This skill should be used when the user asks to "optimize Snowflake queries", "analyze Snowflake SQL performance", "size Snowflake warehouses", "review Snowflake data models", or "troubleshoot Snowflake cost issues".
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安装前可以查看交给 AI 代理的指令内容。
Category: Engineering Domain: Data Warehouse
The Snowflake Development skill provides tools for analyzing and optimizing Snowflake SQL queries, recommending warehouse sizing, and enforcing Snowflake-specific best practices. Helps data engineers reduce costs and improve query performance.
Before analyzing or sizing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
--action; selects the workflow)--file; the subject of the analysis)--workload/--data-volume; drives the warehouse recommendation)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
# Analyze a Snowflake SQL file for optimization opportunities
python scripts/snowflake_query_helper.py --file queries.sql --action analyze
# Get warehouse sizing recommendations
python scripts/snowflake_query_helper.py --action warehouse-sizing --workload "etl" --data-volume "500GB"
# Optimize a specific query
python scripts/snowflake_query_helper.py --file slow_query.sql --action optimize
| Tool | Purpose | Key Flags |
|---|---|---|
snowflake_query_helper.py | Analyze, optimize Snowflake SQL and recommend warehouse sizes | --file, --action, --workload, --data-volume |
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This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
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Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
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Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
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Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
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Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
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Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
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