Deployment Failure Forensics is a skill that gives AI coding agents a structured framework for diagnosing deployment failures rather than falling back on trial-and-error fixes. Instead of suggesting generic remedies such as deleting lockfiles or disabling type checks, it enforces a staged forensics process: first identifying which phase failed (dependency install, build, or runtime), then comparing local and production environment configurations, and finally isolating the specific delta that caused the failure.
The skill covers a broad range of deployment targets. On the platform side it supports Vercel, Netlify, Render, Railway, Fly.io, Heroku, and Cloudflare Pages. On the infrastructure side it covers Docker, Docker Compose, Nginx, PM2, GitHub Actions, and GitLab CI. This breadth means an agent can apply the same diagnostic methodology whether a failure surfaces in a cloud platform build pipeline or a self-hosted VPS container.
Once the root cause is identified, the skill produces safe recovery plans, rollback checklists, and high-context prompts suitable for use with tools such as Cursor or Claude Code. It also enforces secret-protection practices throughout the investigation, so credentials are not inadvertently exposed during log analysis. The skill is designed for production-only failures — bugs that do not reproduce locally — making it most useful when standard debugging approaches have already been exhausted.