genie-proof-prompts

Name: genie-proof-prompts
Availability: InStock
Author: theycallmeholla

Rewrite ambiguous prompts into explicit, loophole-free instructions.

Updated Jul 23, 2026

Search Tags:#prompt engineering #specifications #optimization #llm #clarity #hardening

Ready to run

npx skills add https://github.com/Theycallmeholla/skills --skill genie-proof-prompts

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: genie-proof-prompts
Source: https://github.com/Theycallmeholla/skills/tree/main/skills/genie-proof-prompts
Command: npx skills add https://github.com/Theycallmeholla/skills --skill genie-proof-prompts

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates ambiguity and "monkey's paw" outcomes in AI interactions by transforming vague instructions into precise, bulletproof specifications that prevent LLMs from misinterpreting intent.

Core Features & Use Cases

Loophole Audit: Systematically identifies and closes vulnerabilities in prompts related to scope, format, and failure behavior.
Adversarial Rewriting: Adopts a malicious genie perspective to ensure instructions are literal, explicit, and resistant to misinterpretation.
Use Case: Use this when you need to hand off a complex task to a junior developer or an AI agent and want to ensure the output is exactly what you expect, with no room for "creative" or incorrect interpretations.

Quick Start

Use the genie-proof-prompts skill to rewrite my current prompt about the project requirements to ensure it is completely unambiguous and bulletproof.

Frequently Asked Questions about genie-proof-prompts

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

FAQPage Schema

How do I make prompt engineering instructions loophole-free for AI agents?▼

Prompt hardening prevents monkey's paw outcomes by adopting an adversarial perspective to ensure instructions are literal and resistant to misinterpretation. It is needed when handing off complex project specifications where exact output is required.

How do I audit a system prompt for scope and failure behavior vulnerabilities?▼

Auditing a system prompt for vulnerabilities involves identifying ambiguities related to scope and format, then systematically closing loopholes to define explicit failure behavior. This ensures high-stakes instructions are executed literally.

What is the best way to specify verifiable success criteria in LLM prompts?▼

The best way to specify verifiable success criteria in LLM prompts is through adversarial rewriting, transforming vague requirements into bulletproof specifications that leave no room for creative or incorrect interpretations by the model.

Can I use prompt hardening for complex project specifications handed to junior developers?▼

Yes, you can use prompt hardening for complex project specifications handed to junior developers. Rewriting instructions to be completely unambiguous ensures the output matches expectations without relying on subjective interpretation.

Why does my LLM output deviate from the intended scope of the prompt?▼

Your LLM output deviates from the intended scope due to ambiguous instructions lacking defined boundaries. Applying loophole audits and explicit failure behavior constraints prevents the model from misinterpreting your intent.