altimate-code: Skill for OpenCode

Cost Report

Requirements

Agent: any (read-only analysis) Tools used: sql_execute, sql_analyze, finops_analyze_credits, finops_expensive_queries, finops_warehouse_advice, finops_unused_resources, finops_query_history

Analyze Snowflake warehouse query costs, identify the most expensive queries, detect anti-patterns, and recommend optimizations.

Workflow

1. Query SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY for the top 20 most expensive queries by credits used:

   SELECT
       query_id,
       query_text,
       user_name,
       warehouse_name,
       query_type,
       credits_used_cloud_services,
       bytes_scanned,
       rows_produced,
       total_elapsed_time,
       execution_status,
       start_time
   FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
   WHERE start_time >= DATEADD('day', -30, CURRENT_TIMESTAMP())
     AND execution_status = 'SUCCESS'
     AND credits_used_cloud_services > 0
   ORDER BY credits_used_cloud_services DESC
   LIMIT 20;
   

   Use sql_execute to run this query against the connected Snowflake warehouse.

2. Group and summarize the results by:

   • User: Which users are driving the most cost?
   • Warehouse: Which warehouses consume the most credits?
   • Query type: SELECT vs INSERT vs CREATE TABLE AS SELECT vs MERGE, etc.

   Present each grouping as a markdown table.

3. Analyze the top offenders - For each of the top 10 most expensive queries:

   • Run sql_analyze on the query text to detect anti-patterns (SELECT *, missing LIMIT, cartesian products, correlated subqueries, etc.)
   • Summarize anti-patterns found and their severity

4. Classify each query into a cost tier:

   Tier	Credits	Label	Action
   1	< $0.01	Cheap	No action needed
   2	$0.01 - $1.00	Moderate	Review if frequent
   3	$1.00 - $100.00	Expensive	Optimize or review warehouse sizing
   4	> $100.00	Dangerous	Immediate review required