prompt-engineer

Design and optimize prompts for LLM output quality and reliability.

Updated Mar 17, 2026
One-click install
npx skills add https://github.com/Estom/aiflex --skill prompt-engineer-estom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/Estom/aiflex/tree/main/skills-repo/Jeffallan-skills/prompt-engineer
Command: npx skills add https://github.com/Estom/aiflex --skill prompt-engineer-estom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Designing and tuning prompts to produce reliable, cost-efficient, and validated outputs from large language models while reducing hallucinations, format errors, and model-specific inconsistencies.

Core Features & Use Cases

  • Prompt Design & Patterns: Practical templates and selection guidance for zero-shot, few-shot, chain-of-thought, ReAct, and tree-of-thought approaches.
  • Optimization & Token Efficiency: Iterative optimization loop, token reduction techniques, and diagnostic frameworks to improve accuracy and reduce cost.
  • Evaluation & Testing: LLM-as-judge methods, automated test suites, A/B testing, regression detection, and CI integration for prompt validation.
  • Structured Outputs & Validation: Schema design for JSON mode and function calling, retry-with-correction flows, and Pydantic/Zod validation examples.

Quick Start

Generate a prompt that returns a validated JSON summary with three prioritized action items and confidence scores for the following document: {document}

Frequently Asked Questions about prompt-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design LLM prompts that consistently return valid structured JSON outputs?▼

Design LLM prompts for structured outputs by defining schema constraints for JSON mode and function calling, applying validation examples, and implementing retry-with-correction flows to ensure reliable, format-compliant model responses.

What's the best way to evaluate prompt quality and detect regressions in production?▼

Evaluate prompt quality and detect regressions using LLM-as-judge methods, automated test suites, A/B testing, and CI-driven regression checks to validate prompt variations and maintain production reliability.

How do I use few-shot and chain-of-thought prompting to reduce LLM hallucinations?▼

Reduce LLM hallucinations using few-shot and chain-of-thought prompting by selecting practical templates that guide reasoning steps, applying ReAct or tree-of-thought patterns to improve output accuracy and consistency.

How can I optimize prompt token efficiency to reduce LLM API costs without losing accuracy?▼

Optimize prompt token efficiency by applying an iterative optimization loop and token reduction techniques, using diagnostic frameworks to maintain output accuracy while lowering LLM API costs.

Can I use Pydantic and Zod schema validation for LLM function calling outputs?▼

Yes, you can use Pydantic and Zod schema validation for LLM function calling outputs by designing structured schemas that enforce JSON format constraints and enable automated correction flows for invalid responses.