원클릭으로 Manus에서 모든 스킬 실행

시작하기

deployment-automation

스타35

포크1

업데이트2026년 1월 5일 07:07

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

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

qodex-ai

qodex-ai/ai-agent-skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Vercel Production Deploy Loop

Instructions

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

Initial Deployment Attempt
- Run vercel --prod to start production deployment
- Wait for deployment to complete
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
Error Fixing
- Make minimal, targeted fixes to resolve the specific error
Retry Deployment
- Run vercel --prod again with the fixes applied
- Repeat steps until deployment succeeds
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

이 저장소의 다른 Skills

같은 저장소

autonomous-agent-gaming

qodex-ai/ai-agent-skills

Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and performance optimization. Use when creating game-playing bots, testing game AI, strategic decision-making systems, or game theory applications.

2026-01-0535

chat-with-arxiv

qodex-ai/ai-agent-skills

Build interactive chat agents for exploring and discussing academic research papers from ArXiv. Covers paper retrieval, content processing, question-answering, and research synthesis. Use when building research assistants, paper summarization tools, academic knowledge bases, or scientific literature chatbots.

2026-01-0535

creative-generation-agent

qodex-ai/ai-agent-skills

Build agents that generate creative content including music, memes, podcasts, and multimedia. Covers generative models, content synthesis, style transfer, and creative control. Use when building creative assistants, automated content creators, multimedia generators, or artistic AI systems.

2026-01-0535

deep-research-agent

qodex-ai/ai-agent-skills

Build agents specialized in conducting thorough research, gathering information from multiple sources, and synthesizing findings. Covers research planning, source evaluation, and report generation. Use when automating market research, competitive analysis, literature reviews, or intelligence gathering.

2026-01-0535

document-chat-interface

qodex-ai/ai-agent-skills

Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools.

2026-01-0535

financial-analysis-agent

qodex-ai/ai-agent-skills

Create agents for financial analysis, investment research, and portfolio management. Covers financial data processing, risk analysis, and recommendation generation. Use when building investment analysis tools, robo-advisors, portfolio trackers, or financial intelligence systems.

2026-01-0535

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:

Initial Deployment Attempt
- Run vercel --prod to start production deployment
- Wait for deployment to complete
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
Error Fixing
- Make minimal, targeted fixes to resolve the specific error
Retry Deployment
- Run vercel --prod again with the fixes applied
- Repeat steps until deployment succeeds
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