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Analyze Snowflake AI cost data and generate insights and reports.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
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**[REQUIRED]** Use for ALL tasks combining Snowflake Cortex AI functions with Ibis (Python dataframe library): AI_COMPLETE, AI_SENTIMENT, AI_CLASSIFY, AI_EXTRACT, AI_FILTER, AI_SUMMARIZE_AGG, SNOWFLAKE.CORTEX.COMPLETE, SNOWFLAKE.CORTEX.EMBED_TEXT_768/1024, vector similarity search, semantic search, EnrichmentPipeline, direct Cortex REST API calls (PAT auth, JWT auth, streaming SSE, tool calling), cortex_ibis, cortex_rest.
Deploy AI Cost Dashboard stored procedures and Streamlit app to Snowflake.
Build and manage a Snowflake AI Cost Dashboard using stored procedures and Streamlit. Use for **ALL** requests involving: AI cost, AI spending, cost dashboard, spending dashboard, Snowflake AI credits, Cortex cost tracking. DO NOT attempt AI cost dashboard work manually - invoke this skill first.
| name | analyze-report |
| description | Analyze Snowflake AI cost data and generate insights and reports. |
| parent_skill | ai-cost-dashboard |
From ai-cost-dashboard SKILL.md when user selects Analyze / Report intent.
ADMIN_DB.AI_COSTS)| Table | Purpose |
|---|---|
ADMIN_DB.AI_COSTS.CORTEX_AI_UNIFIED_COSTS | Aggregated costs by date/service/model |
ADMIN_DB.AI_COSTS.USER_AI_COSTS | Per-user, per-query cost detail |
Service categories in unified table: LLM Functions, Analyst, Document Processing, Fine Tuning, Provisioned Throughput, REST API, Search, Document AI, LLM Functions Query, Cortex Code CLI
Goal: Understand what the user wants to analyze.
Actions:
Ask user for analysis parameters:
What would you like to analyze?
1. Overall AI credit consumption
2. Cost breakdown by service category
3. Cost trends and anomalies
4. Cost attribution by user
5. Model-level token usage
Time range: Last 7 / 30 / 90 / 365 days / Custom
Optionally refresh data first:
CALL ADMIN_DB.AI_COSTS.REFRESH_CORTEX_AI_COSTS(<days>);
CALL ADMIN_DB.AI_COSTS.REFRESH_USER_AI_COSTS(<days>);
Output: Defined analysis scope and time range
Goal: Retrieve AI cost data.
Actions:
Overall consumption:
SELECT SERVICE_CATEGORY, SUM(CREDITS) AS TOTAL_CREDITS, SUM(TOKENS) AS TOTAL_TOKENS, SUM(REQUEST_COUNT) AS TOTAL_REQUESTS
FROM ADMIN_DB.AI_COSTS.CORTEX_AI_UNIFIED_COSTS
WHERE USAGE_DATE >= DATEADD('day', -<days>, CURRENT_DATE())
GROUP BY SERVICE_CATEGORY
ORDER BY TOTAL_CREDITS DESC;
Daily trend:
SELECT USAGE_DATE, SUM(CREDITS) AS DAILY_CREDITS
FROM ADMIN_DB.AI_COSTS.CORTEX_AI_UNIFIED_COSTS
WHERE USAGE_DATE >= DATEADD('day', -<days>, CURRENT_DATE())
GROUP BY USAGE_DATE ORDER BY USAGE_DATE;
Top users:
SELECT FULL_NAME, SUM(CREDITS) AS TOTAL_CREDITS, COUNT(DISTINCT QUERY_ID) AS REQUESTS
FROM ADMIN_DB.AI_COSTS.USER_AI_COSTS
WHERE USAGE_DATE >= DATEADD('day', -<days>, CURRENT_DATE())
GROUP BY FULL_NAME ORDER BY TOTAL_CREDITS DESC LIMIT 20;
Model usage:
SELECT MODEL_NAME, SUM(TOKENS) AS TOTAL_TOKENS, SUM(CREDITS) AS TOTAL_CREDITS
FROM ADMIN_DB.AI_COSTS.CORTEX_AI_UNIFIED_COSTS
WHERE USAGE_DATE >= DATEADD('day', -<days>, CURRENT_DATE())
GROUP BY MODEL_NAME ORDER BY TOTAL_TOKENS DESC;
Output: Raw cost data
Goal: Analyze data and surface key findings.
Actions:
Calculate key metrics:
Identify anomalies:
Compile findings
Output: Analysis summary
Goal: Deliver findings to user.
Actions:
Present structured report:
Cortex AI Cost Analysis Report
Period: <start> to <end>
Summary:
- Total credits: X
- Total tokens: X
- Total requests: X
- Daily average: X credits/day
Top Services:
1. <category> - X credits (Y%)
2. <category> - X credits (Y%)
Top Users:
1. <name> - X credits, N requests
Top Models:
1. <model> - X tokens
Anomalies:
- [Any spikes or unusual patterns]
Recommendations:
- [Actionable suggestions]
STOP: Ask if user wants deeper analysis on any area.
If requested, drill down and repeat from Step 2 with narrower scope.
Output: Delivered report
AI cost analysis report with insights, trends, anomalies, and recommendations.