Deployment Automation: Install, Source and Security | FunnelSlayer
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Deployment Automation

Published by qodex-ai in ai-agent-skills

Review recommended123 installs

What this skill does

Automate deployment to Vercel platform. Manages deployment configuration, environment setup, and CI/CD integration.

Add Deployment Automation to your agent

Review the source and files first. When you are ready, copy the prompt instruction or use the CLI command supported by your environment.

Install with a prompt

Paste this into a compatible coding agent:

add this skill "deployment-automation" from https://github.com/qodex-ai/ai-agent-skills

Install with the CLI

Run this command in a controlled environment after reviewing the repository:

npx skills add https://github.com/qodex-ai/ai-agent-skills --skill deployment-automation

Skill instructions

Vercel Production Deploy Loop

Instructions

When requested to deploy to Vercel production with automatic error fixing:

  1. Initial Deployment Attempt

    • Run vercel --prod to start production deployment
    • Wait for deployment to complete

  2. Error Detection & Analysis

    • CRITICAL: Use Vercel MCP tool to fetch detailed logs:
      • The MCP logs provide much more detail than CLI output
    • Analyze the build logs to identify root cause:
      • Build errors (TypeScript, ESLint, compilation)
      • Runtime errors
      • Environment variable issues
      • Dependency problems
      • Configuration issues
    • Extract specific error messages

  3. Error Fixing

    • Make minimal, targeted fixes to resolve the specific error

  4. Retry Deployment

    • Run vercel --prod again with the fixes applied
    • Repeat steps until deployment succeeds

  5. Success Confirmation

    • Once deployment succeeds, report:
      • Deployment URL
      • All errors that were fixed
      • Summary of changes made
    • Ask if user wants to commit/push the fixes

Loop Exit Conditions

  • ✅ Deployment succeeds
  • ❌ SAME error occurs 5+ times (suggest manual intervention)
  • ❌ User requests to stop

Best Practices

  • Make incremental fixes rather than large refactors
  • Preserve user's code style and patterns when fixing

Example Flow

User: "Deploy to production and fix any errors"

  • Vercel MCP build logs are the PRIMARY source of error information
  • CLI output alone is insufficient for proper error diagnosis
  • Always wait for deployment to complete before fetching logs
  • If errors require user input (like API keys), prompt user immediately

Files included

  • SKILL.md

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