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".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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".
日本語の概要は準備中です。原文の説明を表示しています。
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."
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。