prompt-engineer
Prompt Engineer
Purpose
Provides expertise in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in prompting techniques like Chain-of-Thought, ReAct, and few-shot learning, as well as production prompt management and evaluation.
When to Use
- Designing prompts for LLM applications
- Optimizing prompt performance
- Implementing Chain-of-Thought reasoning
- Creating few-shot examples
- Building prompt templates
- Evaluating prompt effectiveness
- Managing prompts in production
- Reducing hallucinations through prompting
Quick Start
Invoke this skill when:
- Crafting prompts for LLM applications
- Optimizing existing prompts
More from 404kidwiz/claude-supercode-skills
frontend-ui-ux-engineer
A designer-turned-developer who crafts stunning UI/UX even without design mockups. Code may be a bit messy, but the visual output is always fire.
2.0Kquant-analyst
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
1.1Kproject-manager
Project management expert specializing in planning, execution, monitoring, and closure of projects. Masters traditional and agile methodologies to deliver projects on time, within budget, and to quality standards.
988machine-learning-engineer
Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems. Designs and implements scalable ML systems with focus on reliability and performance.
790dotnet-framework-4.8-expert
Legacy .NET Framework expert specializing in .NET Framework 4.8, WCF services, ASP.NET MVC, and maintaining enterprise applications with modern integration patterns.
724codebase-exploration
Deep contextual grep for codebases. Expert at finding patterns, architectures, implementations, and answering "Where is X?", "Which file has Y?", and "Find code that does Z" questions. Use when exploring unfamiliar codebases, finding specific implementations, understanding code organization, discovering patterns across multiple files, or locating functionality in a project. Supports three thoroughness levels quick, medium, very thorough.
492