MCPInstall

prompt-enhancer

Enhance user prompts by analyzing project context (code structure, dependencies, conventions, existing patterns). Use when users provide brief development requests that would benefit from project-specific context to generate more accurate, contextually-aware prompts.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/bear2u/my-skills --skill prompt-enhancer
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads prompt-enhancer automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Prompt Enhancer

Transform brief development requests into clear, detailed requirements by analyzing project context. Present the enhanced requirements to the user for confirmation before implementation.

When to Use This Skill

Use this skill when:

  • User provides a brief development request like "로그인 기능 만들어줘", "API 추가해줘"
  • Request lacks specific implementation details
  • User uploads project files or mentions "the project"
  • Task requires understanding project architecture

Core Workflow

Step 1: Analyze Project Context

Check for uploaded files:

view /mnt/user-data/uploads

Gather key information:

  • Project structure and organization
  • Technology stack (package.json, pubspec.yaml, requirements.txt, etc.)
  • Existing patterns (state management, API calls, routing)
  • Code conventions (naming, file structure)
  • Similar existing features

Step 2: Extract Request Intent

From the user's brief request, identify:

  • Feature type: New feature, bug fix, refactoring, API integration
  • Scope: Single screen, full flow, backend + frontend
  • Dependencies: Related features or systems

Step 3: Build Enhanced Requirements

Create a structured requirement document with:

# [기능명] 구현 요구사항

## 📋 프로젝트 컨텍스트
- Framework: [detected framework and version]
- Architecture: [detected pattern]
- State Management: [detected library]
- Key Libraries: [list relevant dependencies]

## 🎯 구현 범위

### 주요 기능
1. [Main feature 1]
2. [Main feature 2]
3. [Main feature 3]

### 파일 구조

[Expected file structure based on project]


## 📝 상세 요구사항

### 1. [Layer/Component Name]
- **위치**: [File path]
- **목적**: [What it does]
- **구현 내용**:
  - [Specific requirement 1]
  - [Specific requirement 2]
- **기존 패턴 따르기**: [Reference to existing pattern]

### 2. [Next Layer/Component]
...

## ✅ 성공 기준
- [ ] [Acceptance criteria 1]
- [ ] [Acceptance criteria 2]
- [ ] [Acceptance criteria 3]
- [ ] 기존 코드 스타일 및 아키텍처 일관성 유지
- [ ] 모든 주요 기능에 대한 테스트 작성

## 🔍 확인 사항
- [Any questions or clarifications needed]
- [Assumptions made]

---
이 요구사항으로 진행할까요? 수정이 필요한 부분이 있다면 말씀해주세요.

Step 4: Present to User

Important: After creating the enhanced requirements, present them to the user and ask for confirmation:

위 요구사항을 분석해서 정리했습니다. 

이대로 진행해도 될까요? 
수정하거나 추가할 내용이 있으면 말씀해주세요!

Do NOT implement until the user confirms. The goal is to clarify requirements first.

Analysis Patterns by Stack

Flutter Projects

Detect: pubspec.yaml, lib/ directory

Key context to gather:

  • State management (Riverpod, Bloc, Provider, GetX)
  • Architecture (Clean Architecture, MVVM, MVC)
  • Navigation (go_router, auto_route, Navigator)
  • Network (Dio, http)
  • Local storage (Hive, SharedPreferences, SQLite)

Enhanced requirements should include:

## 구현 범위

### Presentation Layer
- 화면: lib/presentation/[feature]/[screen]_screen.dart
- 상태: [StateNotifier/Bloc/Controller] with [state pattern]
- 위젯: 재사용 가능한 컴포넌트

### Domain Layer
- Entity: lib/domain/entities/[name].dart
- UseCase: lib/domain/usecases/[action]_usecase.dart
- Repository Interface: lib/domain/repositories/

### Data Layer
- Model: lib/data/models/[name]_model.dart (fromJson/toJson)
- Repository Implementation: lib/data/repositories/
- DataSource: lib/data/datasources/

### Navigation
- Route: [route path]
- Navigation method: [context.go/push based on router]

## 성공 기준
✅ [State management]로 상태 관리
✅ [Existing widget] 스타일 일관성 유지
✅ API 응답 에러 처리
✅ 로딩 상태 표시
✅ Widget test 작성

Next.js/React Projects

Detect: package.json with "next" or "react"

