigger on: "how is the store doing", "store performance", "GMV", "revenue", "AOV", "average order value", "repeat rate", "refund rate", "top products", "best sellers", "customer cohorts", "new vs returning", "Black Friday", "BFCM", "Cyber Monday", "Q4 performance", "monthly orders", "seasonality".
Answer Zendesk customer support questions using Fivetran-synced data via BigQuery, Snowflake, or Databricks. Analyzes ticket volume and trends, first reply and resolution times (business + calendar hours), backlog aging, agent performance, SLA breach rates, CSAT, and channel/priority breakdowns using the fivetran/zendesk dbt package models (zendesk__ticket_enriched, zendesk__ticket_metrics, zendesk__ticket_field_history, zendesk__ticket_backlog, zendesk__ticket_summary, zendesk__sla_policies). Use when someone asks about support ticket volume, first reply / resolution time, backlog, agent performance, SLA breaches, CSAT, or any Zendesk metric. Trigger on: "first reply time", "first response", "resolution time", "ticket resolution", "ticket backlog", "open tickets", "SLA breach", "CSAT", "satisfaction score", "agent performance", "ticket volume", "support performance", "Zendesk", "customer support".
Explore and query your Fivetran Managed Data Lake Service (MDLS). As part of MDLS, Fivetran manages a Polaris catalog and the underlying Apache Iceberg tables where your connector data lands — your fully managed lakehouse. This skill gives you an agentic interface to explore that data (read-only in the current MDLS release). Use for ANY question about your MDLS tables: schemas, namespaces, row counts, sync status, data freshness, partitioning, snapshots, or running queries against your Iceberg data. Trigger on: "what tables do we have", "explore my lakehouse", "query the lakehouse", "check sync status", "how fresh is our data", "what's in MDLS", "describe this table", "how many rows", "show me our Iceberg tables".
| name | fivetran-account-info |
| description | Get a quick overview of the connected Fivetran account. |
| compatibility | {"tools":["fivetran:get_account_info"]} |
| metadata | {"short-description":"Get a quick overview of the connected Fivetran account"} |
Use the get_account_info tool from the fivetran MCP server to summarize the
connected Fivetran account.
Include:
