/deepline-analytics

name: deepline-analytics
description: "Use this skill when answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those."
disable-model-invocation: false

Deepline Analytics

Quick Start

npm install -g deepline
# Fallback for secure sandboxes: mkdir -p "$HOME/.local" && npm config set prefix "$HOME/.local" && export PATH="$HOME/.local/bin:$PATH" && npm install -g deepline --registry https://code.deepline.com/api/v2/npm/
deepline auth register --wait auto
deepline auth wait --timeout 120 # completes Cowork/browser approval; no-op if already connected
deepline auth status
deepline -h

CLI resolution

Run `deepline` when it is available. If the shell reports that command is missing, use `<workspace-root>/.deepline/runtime/bin/deepline` (or the npm-created `.cmd` shim on Windows). If neither exists, follow `https://code.deepline.com/INSTALL.md` to set up Deepline.

Use this skill to answer customer analytics questions through Deepline's warehouse and semantic-layer tools. The goal is not just to run SQL; it is to preserve the customer's business definitions by starting from the semantic layer, validating the query path, and reporting exactly what metric definitions and filters were used.

Before You Start

Use `deepline-gtm` instead when the task is prospecting, enrichment, contact finding, outbound sequencing, personalization, or row-by-row lead/account research. Analytics questions ask about existing customer data: revenue, pipeline, funnel, conversion, retention, usage, calls, accounts, opportunities, or warehouse tables.

If Snowflake credentials or a semantic layer are missing, stop and report the setup blocker. Guessing table names or falling straight to raw SQL hides the actual problem and usually produces incorrect business definitions.

Deepline Internal Analytics Sources

For Deepline's own product, usage, and operational analytics, choose the source by question. Do not assume every Snowflake metric is dbt-modeled.

| Source | Tables / dataset | Use for | Caveats | | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Convex replica raw ledger | `AERO_DB.CONVEX_RAW.*`, especially `PLAY_RUNS` and `INGESTION_PLANES` | Persisted app state, play-run status, prebuilt/custom play identity, customer database inventory/state | Loaded by the Convex-to-Snowflake replica. Authoritative historically, but can lag same-day activity. Raw tables may include sensitive app/customer fields. | | Convex replica reporting views | `AERO_DB.CONVEX.USAGE_EVENTS`, `BA_USER`, `BA_MEMBER` | Tool/enrich calls, Deepline credits, user/org identity | `USAGE_EVENTS` is the primitive billing/usage ledger. Provider spend must not be exposed. Some compatibility views are published selectively; verify freshness before relying on them. | | Axiom production logs | Dataset `vercel` | Real-time play/run failures, CLI outcomes, provider/tool errors, log-message failure classes | Real-time but log-shaped. Deduplicate run/request ids and compare against Snowflake when accuracy matters. | | RudderStack product events | `RUDDERSTACK.EVENTS.*` (`TRACKS`, `PAGES`, `IDENTIFIES`, event tables) | Product journey, page views, signup/auth lifecycle, attribution | Product analytics, not the operational ledger. Some browser events may be missing from Snowflake. | | dbt / transformed analytics marts | `AERO_DB.ANALYTICS*` schemas, for example `ANALYTICS.FCT_CUSTOMER_JOURNEY`, `DIM_ACCOUNT`, `CUSTOMER_STREAM`, and dbt views such as `STG_*` | Business-facing GTM, customer journey, CRM, TAM, and modeled analytics definitions | Use when the question asks for modeled business metrics. Do not use these as the default source for play-runtime health unless a modeled table is explicitly known to cover it. |

Current product usage dashboard source of truth:

• Play runs: Snowflake `AERO_DB.CONVEX_RAW.PLAY_RUNS` plus Axiom `vercel` logs with `[sdk-cli-observability] kind: 'play_run_completed'` and `[cli-failure-report] failure_kind: 'play_run_failed'`.
• Legacy workflow runs: Snowflake `AERO_DB.CONVEX_RAW.WORKFLOW_RUN