Key context to gather:

  • Next.js version (App Router vs Pages Router)
  • State management (Zustand, Redux, Context)
  • Styling (Tailwind, CSS Modules, styled-components)
  • API approach (Next.js API routes, external API)
  • TypeScript usage

Enhanced requirements should include:

## 구현 범위

### UI Components
- 컴포넌트: [path]/[ComponentName].tsx
- Props interface: [existing pattern]
- Styling: [Tailwind classes/CSS modules]

### State Management
- Store: [existing state structure]
- Actions: [action naming convention]

### API Layer
- Endpoint: [app/api or external]
- Method: [GET/POST/PUT/DELETE]
- Response type: [TypeScript interface]

### Routing
- Route: [app/[route] or pages/[route]]
- Dynamic segments: [if needed]

## 성공 기준
✅ TypeScript 타입 안정성
✅ [Existing components] 재사용
✅ 반응형 디자인 (모바일/데스크톱)
✅ SEO 최적화 (메타데이터)
✅ Unit test + Integration test

Python (Django/FastAPI) Projects

Detect: requirements.txt, manage.py, main.py

Key context to gather:

  • Framework (Django, FastAPI, Flask)
  • ORM (SQLAlchemy, Django ORM, Tortoise)
  • Authentication approach
  • API documentation (OpenAPI/Swagger)

Enhanced requirements should include:

## 구현 범위

### Models/Schema
- Model: [app]/models.py or schemas/[name].py
- Fields: [field definitions with types]
- Relationships: [ForeignKey/relationships]

### Views/Endpoints
- Route: [path]
- Method: [HTTP method]
- Request/Response schema: [Pydantic models]

### Business Logic
- Service: [if applicable]
- Validation: [input validation]

### Database
- Migration: [migration command]

## 성공 기준
✅ API documentation 자동 생성
✅ Request validation
✅ Error handling with proper status codes
✅ Unit test + Integration test
✅ [Authentication] 적용

Example Enhancement

User input:

로그인 기능 구현해줘

Skill output:

# 로그인 기능 구현 요구사항

## 📋 프로젝트 컨텍스트
- Framework: Flutter 3.16.0 with null safety
- Architecture: Clean Architecture
- State Management: Riverpod (StateNotifier pattern)
- Network: Dio 5.3.0
- Storage: flutter_secure_storage
- Navigation: go_router

## 🎯 구현 범위

### 주요 기능
1. 이메일/비밀번호 로그인 폼
2. JWT 토큰 기반 인증
3. 로그인 성공 시 홈 화면 이동
4. 에러 처리 및 사용자 피드백

### 파일 구조

lib/ ├── presentation/ │ └── auth/ │ ├── login_screen.dart │ ├── login_notifier.dart │ └── login_state.dart ├── domain/ │ ├── entities/user.dart │ ├── usecases/login_usecase.dart │ └── repositories/auth_repository.dart └── data/ ├── models/ │ ├── user_model.dart │ └── login_response.dart ├── repositories/auth_repository_impl.dart └── datasources/auth_remote_datasource.dart


## 📝 상세 요구사항

### 1. Presentation Layer - 로그인 화면
- **위치**: lib/presentation/auth/login_screen.dart
- **목적**: 사용자 로그인 UI 제공
- **구현 내용**:
  - ConsumerStatefulWidget 사용
  - Email TextFormField (이메일 형식 검증)
  - Password TextFormField (8자 이상, obscureText)
  - 로그인 PrimaryButton
  - 회원가입 링크
  - 로딩 상태 시 오버레이 표시
- **기존 패턴 따르기**: core/widgets/custom_text_field.dart 스타일 사용

### 2. State Management
- **위치**: lib/presentation/auth/login_notifier.dart
- **목적**: 로그인 상태 관리
- **구현 내용**:
  - StateNotifier<LoginState> 상속
  - login(email, password) 메서드
  - 성공 시 토큰 저장 후 상태 업데이트
  - 에러 시 에러 메시지 상태 설정
- **기존 패턴 따르기**: 다른 notifier들과 동일한 패턴

### 3. Domain Layer - 엔티티
- **위치**: lib/domain/entities/user.dart
- **목적**: 사용자 도메인 모델
- **구현 내용**:
  - Freezed로 불변 클래스 생성
  - id, email, name, profileImageUrl 필드
- **기존 패턴 따르기**: 다른 entity들과 동일한 구조

