Perform symptom-driven root-cause analysis for AI workflow failures.
npx skills add https://github.com/HypoxanthineOvO/Hypo-Workflow --skill debug-hypoxanthineovo
Please help me install this Agent Skill. Skill: debug Source: https://github.com/HypoxanthineOvO/Hypo-Workflow/tree/main/skills/debug Command: npx skills add https://github.com/HypoxanthineOvO/Hypo-Workflow --skill debug-hypoxanthineovo
Investigate a concrete failure by performing symptom-driven root-cause analysis rather than a preventive audit, enabling precise diagnosis and targeted remediation.
Start symptom-driven debugging by providing observed failure details and relevant context to initialize the workflow.
High-intent search queries and answers about installing and using this skill.
Root-cause analysis for AI workflow failures uses a structured five-step process: collecting symptoms, assembling context, generating hypotheses, verifying through experiments, and documenting results to ensure precise diagnosis and targeted remediation.
Symptom-driven debugging investigates concrete failures through multi-step hypothesis generation and experimentation. It is needed when preventive audits are insufficient for AI workflows, requiring precise diagnosis based on observed failure details and relevant context.
Start symptom-driven debugging by providing observed failure details and relevant context to initialize the workflow. This triggers the structured five-step process to generate hypotheses and verify them through experiments across worker-separated environments.
Yes, this debugging workflow manages worker separation modes to preserve isolation between reproduction, testing, and implementation tasks. It applies rigorous safety guards around actions, permissions, and evidence handling across these environments.
Diagnostic findings and decisions are traced by writing them to the Analysis ledger and debug logs. This ledgered reporting ensures full traceability for root-cause analysis and documents the outcomes of verified experiments.
Symptom-driven debugging should not be used when a preventive audit is sufficient to address AI workflow issues. It is specifically designed for active incident response scenarios requiring multi-step hypothesis generation rather than proactive checks.
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