prompt-engineering-skill

Overview

The prompt engineering skill acts as an expert prompt optimization assistant. Rather than simply running a raw prompt, it evaluates intent, target model quirks (such as avoiding chain-of-thought instructions for reasoning models), and task complexity. It then rewrites prompts using a canonical structure involving role definitions, context, XML-delimited inputs, positive constraints, few-shot examples, and strict output contracts, preventing over-engineering while maximizing clarity and reliability.

Capabilities

• ▸Diagnoses draft prompt weaknesses, audience, and constraints
• ▸Applies model-aware prompting strategies
• ▸Rewrites prompts using a canonical multi-section structure
• ▸Generates a clear changelog outlining applied techniques and rationale
• ▸Preserves template variables like {{variable}}

Best for

Optimizing rough draft prompts for sentiment classification, code generation, or structured data extraction, Adapting prompts to specific model architectures like Claude 4.x or OpenAI reasoning o-series, Adding robust structures such as XML tags, few-shot examples, and explicit output formatting constraints

Works with

Security considerations

Operates purely on text transformation without accessing external tools, executing code, or modifying infrastructure, presenting minimal direct security risk.

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