“data engineering”

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure.

Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka.

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure.

Data engineering workflows for designing reliable pipelines and datasets: ingestion, transforms, orchestration, schema evolution, data contracts, quality…

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure.

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.

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

Design and build use cases, POCs and production systems with TypeSafe's Jev decision model (System One): choose a design mode and a use-case archetype, build…

Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show.

Expert analytics engineering covering data modeling, dbt development, data transformation, and semantic layer management.

A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems.

Use when answering org-level or repo-level GitHub maintenance questions about repository inventory, branch health, PR backlog, or safe cleanup follow-up.

Your software-engineering brain trust.

Turns a linear agent, prompt chain, or orchestration script into an engineered graph — nodes with contracts, edges that carry data, fan-out/fan-in only where…

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features

Use when preparing a medical-imaging dataset (DICOM/NIfTI) for modelling.

Use when implementation is complete and the user asks to raise, create, open, prepare, or update a PR for an existing Linear issue in this repository

ICM-based zero-to-verified engineering workflow.

Master context engineering for AI agent systems.

Collect immutable, timestamped ICAIRE LinkedIn and X post-level snapshots when refreshing Audience Growth Engine source data.

Chaos engineering: hypothesis-driven fault injection to surface weakness before users do.

Understanding how a compiled, obfuscated, packed or virtualized target works: binaries and firmware, Android APKs and iOS IPAs, .NET and JVM assemblies, WASM…

Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and…