debugging-strategies

Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

0.98x

Quality

47%

Does it follow best practices?

Impact

84%

0.98x

Average score across 6 eval scenarios

Securityby

Passed

No known issues

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Evaluation results

92%

-8%

Slow Report Aggregation Investigation

Python performance profiling

Criteria

Without context

With context

cProfile used

100%

80%

pstats Stats used

100%

75%

sort_stats cumulative

100%

75%

print_stats called

100%

75%

Profile output included

100%

Measurement before fix

100%

Measurement after fix

100%

Root cause identified

100%

Root cause fixed

100%

Findings documented

100%

98%

Order Processing Service Bug

Systematic bug investigation

Criteria

Without context

With context

Observations documented

100%

Hypotheses stated

60%

100%

Experiment described

90%

100%

Analysis included

100%

Root cause identified

100%

Root cause fixed

100%

Single targeted change

75%

No debug artifacts

100%

Information gathered

100%

Findings documented

100%

94%

Payment Checkout Failure Investigation

Production incident investigation

Criteria

Without context

With context

No production changes

100%

Evidence gathering first

100%

Staging before prod

100%

Differential table

33%

66%

Recent changes checked

100%

Hypotheses formed

100%

Failure modes covered

100%

Local reproduction steps

87%

75%

git bisect commands

100%

Bisect test described

100%

92%

Memory Investigation: Long-Running Node.js Service

Node.js memory leak detection

Criteria

Without context

With context

process.memoryUsage used

100%

heapUsed accessed

100%

v8.writeHeapSnapshot used

100%

Threshold check present

100%

Before/after memory tracking

100%

10MB threshold in tests

Root cause identified

100%

Root cause fixed

100%

Measurement baseline included

100%

No debug artifacts in fixed code

100%

93%

-1%

Flaky Checkout Counter: Intermittent Inventory Bug

Intermittent bug and race condition investigation

Criteria

Without context

With context

Timing logged

50%

25%

State transitions logged

100%

External interactions logged

87%

Race condition identified

100%

Missing synchronization noted

90%

100%

Stress test implemented

100%

Concurrent execution in stress test

100%

Root cause fixed

100%

Hypotheses stated

100%

No debug artifacts in fix

100%

Async timing noted

100%

40%

-4%

Slow and Wrong: Debugging a Data Transformation Pipeline

JavaScript debugging API and trace instrumentation

Criteria

Without context

With context

console.table used

console.time/timeEnd used

console.trace used

console.assert used

performance.mark used

performance.measure used

performance.getEntriesByType used

Trace wrapper implemented

100%

60%

Root cause identified

100%

No debug artifacts in fixed code

100%

Single targeted fix

100%

Repository: wshobson/agents
Commit: 70444e5

Evaluated: 29 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Slow Report Aggregation Investigation Order Processing Service Bug Payment Checkout Failure Investigation Memory Investigation: Long-Running Node.js Service Flaky Checkout Counter: Intermittent Inventory Bug Slow and Wrong: Debugging a Data Transformation Pipeline

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