prompt-engineer_skill

Overview

This skill optimizes prompts for large language models and AI systems to improve reliability, relevance, and safety. It provides structured prompt templates, model-specific tuning advice, and an iterative testing workflow to get consistent outputs. Use it to design system prompts, few-shot examples, and multi-step prompt pipelines.

How this skill works

The skill analyzes the intended use case and constraints, selects suitable prompting techniques (few-shot, chain-of-thought, role-play, etc.), and produces a complete prompt ready for deployment. It always returns the full prompt text plus implementation notes explaining choices, expected outcomes, and testing guidance. Iteration recommendations and error-handling strategies are provided to refine results across models.

When to use it

• Building AI features that depend on reliable text generation
• Refining system or assistant prompts to reduce hallucinations
• Creating prompt chains or multi-step agent flows
• Adapting prompts for specific models (GPT, Claude, open models)
• Designing safety constraints and output format enforcement

Best practices

• Always include explicit output format and examples to reduce ambiguity
• Use few-shot examples for complex or domain-specific tasks, zero-shot for broad instructions
• Set clear constraints and evaluation criteria up front (length, style, prohibited content)
• Test across temperature/settings and iterate with small, focused changes
• Add self-consistency or verification steps for high-stakes outputs

Example use cases

• Create a system prompt and few-shot examples for a customer-support agent to follow brand tone and escalation rules
• Design a chain-of-thought prompt for complex reasoning tasks like multi-step math or planning
• Produce a structured code-review prompt that returns severity, line references, and fixes
• Adapt a prompt for an open-source model with strict formatting and token limits
• Build an error-handling section to validate outputs and request clarification when confidence is low