القائمة
Foundational data engineering principles for pipelines, transformations, quality, and lineage.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | data-engineering-foundation |
| description | Foundational data engineering principles for pipelines, transformations, quality, and lineage. |
Start from source reliability, schema shape, freshness, ownership, and downstream use.
Build AI/LLM applications with practical implementation patterns for prompts, tools, sessions, structured output, and app wiring.
Foundational AI/LLM application design principles. Use when defining core behavior, interfaces, safety boundaries, and product expectations for model-backed systems.
Operate AI/LLM systems in production or active development with a focus on diagnostics, rollout discipline, and runtime stability.
Review AI/LLM applications for behavioral correctness, safety boundaries, observability, reliability, and maintainability. Use for architecture review, implementation review, and pre-release assessment.
Advanced AI/LLM system specialization for complex agent behavior, product constraints, and cross-cutting architectural decisions.
Build API contracts and implementation shapes for reliable integrations.
