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.

16 stars

Best use case

prompt-enhancer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using prompt-enhancer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/prompt-enhancer/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/development/prompt-enhancer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/prompt-enhancer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How prompt-enhancer Compares

Feature / Agentprompt-enhancerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this 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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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:**

```bash
view /mnt/user-data/uploads
```

**Gather key information:**

- Project structure and organization
- Technology stack (package.json, pyproject.toml, 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:

```markdown
# [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:**

```markdown
## 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
```

### React Native Projects

**Detect**: package.json with "react-native", app.json/app.config.js, expo dependency

**Key context to gather:**

- React Native version (New Architecture vs Legacy)
- Expo usage (Expo Go, EAS Build, bare workflow, or vanilla RN)
- Navigation (React Navigation, Expo Router)
- State management (Zustand, Redux Toolkit, Context, Jotai)
- Styling (StyleSheet, styled-components, Tailwind RN)
- API approach (Axios, fetch, React Query/TanStack Query)
- Platform-specific code (iOS/Android differences)
- Native modules (Expo SDK vs custom native modules)

**Enhanced requirements should include:**

```markdown
## 구현 범위

### Screens & Navigation

- 스크린: src/screens/[feature]/[Screen]Screen.tsx
- Navigator: [Stack/Tab/Drawer based on existing]
- Navigation params: [TypeScript types]

### Components

- 컴포넌트: src/components/[feature]/[Component].tsx
- Props interface: [existing pattern]
- Styling: [StyleSheet/styled-components]
- Platform-specific: [Platform.select if needed]

### State Management

- Store: [existing state structure]
- Actions/Hooks: [action naming convention]
- Selectors: [if applicable]

### API Layer

- Endpoint: [API URL]
- Method: [GET/POST/PUT/DELETE]
- Response type: [TypeScript interface]
- Error handling: [existing error pattern]

### Native Modules

- Expo SDK: [expo-camera/expo-location/expo-file-system if Expo]
- Custom modules: [if bare RN or ejected]
- Permissions: [Camera/Location/Storage if needed]
- Platform APIs: [iOS/Android specific features]

## 성공 기준

✅ TypeScript 타입 안정성
✅ [Existing components] 재사용
✅ iOS/Android 플랫폼 동작 확인
✅ [Expo Go/EAS/bare workflow] 환경 호환성
✅ 반응형 레이아웃 (다양한 화면 크기)
✅ 성능 최적화 (useMemo, useCallback)
✅ 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:**

```markdown
## 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:**

```markdown
# 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

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