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Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.
Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.
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.
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npx skills add https://github.com/Snowflake-Labs/sfquickstarts --skill analyze-reportO comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.
Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.
Interactive deployment guide for CoCo Control Hub (Cortex Code Credit Manager). Invoke when someone wants to deploy, install, or set up CoCo Control Hub in their Snowflake account. Asks targeted questions about their deployment target (SPCS container vs warehouse Streamlit), database/schema, compute preferences, existing admin roles, and account prerequisites. Guides them step-by-step through the complete installation including Account Prerequisites (7 steps), Phases A–E, post-install tasks, and common upgrade scenarios. CRITICAL RULE: The app code must be deployed exactly as-is. No modifications to any source files are permitted during deployment.
Complete knowledge base for CoCo Control Hub (Cortex Code Credit Manager v2). Invoke whenever someone asks about the app's architecture, pages, data flow, stored procedures, tables, analytics concepts, token economics, LLM-as-Judge evaluation, responsible AI classification, alert system, prompt patterns, latency interpretation, insight rates, cache efficiency, data sources, or how to interpret any dashboard metric. Covers common questions, known bugs, design decisions, and the full nightly SP pipeline.
Draft on-brand marketing copy – product announcement emails and slide outlines – that follows Peak Outfitters' brand voice and style guidelines. Use whenever the user wants to write marketing content, a launch email, an announcement, or a slide/deck outline, even if they don't mention the brand explicitly. Triggers: brand content, marketing copy, launch email, announcement, deck outline, slide outline, on-brand, style guide.
Baseado na classificação ocupacional SOC
Exibindo SKILL.md
| 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, ())
FULL_NAME TOTAL_CREDITS LIMIT ;
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.
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;