Results for “engineering”

Consolidated engineering SOPs covering adapter development, TDD gating, and multi-agent operational protocols.

Guides AI agents through engineering workflows with eval-first execution, task decomposition, cost-aware model routing, and review focus for generated code.

Explains foundational concepts of context engineering: what context is, attention mechanics, the U-shaped attention curve, and why context quality matters more than quantity.

Drupal and WordPress specialist for theme development, custom plugins/modules, content architecture, and code-first CMS implementation

Provides structured reverse engineering techniques for analyzing compiled, obfuscated, packed, or virtualized targets including binaries, APKs, WASM, firmware, and custom VMs using static and dynamic analysis workflows.

Weekly engineering retrospective. (gstack)

Dispatch-only.

Weekly engineering retrospective. (gstack)

Weekly engineering retrospective. (gstack)

Provides technical leadership frameworks for architecture decisions, engineering team scaling, technology strategy, and technical debt assessment.

Routes reverse engineering, exploitation, penetration testing, malware, mobile, firmware, browser automation, documentation, and security tasks to the appropriate specialist skill. Use when a task spans modules or the correct reverse-skill entrypoint is unclear.

Guides building production-grade AI features with LLM integration patterns, RAG architecture, prompt engineering, and cost optimization.

Applies four disciplined mindsets—audit, debt, help, review—to cut complexity, track deferrals, surface reference, and catch over-engineering in codebases.

Create pull requests following Sentry's engineering practices.

Identify cryptographic algorithms, key generation flaws, and potential decryption opportunities in ransomware samples using static and dynamic analysis.

Generates a single-page engineering runbook template with service overview, alerts table, dashboards, common procedures, on-call rotation, and incident response checklist.

Eng manager-mode plan review. (gstack)

Use when implementing UI functionality with production-grade patterns and safeguards.

Debt that exists only in engineers' heads cannot be prioritised, funded, or

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates.

Engineering observability and reliability: SLO/SLI design, distributed tracing, alerting, dashboards, capacity planning, toil automation, reliability review. Use for instrumentation or SLO definition.

Create pull requests following Sentry's engineering practices.

Write product-facing technical specs that clarify decisions, constraints, dependencies, and rollout.

Observability and reliability engineer — SLOs, alerting, instrumentation, and incident response.

Writes, reviews, and debugs idiomatic Rust code with memory safety and zero-cost abstractions, covering ownership patterns, lifetimes, trait hierarchies, async/await with tokio, and error handling with Result/Option.

Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations. Use when asked to "review the architecture", "engineering review", or "lock in the plan". Proactively suggest when the user has a plan or design doc and is about to start coding — to catch architecture issues before implementation. (gstack) Voice triggers (speech-to-text aliases): "tech review", "technical review", "plan engineering review".

Analyze .NET malware by decompiling and debugging assemblies with dnSpy, deobfuscating with de4dot, and extracting C2 configurations and IOCs.

Audits local content archives and builds a reusable, structured content engineering project with content units, topic maps, and assembly drafts for ongoing reuse.

Enforces production-grade Python and AI engineering standards for FastAPI, LangChain/LangGraph, RAG pipelines, and LLM integrations, covering type safety, error handling, testing, and security.