agency-prompt-engineer

Design, test, and optimize system prompts for large language models.

Updated Jul 23, 2026

Search Tags:#automation #testing #version control #prompt engineering #ai development #llm

Ready to run

npx skills add https://github.com/rajyeole6/AI-RECRUITER --skill agency-prompt-engineer-rajyeole6

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agency-prompt-engineer
Source: https://github.com/rajyeole6/AI-RECRUITER/tree/main/.agents/skills/engineering-prompt-engineer
Command: npx skills add https://github.com/rajyeole6/AI-RECRUITER --skill agency-prompt-engineer-rajyeole6

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest.

What problem does it solve?

This Skill solves the challenge of inconsistent and unreliable LLM behavior by providing a rigorous, scientific framework for prompt design, testing, and version control.

Core Features & Use Cases

Structured Prompt Design: Utilizes a proven Role-Constraints-Reasoning-Examples template to ensure predictable model outputs.
Automated Regression Testing: Includes a test suite template to catch regressions and validate prompt performance across different model versions.
Use Case: A developer needs to ensure a classification prompt consistently returns valid JSON; this Skill provides the tools to define the schema, build few-shot examples, and run automated tests to verify compliance.

Quick Start

Use the agency-prompt-engineer skill to draft a system prompt for a customer support bot that follows the role-constraints-reasoning-examples structure.

Frequently Asked Questions about agency-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 JSON?▼

To design LLM prompts that consistently return valid JSON, apply a structured Role-Constraints-Reasoning-Examples template to enforce predictable model outputs. You define the schema, build few-shot examples, and run automated tests to validate compliance.

What is prompt regression testing and when do I need it for AI development?▼

Prompt regression testing is the process of validating prompt performance across different LLM versions to catch behavioral regressions. You need it during production-grade AI development to ensure outputs remain predictable and high-quality when updating models.

How do I structure a system prompt for multi-model instruction tuning?▼

Structure a system prompt for multi-model instruction tuning by applying a Role-Constraints-Reasoning-Examples methodology. This framework ensures predictable outputs and consistent behavior across different large language models during optimization.

Can I use pytest to automate LLM prompt testing?▼

Yes, you can use pytest to automate LLM prompt testing. This skill includes an automated test suite template that leverages pytest to run regression tests, validating prompt behavior and catching performance regressions across model versions.

What is the best way to optimize large language model prompts for production?▼

The best way to optimize large language model prompts for production is applying a scientific framework for prompt design, testing, and version control. This methodology utilizes structured templates, few-shot example generation, and chain-of-thought reasoning scaffolds.

Why does my LLM prompt behave inconsistently across different model versions?▼

LLM prompts behave inconsistently across versions due to a lack of structured constraints and regression testing. Without a rigorous prompt design methodology using Role-Constraints-Reasoning-Examples templates, model updates introduce unpredictable behavioral changes.