googlecloudplatform/db-context-enrichment
6 skills · 6 total installs
$npx skills add googlecloudplatform/db-context-enrichmentSkillInstalls
autoctx-bootstrapA context engineering agent designed to generate, manage, and optimize structured context sets from your database schemas. It bridges the gap between Large Language Models (LLMs) and databases by compiling, evaluating, and maintaining the precise operational context needed for highly accurate natural language-to-SQL query generation.autoctx-dataset-generationA context engineering agent designed to generate, manage, and optimize structured context sets from your database schemas. It bridges the gap between Large Language Models (LLMs) and databases by compiling, evaluating, and maintaining the precise operational context needed for highly accurate natural language-to-SQL query generation.autoctx-evaluateA context engineering agent designed to generate, manage, and optimize structured context sets from your database schemas. It bridges the gap between Large Language Models (LLMs) and databases by compiling, evaluating, and maintaining the precise operational context needed for highly accurate natural language-to-SQL query generation.autoctx-hillclimbA context engineering agent designed to generate, manage, and optimize structured context sets from your database schemas. It bridges the gap between Large Language Models (LLMs) and databases by compiling, evaluating, and maintaining the precise operational context needed for highly accurate natural language-to-SQL query generation.autoctx-initA context engineering agent designed to generate, manage, and optimize structured context sets from your database schemas. It bridges the gap between Large Language Models (LLMs) and databases by compiling, evaluating, and maintaining the precise operational context needed for highly accurate natural language-to-SQL query generation.context-generation-guideA context engineering agent designed to generate, manage, and optimize structured context sets from your database schemas. It bridges the gap between Large Language Models (LLMs) and databases by compiling, evaluating, and maintaining the precise operational context needed for highly accurate natural language-to-SQL query generation.