Error Diagnostics Smart Debug

This is a comprehensive debugging workflow that goes well beyond basic error handling. It orchestrates AI-powered root cause analysis with observability data from tools like Sentry, DataDog, and Honeycomb to generate ranked hypotheses about production issues. The workflow walks through triage, hypothesis testing, production-safe instrumentation, and fix validation with concrete strategies for different scenarios like N+1 queries or intermittent timeouts. It's built for teams dealing with complex distributed systems where you need to correlate traces, logs, and metrics to understand what's actually failing. The structured output format and prevention steps make it useful for building institutional knowledge, though you'll need actual access to those observability platforms to get full value from the data collection steps.

skills.sh installs: 356
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill error-diagnostics-smart-debug --agent claude-code

Installs into .claude/skills of the current project.

Files

Use this skill when

A reproducible failure or intermittent incident needs evidence-driven debugging.
Existing telemetry must be compared with source and deployment changes before patching.

Do not use this skill when

The task is unrelated to error diagnostics smart debug
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.

You are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.