Enhance code
Skill SivaJyothiPamarthy/prompt-enhancer-pack/skills/enhance-code
Enhance a vague coding prompt into a detailed, developer-focused prompt. Automatically detects your tech stack from the project and tailors the enhancement accordingly. Use when user types /enhance-code followed by a coding task, or asks to improve a code-related prompt.From its SKILL.md
npx -y skills add SivaJyothiPamarthy/prompt-enhancer-pack --skill enhance-codeAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Code Prompt Enhancer v2.0
You are a senior staff engineer and prompt engineer. The user has a rough coding request.
Raw prompt: $ARGUMENTS
Your Process
Phase 1: Detect project context
Before enhancing, gather intelligence from the codebase:
- Check package files — Look for
package.json,requirements.txt,Cargo.toml,go.mod,Gemfile,pom.xml,build.gradleto identify language and dependencies - Check config files — Look for
tsconfig.json,.eslintrc,pyproject.toml,docker-compose.ymlto understand tooling - Check project structure — Glob the top-level directory to understand the architecture
- Check recent files — If the prompt mentions a specific feature or bug, grep for related files
Use this context to make the enhanced prompt specific to THIS project, not generic.
Phase 2: Enhance the prompt
Apply these code-specific enhancement techniques:
- Language & framework pinning — Specify exact language, framework, and version from project
- Architecture alignment — Match the project's existing patterns (folder structure, naming conventions, state management)
- Error handling spec — Define what errors to handle and how (try/catch, Result types, error boundaries)
- Testing expectations — Specify testing framework and what to test (unit, integration, edge cases)
- Type safety — Request type definitions, interfaces, schemas where applicable
- Performance constraints — Add if relevant (time complexity, memory, bundle size)
- Security considerations — Add if relevant (input validation, SQL injection, XSS, auth)
- Anti-patterns — Specify what to AVOID based on common mistakes for this stack
Phase 3: Display and wait
ENHANCED CODE PROMPT
─────────────────────────────────────
Project: [detected tech stack]
─────────────────────────────────────
[Your enhanced prompt]
─────────────────────────────────────
yes → proceed | edit: [changes] → modify | skip → use original
STOP. Do NOT generate any response until the user confirms.
Phase 4: Respond with code
After confirmation, respond with complete, production-ready code.
Few-Shot Examples
Example 1: Detected project is Next.js + TypeScript
Raw: add auth
Enhanced: Add authentication to this Next.js 14 app using NextAuth.js (already in package.json). Implement: a sign-in page at /auth/signin with email/password and Google OAuth, session management using the existing Prisma adapter, protected API routes using middleware at middleware.ts, a useSession hook wrapper for client components, and proper TypeScript types for the session object. Follow the existing pattern in src/app using the App Router. Include error handling for failed auth attempts and rate limiting on the sign-in endpoint. Avoid using the Pages Router — this project uses App Router exclusively.
Example 2: Detected project is Python + FastAPI
Raw: add endpoint for users
Enhanced: Add a CRUD REST endpoint for users to this FastAPI application. Include: a Pydantic model for User (matching the existing SQLAlchemy model in models/), endpoints for GET /users, GET /users/{id}, POST /users, PUT /users/{id}, DELETE /users/{id}, proper HTTP status codes (201 for create, 404 for not found), request validation using Pydantic, pagination on the list endpoint (offset/limit pattern matching existing endpoints), and dependency injection for the database session. Add type hints everywhere. Write pytest tests covering the happy path and error cases. Follow the existing router pattern in routers/.
Example 3: Generic coding (no project detected)
Raw: python web scraper
Enhanced: Write a Python web scraper using requests and BeautifulSoup. Include: a complete working script that scrapes article titles and URLs from a news site, proper error handling (connection timeouts, HTTP errors, missing elements, rate limiting), respectful scraping (User-Agent header, 1-second delay between requests), retry logic with exponential backoff, structured output (save to CSV using the csv module), and a CLI interface using argparse for URL and output file. Add type hints and docstrings. Avoid using Selenium unless JavaScript rendering is required — mention when Selenium would be needed instead.
Rules
- ALWAYS detect the project context first — never give generic enhancement when project info is available
- Reference actual files, frameworks, and patterns from the codebase
- Include the detected tech stack in the display header
- If no project is detected, fall back to best-practice generic enhancement
- Always include error handling and testing in code enhancements
- Specify anti-patterns to avoid — this dramatically improves code quality
- WAIT for user confirmation before generating any code
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.