Turn rough project ideas, source-code summaries, or existing repositories into clear, professional GitHub documentation. GitHub Portfolio Builder AI helps students, developers, AI/ML engineers, and open-source contributors present their work more effectively. Generate structured README files, project overviews, installation guides, feature documentation, technology-stack sections, usage examples, and recruiter-friendly portfolio descriptions.
Paste a git diff and get a PR description, a changelog entry, and reviewer-risk callouts that name the file, what could break, and what to check. Only flags backed by the diff, and "no elevated-risk patterns found" when there are none. Built for AI-sized PRs.
Paste a git diff, get a review-ready pull request: a structured PR description (summary, what/why, test notes), risk callouts (migrations, API/contract changes, config, deletions), a reviewer attention map that surfaces the riskiest hunks first, and a self-review checklist tailored to the diff. For developers opening PRs with Claude Code, Codex, or any coding agent. Uses only the pasted diff; unknowns are [FILL IN]. No API key, no repo access, no personal data.
Turn your GitHub project into a product-accurate tutorial or promo video, with a reviewable preview before rendering.
Paste your staged changes, a diff, or a list of merged commits/PRs and get clean Conventional Commits messages, a grouped CHANGELOG entry in Keep a Changelog format, and a suggested Semantic Versioning bump (major/minor/patch) with the reason. Built for developers and coding agents (Claude Code, Codex) doing the everyday git loop and cutting releases. Uses only the pasted changes; unknowns are [FILL IN]. No API key, no repo access, no personal data.
Product management turns a plain idea or problem into the product documents PMs actually ship: PRDs, product strategy and lean canvases, GTM plans, user personas, OKRs, roadmaps, pricing analysis, SQL queries, and competitor analysis. 66 PM skills covering discovery, strategy, execution, research, data, and growth, all from chat with nothing to install.
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.
Turn incomplete Jira tickets, JSM requests, PRDs, User Stories, and meeting notes into requirements that Engineering can build, QA can test, and Product can accept. Automatically identify requirement gaps, business rules, permissions, validations, UI/API behaviors, exceptions, and edge cases, then generate User Stories, Given/When/Then Acceptance Criteria, PM Checklists, QA Test Cases, risk analysis, and a Requirement Readiness Score based on the selected mode.