Create and refine LLM prompts with explicit output contracts and constraints.
npx skills add https://github.com/JHostalek/dotclaude --skill prompt-jhostalek
Please help me install this Agent Skill. Skill: prompt Source: https://github.com/JHostalek/dotclaude/tree/main/skills/prompt Command: npx skills add https://github.com/JHostalek/dotclaude --skill prompt-jhostalek
This Skill helps turn vague or bloated LLM instructions into focused prompts with clear behavior, constraints, and output contracts, improving consistency and reducing unintended responses.
Use the prompt skill to refine the provided LLM system prompt for clarity, reliability, and model-specific performance.
High-intent search queries and answers about installing and using this skill.
To refine LLM system prompts for behavioral consistency, you must model the target model, task shape, and output contract while removing unnecessary complexity and preserving essential refusal boundaries. This reduces unintended responses and ensures reliable execution.
An output contract in prompt engineering defines strict formatting and behavioral constraints for LLM responses. Establishing a clear output contract ensures the generated text adheres to required structures for classifier, generator, extractor, agent, and judge tasks.
Yes, prompt optimization applies to small open-weight models by adapting structure, examples, decomposition, and constraint density. Model-aware design tailors the system prompts to match the specific reasoning capabilities of instruction-tuned chat or small models.
The best way to structure few-shot exemplars for agent loops is to align examples with explicit task shape, security boundaries, and refusal behavior. This example strategy guides the LLM through expected tool descriptions and multi-step instructions reliably.
LLM output fails despite detailed prompt instructions due to bloated or vague constraints lacking a defined output contract. Simplifying instructions while explicitly modeling the target model and task shape improves output reliability and consistency.
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jralphCraft precise prompts for LLM agents using patterns and templates.
