Halp - Pull Request Assesor and Code Reviewer

Halp reorders the GitHub pull request review queue by risk, not arrival time. In 2026, code review has become the central bottleneck in software development: GitLab research found 85% of developers agree the bottleneck has shifted from writing code to validating it. AI-generated pull requests now wait 4.6x longer for first review and get accepted only 32.7% of the time, versus 84.4% for human-written code. Reviewers are overwhelmed, and the riskiest changes often sit buried in the queue with no way to tell which one needs attention first. Halp connects to any GitHub repository and scores every open pull request across six weighted signals: change size, AI-generated fingerprints (co-author trailers, commit bursts, ratio anomalies), PR age, sensitive file paths (auth, payment, config, secrets), commit message quality, and reviewer engagement. Each PR gets a 0-100 risk score mapped to four badges, Low, Medium, High, and Critical, with a transparent breakdown showing exactly why a PR scored the way it did, not a black-box number. The result is a single dashboard that reorders the review queue by priority, so teams audit the most dangerous changes first instead of working through PRs in the order they arrived. Built on Next.js 16, TypeScript, and Tailwind CSS, with zero database and only 3 runtime dependencies, Halp is lightweight (93.9 kB first load) and deploys instantly on Vercel. Security is built in from the start: GitHub tokens are never logged or stored, all input is sanitized, and every response carries standard security headers. Engineering teams adopting AI coding assistants can use Halp to keep code review from becoming their next bottleneck, so a faster pace of AI-assisted development doesn't come at the cost of code quality or production stability. Halp was built using IBM Bob 2.0 throughout the IBM Bob 2.0 Hackathon.