writing-infrastructure-code
Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-nativ...
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Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-nativ...
Transforms ambiguous user requests into rigorous Product Requirements Documents (PRDs). Use when requirements are vague or high-level.
Identify independent systems within a project and define clear system boundaries. Produces the Architecture Overview that serves as the foundation for subsequent system design.
Use the WBS method to decompose system design documents into hierarchical tasks. Supports dependency analysis, traceability, and acceptance criteria.
The final gatekeeper. Audits RFCs to reject over-engineering, unnecessary dependencies, and resume-driven development.
Evaluate technology stack options and produce Architecture Decision Records (ADR) using weighted decision matrices and the ATAM methodology.
Analyze runtime behavior, process boundaries, and IPC mechanisms to detect protocol drift risks and process lifecycle issues.
Synthesize all Scout-phase analyses (build-inspector, runtime-inspector, git-forensics, concept-modeler) into a decision-ready system risk report.
Design a detailed technical document for a single system, including architecture diagrams, interface design, data models, and trade-off analysis.
Extract domain concepts from vague user requirements—entities, flows, and “dark matter” (what users don’t say). Based on Domain-Driven Design (DDD).
Analyze build-system topology to identify independent build units, multi-artifact risks, and version-drift hazards.
Expert Go developer for high-performance systems, concurrent programming, and cloud-native microservices. Invoke for Go 1.21+ development, goroutines, generics, gRPC. Keywords: Go, Golang, concurrency, microser...
Analyze Git history to uncover “logical coupling” (files that always change together) and “hotspots” (frequently modified, complex modules). Based on Adam Tornhill’s *Your Code as a Crime Scene*.
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