home / skills / qodex-ai / ai-agent-skills / deployment-automation
npx playbooks add skill qodex-ai/ai-agent-skills --skill deployment-automationReview the files below or copy the command above to add this skill to your agents.
---
name: deployment-automation
description: Automate deployment to Vercel platform. Manages deployment configuration, environment setup, and CI/CD integration.
license: Proprietary. LICENSE.txt has complete terms
---
# 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