Turn onboard deficiencies, PSC findings, Class observations, and ISM/ISPS/MLC audit issues into professional maritime corrective-action replies, CAPA reports, evidence checklists, and close-out summaries.
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.
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.
Get to the root of why loadbearing instructions fail You get the Drop Table, a plain-English root cause, a rebuild, and a scoreboard you keep
Generate, audit, co-author, and maintain software documentation under a style your agent cannot drift from. Version 3.0.0 adds controlled language (STE-derived), a distilled house style enforced by mechanical gates (vale, markdownlint, cspell), canonical vocabulary that steers agents, OKF v0.2 knowledge bundles with a working attester, and the 2026 spec-driven development landscape with 11 tool-neutral templates. Works across software, hardware, firmware, and embedded projects in any runtime.
Assistente AI autonomo per smistamento documenti, F24, TARI, IMU, IVA, IRPEF, calcolo provvigioni agenti, gestione clienti e OCR istantaneo da schermo e cartelle PC.
A scan tells you what is wrong. This tells you what to do. Same engine as the ADA Website Risk Scanner — 14 WCAG 2.2 AA check families. But instead of a defect list, you get a work plan: findings grouped into batches, ordered by exposure reduced per hour of developer time, each with before/after code, effort estimate, required skill, and a verification step. Three phases. Quick wins first. A project manager can assign it as-is.
Prepare CSV, TSV, Excel and JSONL files before sharing with AI, clients or vendors. Preview candidate matches, redact, use consistent pseudonyms or remove fields, then export separate copies with an audit report. Local processing; no API required. English edition.