yes

Enforce safety, validation, and evidence-based protocols for AI-driven tasks.

10|3|Updated Jun 3, 2023

Search Tags:#code review #prompt engineering #validation #risk management #guardrails #ai safety

Ready to run

npx skills add https://github.com/j7-dev/wp-power-shop --skill yes-j7-dev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: yes
Source: https://github.com/j7-dev/wp-power-shop/tree/main/.claude/skills/yes
Command: npx skills add https://github.com/j7-dev/wp-power-shop --skill yes-j7-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill acts as a safety and validation layer for AI actions, preventing errors, malicious behavior, and ensuring all AI-generated outputs are evidence-based and verified before execution.

Core Features & Use Cases

Evidence-Based Reasoning: Enforces that all AI claims and actions are backed by verifiable data, preventing guesswork.
Safety Guardrails: Implements checks before modifications, deployments, or conclusions to prevent unintended consequences.
Use Case: When an AI needs to modify a configuration file, this Skill will first ensure a backup is made, check the impact scope, and then require verification of the change before confirming completion.

Quick Start

Use the yes skill to ensure the AI follows all safety and validation protocols before making any changes.

Frequently Asked Questions about yes

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

FAQPage Schema

How do I prevent prompt injection and malicious code execution during AI-driven tasks?▼

To prevent prompt injection and malicious code execution, you need structured safety guardrails that enforce evidence-based reasoning and verification steps before any AI action is executed. This involves implementing decision-making gates that validate impact scope and require backups before modifications.

What are AI safety guardrails and how do they validate AI-generated outputs?▼

AI safety guardrails are validation protocols that ensure AI-generated outputs are evidence-based and verified before execution. They work by guiding AI behavior through structured decision-making gates, performing impact analysis, and requiring verifiable data to back all AI claims and actions.

How do I ensure AI makes verified changes to configuration files without unintended consequences?▼

To ensure verified configuration file changes without unintended consequences, enforce a safety protocol that creates a backup, checks the impact scope, and requires verification of the change before confirming completion. This prevents errors and malicious behavior in AI-driven tasks.

Can I use a meta-skill to govern other AI skills for reliable task completion?▼

Yes, you can use a meta-skill to govern other AI skills for reliable task completion. It operates as a safety and validation layer, enforcing rigorous protocols, preventing data exfiltration, and ensuring all downstream AI skills follow evidence-based reasoning and structured decision-making gates.

How do impact analysis and structured decision-making gates improve AI risk management?▼

Impact analysis and structured decision-making gates improve AI risk management by enforcing checks before modifications, deployments, or conclusions. This prevents unintended consequences, stops data exfiltration, and ensures every AI action is backed by verifiable data rather than guesswork.