Analyze Cloud Costs Agent Skill

Analyze Cloud Costs

Overview

Use this skill for evidence-backed Langfuse Cloud cost analysis. The primary

source is the Metabase infra cost dashboard and its production cost marts; the

deliverable should name the time window, query grain, top drivers, and caveats.

Workflow

• Clarify the question and choose the grain:
• Headline daily totals: total, AWS, ClickHouse, tracing events, and cost per

100k events.

• Cost structure: provider, service, usage type, operation, account, and day.
• Driver or regression analysis: compare a recent complete-day window against

a prior baseline.

• Load references/cost-marts.md for table IDs,

field IDs, query examples, and caveats.

• Use the Metabase MCP. If the Metabase tools are not visible, discover them

with tool search before falling back to manual interpretation.

• Prefer complete UTC days. Avoid treating current-day AWS cost as final

because AWS CUR rows can arrive late.

• Start broad, then drill down:
• Provider split.
• Service split within the dominant provider.
• Usage type, operation, and account split for the top services.
• Daily trend when explaining change over time.
• Report only what the queried data supports. If a requested slice is absent,

say that no rows were found for that slice instead of inventing a driver.

Query Rules

• Use mcp__metabase__.query for quick reads. Use

construct_query plus execute_query when you need to inspect or reuse the

opaque query.

• Pass filters, aggregations, group_by, and fields as JSON arrays. Some

tool schemas may display these as strings; if that happens, serialize the same

arrays without changing their shape.

• Keep limits explicit and small enough for analysis. Use pagination only when

the continuation token is needed.

• Include the Metabase dashboard link or query result context in the final

answer when useful.

Output Expectations

Summarize:

• Time window and whether it uses complete UTC days.
• Total cost and provider split when relevant.
• Top cost drivers by service, usage type, operation, or account.
• Trend or baseline comparison when the user asks "why did this change?"
• Caveats, especially incomplete current-day AWS data and ClickHouse credit

labeling in the unified mart.