error-recovery

Categorize errors and recover using memory-informed strategies with escalation paths.

6|3|Updated Jan 2, 2026
One-click install
npx skills add https://github.com/mgd34msu/goodvibes-plugin --skill error-recovery-mgd34msu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: error-recovery
Source: https://github.com/mgd34msu/goodvibes-plugin/tree/main/plugins/goodvibes/skills/protocol/error-recovery
Command: npx skills add https://github.com/mgd34msu/goodvibes-plugin --skill error-recovery-mgd34msu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a robust framework for agents to systematically diagnose, recover from, and log errors encountered during task execution, ensuring reliability and maintainability.

Core Features & Use Cases

  • Categorization: Classifies errors into distinct types (TOOL_FAILURE, BUILD_ERROR, etc.) for targeted handling.
  • Memory-Informed Recovery: Leverages past failure logs (failures.json) to apply known resolutions.
  • Multi-Source Strategy: Consults internal knowledge, documentation, community, and web resources for solutions.
  • Logging: Records resolutions and unresolvable issues for future reference and analysis.
  • Escalation: Defines clear criteria and procedures for escalating unresolvable issues to an orchestrator.
  • Use Case: When a precision tool fails unexpectedly, this skill guides the agent to categorize the failure, check failures.json, attempt a fix based on documented patterns, and log the outcome, preventing blind retries and improving overall task success rates.

Quick Start

Use the error-recovery skill to systematically diagnose and resolve any encountered tool failures.

Frequently Asked Questions about error-recovery

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

FAQPage Schema
How do I systematically handle agent task execution errors and prevent blind retries?

To handle agent task execution errors systematically, you can use a protocol that categorizes failures, checks memory-informed logs for known resolutions, and applies multi-source recovery strategies before logging outcomes or escalating unresolvable issues.

What is the best way to recover from tool failures and build errors during automated tasks?

The best way to recover from tool failures and build errors is to classify the error type, consult documented patterns or community resources for a fix, and record the resolution in a failures log to prevent future blind retries.

How does memory-informed error recovery work for runtime and test failures?

Memory-informed error recovery works by leveraging a historical failure log to apply previously documented resolutions to current runtime and test failures, ensuring agents reference past outcomes before attempting new fixes.

When should I escalate unresolvable errors to an orchestrator instead of retrying?

You should escalate unresolvable errors to an orchestrator only after systematically exhausting internal knowledge, documentation, community resources, and web searches, meeting clear criteria defined by the recovery protocol.

Can I use this error handling protocol for external service errors and type errors?

Yes, you can use this error handling protocol for external service errors and type errors, as it specifically addresses these categories alongside tool failures, build errors, test failures, and runtime errors.

Why does logging unresolvable issues improve task success rates over time?

Logging unresolvable issues improves task success rates by creating a persistent record of failures and escalation paths, enabling memory-informed diagnostics to apply known resolutions to similar future errors automatically.