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handling-corrections

Identify root causes of user-corrected actions and draft a rectification plan.

1|Updated Feb 24, 2026
Search Tags:#verification#root-cause#user-feedback#corrections#anti-thrashing#debris-cleanup#conversation-safety
One-click install
npx skills add https://github.com/dzackgarza/ai --skill handling-corrections
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: handling-corrections
Source: https://github.com/dzackgarza/ai/tree/main/opencode/skills/handling-corrections
Command: npx skills add https://github.com/dzackgarza/ai --skill handling-corrections

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps stabilize AI interactions when users correct errors, challenge actions, or ask "why", preventing impulsive pivots and guiding debris cleanup and root-cause analysis.

Core Features & Use Cases

  • Anti-thrashing protocol: pause, analyze, and plan before acting.
  • Debris cleanup: identify changes, assess damage, and verify outcomes after errors.
  • Root-cause analysis & user verification: diagnose cognitive failures and confirm corrective steps with the user.
  • Research-oriented handling for "why" questions: gather evidence before acting.

Quick Start

Tell me the correction or question, and I will identify the root cause, assess changes, and propose a plan before taking any action.

Frequently Asked Questions about handling-corrections

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

FAQPage Schema
How do I handle user corrections without causing the AI to thrash or pivot impulsively?▼

To handle user corrections safely, an anti-thrashing protocol pauses the AI to identify the cognitive failure, assess damage, and draft a rectification plan before executing any changes.

What is the best way to perform root-cause analysis when an AI makes an error in documentation or code?▼

Root-cause analysis for AI errors involves identifying the cognitive failure, assessing damage, checking for collateral effects, drafting a rectification plan, and verifying the outcome with the user.

How do I clean up collateral debris after an AI makes incorrect changes to a conversation or codebase?▼

Debris cleanup after AI errors requires assessing the damage, checking for collateral effects across code and documentation, and verifying the rectification outcomes with the user before finalizing.

Can I use a structured process to safely respond when a user questions why an AI took a specific action?▼

Yes, handling user "why" questions uses a research-oriented process that gathers evidence before acting, identifying the root cause and proposing a verified plan rather than responding impulsively.

What steps should I follow to verify corrective actions with a user after an AI error?▼

Verifying corrective actions requires identifying the cognitive failure, assessing damage, checking collateral effects, drafting a rectification plan, and confirming the execution plan with the user.

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