Guide the creation, testing, and refinement of LLM prompts with structured workflows.
npx skills add https://github.com/JayRHa/AgentSkills --skill prompt-engineer-jayrha
Please help me install this Agent Skill. Skill: prompt-engineer Source: https://github.com/JayRHa/AgentSkills/tree/main/prompt-engineer Command: npx skills add https://github.com/JayRHa/AgentSkills --skill prompt-engineer-jayrha
Designs, refines, and systematically evaluates prompts for LLMs using structure, role framing, few-shot examples, explicit output contracts, and reasoning scaffolds. It also provides a rigorous evaluation approach with golden sets, rubrics, and baselines to compare prompt variants.
Install and run the evaluation harness to compare prompt variants against a golden set.
High-intent search queries and answers about installing and using this skill.
To evaluate prompt variants against a golden set, use LLM-as-judge rubrics and baseline comparisons. This approach systematically scores output accuracy across classification, extraction, and generation tasks to identify the most effective prompt structure.
The best way to enforce structured JSON output is by defining explicit output contracts within your prompt. This technique constrains the model's response format, ensuring downstream parsing succeeds without unexpected formatting errors or missing data fields.
Writing few-shot examples involves selecting representative exemplars and assembling them into your prompt. This guides the model's behavior by demonstrating the desired input-output mapping, significantly improving accuracy for complex reasoning and transformation tasks.
Hallucinations and refusals often occur due to ambiguous system prompts or lack of reasoning scaffolds. You can debug these issues by applying a pattern-driven workflow: clarify the job, draft canonical sections, add output contracts, and run automated evaluation.
You need a chain-of-thought reasoning scaffold when tackling complex extraction, classification, or generation tasks. This mechanism forces the model to articulate intermediate reasoning steps, drastically reducing logical errors and improving overall output reliability.
Yes, you can use an automated evaluation harness to debug system prompts for extraction tasks. By running your prompts against a golden set with defined rubrics, you isolate failure points and systematically refine the prompt pattern.
daemon-blockint-techDocument prompt engineering and evaluation pipeline best practices for tool-using agents.
ThejanaJayalathStandardize machine learning lifecycle from data contracts to production monitoring.
enigmaicon-engScore or compare LLM outputs with rubric-based LLM-as-judge evaluation and structured JSON results.
drbobberAnalyze and refine AI prompts with advanced techniques like Chain-of-Thought.
