Diagnose Auto-AI task failures by analyzing logs, traces, and runtime metrics.
npx skills add https://github.com/kardelitaitu/auto-ai --skill debug-kardelitaitu
Please help me install this Agent Skill. Skill: debug Source: https://github.com/kardelitaitu/auto-ai/tree/main/.agents/skills/debug Command: npx skills add https://github.com/kardelitaitu/auto-ai --skill debug-kardelitaitu
Debugging Auto-AI workflows is time-consuming and error-prone. This skill provides structured approaches to error investigation, log analysis, and stack-trace interpretation to accelerate root-cause identification.
Reproduce the issue, collect evidence, and initiate a guided debugging workflow to identify and verify the root cause.
High-intent search queries and answers about installing and using this skill.
To debug Auto-AI workflow failures, you can parse logs and break down stack traces to locate the root cause. This skill provides structured diagnostic workflows to collect evidence and identify the exact point of failure.
The best way to parse stack traces for browser automation errors is to use a structured debugging workflow that captures page state and runtime metrics. This isolates the root cause and provides repair guidance to prevent regression.
You diagnose performance issues and crashes in agent tasks by collecting runtime metrics and environment details for reproducibility. Filtering logs surfaces the relevant error data needed for accurate crash diagnosis.
Yes, you can use this skill to reproduce and fix errors in browser automation. It captures page state and environment details, then suggests validation steps and fixes to repair the workflow and prevent future regressions.
NTCodingInstrument software with logs and traces to debug runtime behavior.
fmflurryForm one hypothesis at a time from observable symptoms to identify root causes.
githubAutomate browser control and debugging via the Chrome DevTools MCP.
thesaifalitaiDiagnose and isolate software issue root causes through structured debugging workflows.
