“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.

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

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.

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.

ICM-based zero-to-verified engineering workflow.

Master context engineering for AI agent systems.

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…

Run bounded, evidence-driven loops for repeated, resumable, delegated, or scheduled work.

Rigorous agentic engineering workflow — 23 playbooks and 23 principle rules for bug fixes, features, refactors, performance work, investigations, architecture…

Curates the right context for an agent at the right time.

Design and implement focused end-to-end tests against configured disposable environments.

Captures reusable agent context after a pull request's reviewer agents have approved.

Author a self-contained unattended dispatch for a generate-and-select loop (generate candidates, grade, iterate, keep the best for human selection), or for a…

MLOps across model deployment, ML pipelines, monitoring, and feature stores.

fstack engineering execution.