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agency-prompt-engineer

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

Updated Jul 24, 2026
Search Tags:#automation#testing#version control#prompt engineering#ai development#llm
One-click install
npx skills add https://github.com/imMamdouhaboammar/kaku-chatgpt-harness --skill agency-prompt-engineer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agency-prompt-engineer
Source: https://github.com/imMamdouhaboammar/kaku-chatgpt-harness/tree/main/.agents/skills/engineering-prompt-engineer
Command: npx skills add https://github.com/imMamdouhaboammar/kaku-chatgpt-harness --skill agency-prompt-engineer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest.

What problem does it solve?

This Skill addresses the inconsistency and unreliability of LLM outputs 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 minimize ambiguity.
  • Automated Regression Testing: Includes a Python-based test suite to validate prompt behavior against happy paths, edge cases, and adversarial inputs.
  • Use Case: When deploying a new customer support bot, use this Skill to define the persona, establish strict guardrails against off-topic queries, and run a test suite to ensure the model consistently adheres to the required JSON output format.

Quick Start

Use the agency-prompt-engineer skill to draft a system prompt for a technical support agent and generate three test cases for edge-case handling.

Frequently Asked Questions about agency-prompt-engineer

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

FAQPage Schema
How do I test LLM prompts for edge cases and adversarial inputs?▼

Systematic prompt engineering uses a Role-Constraints-Reasoning-Examples template to minimize ambiguity and establish strict guardrails. This structured approach enforces predictable model behavior and consistent JSON output formatting.

How do I apply version control to system prompts for production AI agents?▼

Applying version control to system prompts requires treating prompt templates as code dependencies. You track structural changes to the Role-Constraints-Reasoning-Examples templates and run regression tests to maintain output reliability across versions.

Do I need pytest to run automated regression tests on my LLM prompts?▼

Yes, pytest is required as the core dependency to execute the Python-based test suite. It validates your prompt engineering logic against defined edge cases and adversarial inputs to ensure predictable behavioral outputs.

What is the best way to ensure my customer support bot outputs consistent JSON?▼

The best way to ensure consistent JSON is using a structured prompt engineering template that establishes strict guardrails. You then validate this format by running automated regression tests against edge cases before deploying the AI agent.

Why does my LLM prompt break when handling off-topic queries in production agents?▼

LLM prompts break when they lack strict constraints and structured reasoning templates. Without systematic prompt engineering and regression testing against adversarial inputs, the model fails to adhere to required formats and guardrails.

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