Deploy Doctor

The Problem. Deployment failures are rarely mysterious — they're predictable. The same handful of mistakes repeat across every team and every project: hardcoded API keys committed to source, SQLite databases and file uploads that silently vanish the moment a free-tier host (Render, Heroku, Railway, Fly.io) redeploys or restarts, missing environment variables discovered only in production, and absent health-check endpoints that leave load balancers blind to crashes. These aren't runtime surprises — they're static facts sitting in the code before anyone even pushes. Yet the tools developers already use daily — ESLint, Semgrep, Snyk, hosting dashboards — don't catch them, because none of these tools understand deployment context. The result: a typical cycle of push → deploy → crash → dig through logs → fix locally → redeploy, often costing 30–60 minutes of downtime and debugging per incident. The Solution. Deploy Doctor is an AI development partner, built on IBM Bob 2.0, that closes this gap in two connected phases. Part 1, the Pre-flight Scanner, runs four static risk checks — hardcoded secrets (via keyword matching plus real-world key fingerprinting for Stripe, AWS, OpenAI), ephemeral storage usage, environment variable coverage gaps, and missing health endpoints — against any local repo or public GitHub URL, producing a severity-tagged report with file:line locations and a clear SAFE/NOT SAFE verdict. Part 2, the Self-Healing Loop, goes further: it deploys the app to Render, waits for it to crash, fetches and analyzes the real logs, identifies root cause, automatically patches the code, pushes the fix to GitHub, redeploys, and verifies the app is healthy — fully unattended, with reliability guardrails like a 3-attempt cap and timeout/retry logic. Target Users. Individual developers, small teams, and student/indie projects deploying Node.js backends to free or low-tier hosting — exactly the audience most likely to hit ephemeral-storage surprises and secret leaks, and least likely to have dedicated DevOps support. How They Interact. Developers use a simple CLI (deploy-doctor scan, serve, heal) with --fix and --json flags for local or CI use, or a web dashboard with animated reports, a donut-chart breakdown, one-click auto-fix, and a live browser terminal streaming logs in real time via Server-Sent Events — no separate tooling required. What Makes It Creative and Unique. Existing tools stop at detection: linters flag code style, Snyk flags dependencies, hosting dashboards show logs after the fact — none predict deployment-specific failure modes or act on them. Deploy Doctor is the only tool that connects static risk prediction → live crash evidence → automated fix → verified redeploy into one closed, autonomous loop, cross-referencing what it predicted against what actually broke. Effectiveness and Efficiency. By catching Critical issues before deploy, Deploy Doctor prevents downtime rather than reacting to it — and when something does slip through, the self-healing loop resolves it in minutes instead of the usual 30–60-minute manual debugging cycle, with zero human intervention required to identify, patch, and confirm the fix.