provider-openai-proxy-user-corrections-2

Extract recurring user corrections into reusable rules for OpenCode workflows.

171|10|Updated Feb 20, 2026

Search Tags:#guidance #patterns #workflows #continuous-learning #rule-extraction #user-corrections

Ready to run

npx skills add https://github.com/fmflurry/settings-opencode --skill provider-openai-proxy-user-corrections-2

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: provider-openai-proxy-user-corrections-2
Source: https://github.com/fmflurry/settings-opencode/tree/main/.claude/skills/provider-openai-proxy-user-corrections-2
Command: npx skills add https://github.com/fmflurry/settings-opencode --skill provider-openai-proxy-user-corrections-2

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Recurring user corrections slow progress and create inconsistencies across tasks. This pattern provides a clear mechanism to capture corrections as reusable rules.

Core Features & Use Cases

Extract corrections as concrete rules to apply to current work and related decisions.
Apply rules across ongoing tasks to ensure consistency and reduce rework.
Revalidate outputs after applying corrections and store guidance for future reuse.

Quick Start

Convert a recurring user correction into a concrete rule and apply it to ongoing work.

Frequently Asked Questions about provider-openai-proxy-user-corrections-2

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

FAQPage Schema

How do I turn recurring user corrections into reusable rules in OpenCode workflows?▼

To turn recurring user corrections into reusable rules, you capture repetitive feedback across agent decisions and prompts, extract it as explicit rules, and store the guidance for future reuse to ensure consistency.

What is the best way to stop repetitive user feedback from causing inconsistencies across tasks?▼

Stop repetitive feedback inconsistencies by extracting user corrections as concrete rules, propagating them across ongoing tasks, and revalidating outputs against the stored guidance to reduce rework.

How does rule extraction from user corrections work for continuous learning?▼

Rule extraction works by capturing recurring user corrections as explicit policies, applying them to current work, revalidating the outputs, and storing the guidance to drive continuous learning across workflows.

Can I apply extracted correction rules to ongoing agent decisions automatically?▼

Yes, you can apply extracted correction rules automatically to ongoing agent decisions and prompts, ensuring that current work and related outputs are revalidated for consistency without manual intervention.

Do I need any dependencies to automate extracting corrections as rules?▼

No dependencies are required to automate extracting corrections as rules, as the pattern operates natively within OpenCode workflows to capture feedback, propagate rules, and store guidance for reuse.

When should I not use a continuous-learning rule extraction pattern for workflow corrections?▼

Avoid using continuous-learning rule extraction for one-off, non-recurring feedback, as the overhead of extracting, propagating, and storing guidance only pays off when correcting repetitive issues across multiple tasks.