Agent Skills › langfuse/langfuse › analyze-cloud-costs

analyze-cloud-costs

GitHub

用于分析 Langfuse Cloud 基础设施成本结构。通过 Metabase 数据集市查询 AWS 与 ClickHouse 费用、按服务/账户拆分驱动因素，识别成本异常及趋势，提供基于数据的成本洞察报告。

.agents/skills/analyze-cloud-costs/SKILL.md langfuse/langfuse

Trigger Scenarios

询问云支出详情分析 AWS 与 ClickHouse 成本占比排查成本增长原因或回归查看每日追踪事件成本

Install

npx skills add langfuse/langfuse --skill analyze-cloud-costs -g -y

More Options

Non-standard path

npx skills add https://github.com/langfuse/langfuse/tree/main/.agents/skills/analyze-cloud-costs -g -y

Use without installing

npx skills use langfuse/langfuse@analyze-cloud-costs

指定 Agent (Claude Code)

npx skills add langfuse/langfuse --skill analyze-cloud-costs -a claude-code -g -y

安装 repo 全部 skill

npx skills add langfuse/langfuse --all -g -y

预览 repo 内 skill

npx skills add langfuse/langfuse --list

SKILL.md

Frontmatter

{
    "name": "analyze-cloud-costs",
    "description": "Analyze Langfuse Cloud infrastructure cost structure using Metabase cost\nmarts. Use when asked about cloud spend, AWS versus ClickHouse cost splits,\ncost drivers by provider\/service\/usage type\/account, daily cost per tracing\nevent, infra cost dashboards, or cost regressions visible in Metabase."
}

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.