interval-profiling-performance-analyzer

Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.

1.23x

Quality

86%

Does it follow best practices?

Impact

99%

1.23x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Evaluation results

98%

27%

Slow Data Pipeline Investigation

Python profiling workflow with visualization

Criteria

Without context

With context

Uses profile_python.py

100%

Produces profile_results.json

100%

Runs generate_visualization.py

100%

Produces profile_report.html

100%

Reports top hotspots with % impact

100%

83%

Identifies compute_discount as hotspot

100%

Specific optimization suggestions

100%

Quick win identified

50%

100%

Profile-first approach

100%

80/20 focus on hotspots

90%

100%

Verify Text Processing Optimization

Before/after optimization comparison workflow

Criteria

Without context

With context

Uses profile_python.py for original

80%

100%

Saves original results as before.json

100%

Uses profile_python.py for refactored

80%

100%

Saves refactored results as after.json

100%

Compares hotspots across versions

100%

Quantifies total execution time change

100%

Reports % of total time per hotspot

60%

100%

Addresses string-building claim

100%

Measurement-based conclusions

100%

23%

Memory Usage Investigation for Image Batch Processor

Memory leak investigation and recommendations

Criteria

Without context

With context

Uses profile_python.py

100%

Produces profile_results.json

40%

100%

References memory_usage section

58%

100%

Identifies top memory locations

100%

Root cause explanation

100%

Recommends generators

100%

Recommends object pooling or cleanup

100%

Two or more concrete recommendations

100%

Measurement-driven analysis

100%

Does NOT use alternative memory profilers

100%

Repository: ArabelaTso/Skills-4-SE
Commit: 0f00a4f

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

Table of Contents

Slow Data Pipeline Investigation Verify Text Processing Optimization Memory Usage Investigation for Image Batch Processor

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