### 4. Domain Layer - UseCase
- **위치**: lib/domain/usecases/login_usecase.dart
- **목적**: 로그인 비즈니스 로직
- **구현 내용**:
  - call(LoginParams) 메서드
  - Either<Failure, User> 반환
  - repository 의존성 주입
- **기존 패턴 따르기**: 단일 책임 UseCase 패턴

### 5. Data Layer - API 통신
- **위치**: lib/data/datasources/auth_remote_datasource.dart
- **목적**: 로그인 API 호출
- **구현 내용**:
  - POST /api/auth/login
  - Request: {"email": string, "password": string}
  - Response: LoginResponse (accessToken, refreshToken, user)
  - Dio instance 재사용
- **기존 패턴 따르기**: 기존 datasource들의 에러 처리 방식

### 6. Data Layer - Repository 구현
- **위치**: lib/data/repositories/auth_repository_impl.dart
- **목적**: Repository 인터페이스 구현
- **구현 내용**:
  - login 메서드 구현
  - 토큰 저장 (TokenStorage 사용)
  - DioException 처리
  - UserModel을 User entity로 변환
- **기존 패턴 따르기**: try-catch-Either 패턴

### 7. Navigation 설정
- **위치**: lib/core/router/app_router.dart
- **목적**: 로그인 라우트 추가
- **구현 내용**:
  - /login 라우트 추가
  - 로그인 성공 시 /home으로 리다이렉트
  - 인증 가드 로직
- **기존 패턴 따르기**: 기존 go_router 설정 방식

## ✅ 성공 기준
- [ ] 사용자가 이메일과 비밀번호 입력
- [ ] 로그인 버튼 클릭 시 API 호출
- [ ] 성공 시 토큰 저장 및 홈 화면 이동
- [ ] 실패 시 적절한 에러 메시지 표시 (SnackBar)
- [ ] 로딩 중 버튼 비활성화 및 로딩 인디케이터
- [ ] 이메일 형식 및 비밀번호 길이 검증
- [ ] 기존 코드 스타일 및 아키텍처 일관성 유지
- [ ] Widget test 작성 (로그인 화면)
- [ ] Repository test 작성 (mock API)
- [ ] UseCase test 작성

## 🔍 확인 사항
- API 엔드포인트 주소가 `https://api.example.com`이 맞나요?
- 토큰 만료 시 자동 갱신 기능이 필요한가요?
- 소셜 로그인(구글, 애플 등)도 함께 구현할까요?
- "비밀번호 찾기" 기능이 필요한가요?

---
위 요구사항으로 진행할까요? 수정이 필요한 부분이 있다면 말씀해주세요!

Tips for Effective Enhancement

Always Ask for Clarification

If the project context is unclear or insufficient:

프로젝트 파일을 업로드해주시면 더 정확한 요구사항을 만들 수 있습니다.
또는 다음 정보를 알려주세요:
- 사용 중인 프레임워크
- 상태 관리 라이브러리
- 기존 프로젝트 구조

Include Visual Examples

When helpful, mention existing screens/components:

기존 ProfileScreen과 유사한 레이아웃으로 구현
- AppBar 스타일 동일
- TextFormField 디자인 재사용
- PrimaryButton 컴포넌트 사용

Highlight Dependencies

## 🔗 연관 기능
- UserRepository: 사용자 정보 조회에 재사용
- TokenStorage: 기존 토큰 저장 로직 활용
- ErrorHandler: 공통 에러 처리 적용

Reference Files

For detailed patterns:

  • Enhancement patterns: references/enhancement-patterns.md
  • Framework guides: references/framework-guides.md

FAQ

What does the prompt-enhancer skill do?

Enhance user prompts by analyzing project context (code structure, dependencies, conventions, existing patterns). Use when users provide brief development requests that would benefit from project-specific context to generate more accurate, contextually-aware prompts.

How do I install prompt-enhancer?

Run npx skills add https://github.com/bear2u/my-skills --skill prompt-enhancer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prompt-enhancer/ (personal) or .claude/skills/prompt-enhancer/ (project).

Which AI agents can use prompt-enhancer?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is prompt-enhancer safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains prompt-enhancer?

It is published by bear2u in bear2u/my-skills. It has 56 recorded installs.

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