Analyze failed CI/CD pipelines, identify likely root causes, inspect repository configuration and code context, and produce actionable, validated remediation guidance for GitHub Actions, GitLab CI, Jenkins, and related development pipelines.
Optimize Dockerfiles for build speed, image size, and security. Multi-stage builds with CIS Docker Benchmark compliance.
Paste a diff and get a risk-ranked checklist of what it'll actually break — hidden dependencies, config drift, and blast radius that static analysis can't see, checked before you ship.
Catches cascade failures that partial checks miss — pre-check names the risk before editing, post-check demands evidence values after, then auto-repairs in a loop while rereading the entire file every cycle until zero errors remain.
A senior-engineer-style code review assistant: severity-ranked findings, security smell detection, and constructive feedback across 8 language families.
Systematic QA of CLI tools and pipelines across multiple real workspaces using SFDPOT heuristics. Executes structured test flows, evaluates consistency oracles, collects evidence, and produces a structured bug report with P1/P2/P3 priorities and a release readiness verdict.
I'm a full-stack engineer who's shipped and maintained Next.js (App Router), React, and TypeScript apps under real production load. I kept watching AI write code that looked done - then leaked NEXT_PUBLIC_ secrets, swallowed errors, and skipped every state but "success." Shipwright distills the senior-engineer checklist I reach for when things break at 2 a.m. - secrets, auth, input validation, caching, and the unhappy paths - so your AI ships production-grade code, not demos.
Assess Expo release readiness with evidence-based priorities.