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Turn debugging into a reusable knowledge graph for root-cause analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "debug-thinking" yet — see the docs or source repo.
Structure this investigation of a 500 API error into a knowledge graph: break down possible causes, design validation steps for each hypothesis, and record ruled-out and confirmed findings. Known symptoms: after deployment, the order API intermittently times out, and application logs show the database connection pool is exhausted.
A structured debugging graph with problem decomposition, hypotheses, validation steps, evidence, and root-cause conclusions.
Organize the last three similar cache avalanche incidents into a reusable knowledge graph. Summarize shared triggers, troubleshooting order, effective fixes, and prevention recommendations for faster future reuse.
A reusable incident knowledge entry showing common patterns, best troubleshooting paths, and recommended resolutions.
Build a debugging knowledge graph for this failed ETL job: create hypotheses across source data issues, schema changes, scheduler dependencies, and resource limits, provide validation methods, and mark the optimal next investigation path.
A debugging structure for data jobs that clearly shows hypothesis branches, validation priorities, and recommended actions.
Systematically reproduce, isolate, diagnose, and fix tricky software or environment issues.
Systematically triage failures and fix broken builds or unexpected runtime issues.
Create debug tests and iterate to reliably reproduce and diagnose tricky bugs.
Debug issues with a four-phase method that finds root causes before fixes.
Systematically investigate bugs before fixing them with a four-phase debugging framework.
Improve complex reasoning with multi-agent debate, bias detection, and structured thinking.