“data engineering”

Use when Malvex needs to turn validated findings into YARA, Sigma, EDR, network, memory, or behavioral detections.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit…

Use this skill when wiring the DOCA SHA Offload Engine (an OpenSSL ENGINE) into an existing OpenSSL pipeline to offload one-shot SHA-1, SHA-256, or SHA-512…

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

Recommend playbooks; apply human-selected methods.

Constructs technical Suno style prompts for the current model family (v6, v6-wild, v6-mini), selects models and generation settings, and optimizes prompts.

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

Use when implementing or reviewing frontend architecture, component variants, data boundaries, localization, preferences or SSR hydration while preserving…

Practical guide for building production ML systems based on Chip Huyen's AI Engineering book.

ICM-based zero-to-verified engineering workflow.

A CPU performance engineering brain: a vetted reading list, benchmarks and cited source passages

Use when reviewing a vd-studio pull request as the automated AI PR reviewer (OpenHands pr-review plugin) — judging test coverage for a diff, structuring the…

Master context engineering for AI agent systems.

AI Engineering Fluency

Rob Bos

Track your AI Engineering Fluency — daily and monthly token usage, cost estimates, and AI fluency insights in VS Code.

A/B testing infrastructure, feature flags (LaunchDarkly, Unleash), experimentation platforms, PLG patterns, and funnel optimization.

Use when designing, building, reviewing, or polishing user interfaces, motion, component behavior, interaction details, and product-feel across any frontend…

Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI.

Craft rules for web UI polish (press states, origins, transforms, clip-path, gestures, performance, accessibility) and a Before/After review table.

Reference manual for Python project layout, uv dependencies, static checks, tests, packaging, and CI conventions.

Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI.

Inject controlled failures to test system resilience, validate recovery paths, and build reliability confidence.

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

Orchestrates substantial frontend and UI work with audit-first context, product-aware design direction, shadcn-aware component engineering, Storybook state…

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