prompt-leverage

Prompt optimization for AI coding agents involves transforming raw requests into structured instructions. This skill augments prompts with framework blocks like Objective, Context, Tool Rules, and Verification to ensure reliable execution.

Structuring LLM instructions requires adding framework blocks such as objective, context, work style, output contract, and done criteria. This approach clarifies the task type and sets strict boundaries for reliable AI agent execution.

Yes, prompt engineering frameworks apply to tasks beyond coding. The skill automatically classifies task types like research and writing, tailoring the prompt structure with specific instructions such as target audience and desired tone for those domains.

To convert a vague request into an actionable AI prompt, infer task intensity and apply instruction tuning blocks. This process adds missing details like output format and key points, making the request execution-ready for agents like Claude Code.

Yes, Python is required to run prompt augmentation scripts. The skill relies on standard Python libraries like argparse, re, and textwrap to process and format text inputs into structured prompt frameworks.