“data engineering”

自愈数据管道专家——使用气隙隔离的本地 SLM 和语义聚类，自动检测、分类和修复大规模数据异常。专注于修复层：拦截坏数据、通过 Ollama 生成确定性修复逻辑，并保证零数据丢失。不是通用数据工程师——而是当你的数据出了问题且管道不能停的时候，出手的外科手术级专家。

Bind a Cursor agent (including Grok Bot cloud sessions) to the data-engineering-knowledge-capture ContentPack.

Bind a Grok Bot agent to Data Engineering Knowledge Capture.

Bind data-engineering-knowledge-capture for LangChain Deep Agents / Deep Agents Code.

Extracts logic from UI/rendering, enforcing a data-driven architecture.

Organism-expressed skill from paradigm data_pipeline->asynchronous

Specialist in self-healing data pipelines — uses air-gapped local SLMs and semantic clustering to automatically detect, classify, and fix data anomalies at…

Moved to data-pipelines in skills-developer.

Expert database engineering for application data layers: schema and ERD design, relational modeling, indexing, query performance gates, views, triggers…

Curates a concise daily briefing on data engineering, databases, and open-source data tools.

Data engineering playbook for atlas-data-eng.

Hub for data-engineering pipeline work — storage/ingest/serve choice plus shared discipline (idempotency, incremental-not-full-recompute, schema fencing…

You are a **Databricks Engineer** — a professional operating at the pinnacle of data and AI engineering excellence.

Use for AI and agent engineering work: system prompt design, tool call architecture, context engineering, memory and learning systems, multi-agent…

Analytics engineering across data modeling, dbt, transformation, and semantic layers.

Use for any Data Engineering question.

Data and Database Engineer expert in schema design, query optimization, and data pipelines.

Load for system-level data engineering decisions on any platform — sizing, service selection, pipeline shape, layered modeling, idempotency, observability…

Make an engineering drawing of a part as a PDF - orthographic views with hidden lines and centre marks, real dimensions, hole callouts, notes and a title…

Guide engineering design and implementation decisions.

Analytics engineering - dbt projects, maintainable SQL as CTE pipelines, event patterns (funnels, sessions, retention, window functions, MATCH_RECOGNIZE).

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

Use for discovering and reverse-engineering data sources: official APIs, XHR/fetch, GraphQL, persisted queries, Algolia, Shopify, Salesforce Commerce Cloud…

Break down complex bioinformatics, data engineering, and algorithmic pipelines from first principles.