Data (Query, Visualize, Interpret)
Write SQL, explore schemas, build visualizations and dashboards, and validate analyses across any data warehouse or CSV upload.
Before & after
Same prompt, same agent — the only difference is the skill.
That's a concerning trend. To diagnose it, I'd need to look at your data—specifically, churn rate, new MRR, and maybe cohort breakdown by month. Do you have access to a data warehouse like Snowflake or BigQuery? Or can you export CSV of subscription events (sign-ups, cancellations, downgrades)? If you share that, I can write queries and help you spot the pattern.
MRR DROP ANALYSIS FINDINGS MRR June: 480k | MRR July: 441k | Delta: -39k (-8.1%) ROOT CAUSE BREAKDOWN Churn: +2.3% (historical: 1.8%) → -34k MRR loss Downgrades: Pro→Basic tier: 47 accounts (up from 12) → -5.2k loss New MRR: -2.1% vs May (200 new signups vs 204) → minimal impact CHURN COHORT ANALYSIS Cohort analysis by signup month shows high churn concentrated in 2-month-old cohorts (July 2023): • Retention at 60-day mark: 67% (vs 81% historical) • Reason flagged in support tickets: "Scaling costs, not seeing ROI" DOWNGRADE TRIGGER Pricing change (June 15) moved Pro from 99/mo to 129/mo (30% increase) Downgrades spiked June 17-25 (8 days after change) • Users in tech/startup segment most sensitive • Enterprise segment: 0 downgrades TOP 3 ACTIONS (priority) 1. Win-back: Email to 47 downgraders with "Pro-lite" option at 99/mo (recover 60%) 2. Pause price increase for 30-day cohort (June signups < 60 days old) 3. Analyze support churn tickets → update onboarding PREDICTION If no action: MRR stays -8% (new baseline) With actions: MRR recovery to -2% by August Confidence: High (cohort data + support signals align)
About this skill
name: kw-data description: Use when write SQL, explore schemas, build visualizations and dashboards, and validate analyses across any data warehouse or CSV upload.
Data (Query, Visualize, Interpret)
Anthropic's Data Analyst plugin turns Claude into a data collaborator. With a warehouse connection (Snowflake, Databricks, BigQuery, or any SQL-compatible database via MCP), Claude queries directly, explores schema and table metadata, runs end-to-end analyses, and iterates on queries based on results. Without a connection, paste SQL results or upload CSV/Excel files for analysis and visualization — Claude will also write SQL for you to run manually and then analyze the results you provide. Slash commands cover query writing, dashboarding, and validation.
What you get
- Direct warehouse querying via MCP (Snowflake, Databricks, BigQuery, SQL-compatible)
- schema and metadata exploration
- visualization and dashboard building
- CSV/Excel upload path when no warehouse is connected
- SQL written for manual execution + result analysis
- connectors Snowflake, Databricks, BigQuery, Definite, Hex, Amplitude, Jira
Customize your output
- Connector mode: warehouse (Snowflake/Databricks/BigQuery) vs standalone CSV/Excel
- SQL dialect: any SQL-compatible
- Output: query, chart, dashboard, narrative insight
Example output
Given a Snowflake connection and 'why did MRR dip in June', Claude explores billing and subscription schemas, writes the churn cohort query, charts new vs churned MRR by plan, and flags a mid-June price-test cohort as the driver.
Best for
Data analysts, analytics engineers, and PMs running SQL and dashboards on a warehouse or spreadsheets
SKILL.md preview
---
name: kw-data
description: Use this skill to write SQL, explore warehouse schemas, and build visualizations or dashboards across any SQL-compatible database or uploaded CSV.
version: 1.0.0
category: Data / Analytics
author: AgentVolt
license: proprietary
tags:
- data
- analytics
- p3
---
# Data (Query, Visualize, Interpret)
Acts as a data collaborator: explores schema metadata, writes and iterates on SQL, and turns query results into visualizations and validated analyses.
## When to use
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