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Deployment Failure Forensics
by Shandra
Professional DevOps diagnostics for AI agents to solve failed deployments, Docker crashes, and CI/CD pipeline errors.
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You say
My Next.js app works locally but fails on Vercel during production build. Create a deployment failure report and Cursor prompt. Do not expose secrets, delete lockfiles blindly, disable type checks, or randomly change build commands. Include likely causes, investigation steps, safe fix plan, verification checklist, and prevention steps.
Your agent does
=== DEPLOYMENT FAILURE FORENSICS REPORT ===
Original request: My Next.js app works locally but fails on Vercel during production build.
Interpreted deployment goal: Diagnose and fix a Vercel production build failure for a Next.js app while protecting secrets and avoiding unsafe random changes.
Deployment target: Vercel
Application stack: Next.js
Failure stage: Production build
Current production impact: Unknown. If production is currently broken, roll back to the last successful deployment before continuing risky debugging.
Confirmed facts:
- The app works locally.
- The app fails on Vercel during production build.
- The user does not want unsafe fixes such as deleting lockfiles blindly, disabling checks, exposing secrets, or random build command changes.
Assumptions:
- The local command may differ from Vercel’s build command.
- Vercel may use a different Node version or package manager behavior.
- Some environment variables may be needed at build time.
- The exact build log error is not yet provided.
Unknowns:
- exact Vercel build error
- package manager
- Node version
- package.json scripts
- lockfile status
- environment variable names
- whether the app uses static generation
- whether the build calls APIs or database during build
- recent commits
Evidence needed:
- first real Vercel build error
- package.json
- lockfile
- next.config file
- Vercel build command and output settings
- local Node version and Vercel Node version
- names of environment variables used during build, without values
- recent dependency or config changes
Likely root cause hypotheses:
- Node version mismatch
Evidence: The app works locally but fails in Vercel build, which may use a different Node version.
How to confirm: Compare local Node version with Vercel runtime and package.json engines.
Safe fix: Set the supported Node version in package.json engines or Vercel project settings.
Risk if wrong: The build will continue failing if the real issue is dependency, environment, or static generation related.
- Missing build-time environment variable
Evidence: Next.js builds may need environment variables during static generation or configuration loading.
How to confirm: Search for process.env usage in build-time code, next.config, static generation functions, and server components. Compare names with Vercel environment variable names.
Safe fix: Add the missing variable in Vercel with the correct environment scope. Do not print or expose values.
Risk if wrong: Adding variables will not fix unrelated compile errors.
- Undeclared dependency or dependency group issue
Evidence: Local node_modules may contain packages not declared correctly, or a package needed during build may be incorrectly classified.
How to confirm: Run clean install and production build locally using the same package manager and lockfile.
Safe fix: Declare the missing dependency correctly.
Risk if wrong: Unnecessary dependency changes may add noise.
- Case-sensitive import path
Evidence: Vercel runs on Linux, while local Windows or macOS may tolerate import casing mistakes.
How to confirm: Inspect Vercel error for “module not found” and compare import casing with file names.
Safe fix: Correct import path casing.
Risk if wrong: Low if confirmed by logs.
Do not fix by:
- deleting lockfiles blindly
- disabling TypeScript checks without evidence
- disabling lint checks without evidence
- exposing secret values
- randomly changing build command
- changing framework config without understanding the error
- forcing deployment without a passing build
Investigation plan:
- Read the Vercel build log and identify the first real error.
- Compare local and Vercel Node versions.
- Inspect package.json scripts and engines.
- Confirm package manager and lockfile.
- Run a clean install locally.
- Run the same production build command locally.
- Inspect environment variable names used during build.
- Check next.config and static generation code.
- Check case-sensitive imports if module resolution fails.
- Apply the smallest fix supported by evidence.
Recovery plan: If production is affected, roll back to the last successful Vercel deployment. Debug in a preview deployment until the build passes.
AI coding agent prompt: Diagnose this Next.js deployment failure on Vercel by reading the build logs first. Identify the first real error and the failure stage. Inspect package.json, lockfile, build scripts, Node/runtime version, Next.js config, Vercel project settings, output directory, and environment variable names used during build. Do not expose secret values. Do not delete lockfiles blindly, disable type checks, disable lint checks, or randomly change build commands. Reproduce the production build locally from a clean install if possible. Compare local and Vercel environments. Implement the smallest safe fix supported by evidence. Return root cause, evidence, files inspected, files changed, commands run or recommended, deployment verification steps, prevention notes, and remaining risks.
Verification checklist:
- clean install succeeds
- production build command succeeds locally
- Vercel preview deployment succeeds
- Vercel build logs show no error
- required environment variable names are documented
- no secret values are exposed
- critical pages load after deployment
- rollback path remains available
Prevention plan:
- pin Node version
- document build command
- add .env.example with names only
- add CI production build check
- document Vercel settings in deployment notes
- add a simple post-deploy smoke test
What you get
About this skill
What it does
Deployment Failure Forensics transforms your AI coding agent into a DevOps investigator. It provides a structured framework to diagnose failed deployments across Vercel, Docker, AWS, and VPS environments without the "guess-and-check" anti-pattern. The skill analyzes build logs, project configurations, and environment deltas to find the root cause of failures that only happen in production.
Why use this skill
Standard AI prompts often suggest "deleting the lockfile" or "disabling type checks" as a first resort. This skill enforces a professional forensics process: identifying failure stages (install vs. build vs. runtime), comparing local and production environments, and protecting secrets. It generates safe recovery plans, rollback checklists, and high-context prompts for tools like Cursor or Claude Code, ensuring fixes are evidence-based and minimal.
Supported tools and platforms
- Platforms: Vercel, Netlify, Render, Railway, Fly.io, Heroku, Cloudflare Pages.
- Infrastructure: Docker, Docker Compose, Nginx, PM2, GitHub Actions, GitLab CI.
- Frameworks: Next.js, React, Node.js, Python, PHP, Ruby, and more.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
Shandra is a top-ranked AI prompt creator and premium agent skill builder with an established track record in the AI marketplace. She is recognized as a #1 Top Seller on PromptBase, where she has built a trusted catalog of specialized AI prompts and agent skills for creators, entrepreneurs, educators, marketers, digital product sellers, and business professionals. With over 3,500 AI products published, more than 3,400 sales, and 1,000+ five-star reviews, Shandra has become known for creating practical, polished, and commercially useful AI resources that help users save time, organize complex ideas, generate high-quality content, build digital products, and transform creative concepts into actionable workflows. Her Agensi store focuses on premium, ready-to-use agent skills designed for real-world productivity. Each skill is developed with clear instructions, structured workflows, professional formatting, practical use cases, setup guidance, examples, edge-case handling, and a strong emphasis on usability. Her work combines creative strategy, prompt engineering, documentation design, business thinking, and practical automation into reliable tools that users can apply immediately. Shandra’s mission is to create AI skills that feel professional, useful, and complete from the first use — not generic templates, but carefully built workflow systems that help users think better, work faster, and produce stronger results.
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