Results for “engineering-process”

Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.

Use only when the request concerns standards, responsibility, complexity, smells, naming, refactoring catalogs, or task-plan rules; not routine work.

Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.

Provides the shared STORM methodology, artifact layout, stage-gating contract, citation hygiene, and retrieval fallback. Use when executing any /storm:* skill (generate, research, outline, write, polish). Internal knowledge; never user-invocable.

Analyze engineering delivery throughput, hiring funnel health, team structure, and production discipline for startup VPEs and founders.

Eng manager-mode plan review. (gstack)

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

Routes software engineering tasks across backend, frontend, AI/ML, mobile, DevOps, and infrastructure sub-skills, applying security and ethics gates.

Run the full autonomous engineering pipeline end-to-end (plan, work, code review, test, commit, push, open PR, watch CI, fix CI failures until green). Use only when the user explicitly requests hands-off execution of a software task and provides a feature description; do not auto-route casual conversation here.

Use after solving a non-trivial bug or making a non-obvious decision, to capture it as durable, searchable documentation so the next session doesn't re-derive it from scratch - and use at the start of related work to check whether that documentation already exists.

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

Full autonomous engineering workflow

Expert data engineer specializing in building reliable data pipelines, lakehouse architectures, and scalable data infrastructure. Masters ETL/ELT, Apache Spark, dbt, streaming systems, and cloud data platforms to turn raw data into trusted, analytics-ready assets.

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".

Generates detailed, technology-agnostic implementation blueprints by analyzing project architecture, technology stacks, and data flow patterns.

Detect business email compromise (BEC) attacks using email gateway rules, behavioral analytics, and financial process controls.

6-phase iterative specification execution workflow covering implementation, testing, review, improvement, commit, and progress tracking with quality-gated convergence.

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.

Composite skill — safely refactor a module end-to-end with sequencing, parallel implementation, post-refactor cleanup, and rationale capture. Chains refactor-plan (phased plan + rollback) → three-man-team (architect/builder/reviewer in parallel) → fix-the-suite post-refactor → adr-write → docs-sync. Use for non-trivial refactors that need both careful sequencing and durable record.

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

Resolves legacy references to the devops-engineer capability by routing to the current runtime agent or plugin skill.

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

Use this skill for context gathering, file triage, source maps, assumptions, task framing, prompt hygiene. Trigger when the task involves programming work related to Context Engineering, production implementation, audits, debugging, strategy, or validation.

End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

Coordinate multi-stage edge research pipelines from candidate detection through strategy design, review, revision, and export.

Generate a step-by-step construction plan from a one-line objective, with dependency graphs, parallel step detection, and adversarial review for multi-session engineering projects.

Resolves legacy references to the conversion-engineer capability by routing to the current runtime agent or skill.

Develop firmware for microcontrollers, implement RTOS applications, and optimize power consumption for resource-constrained devices.

Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Building engineer self-branding by turning technical contributions into a professional brand. Use for GitHub/LinkedIn/blog/conference positioning or content strategy.