memory-profiler-setup

Memory Profiler Setup - Auto-activating skill for Performance Testing. Triggers on: memory profiler setup, memory profiler setup Part of the Performance Testing skill category.

1.04x

Quality

Does it follow best practices?

Impact

90%

1.04x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/10-performance-testing/memory-profiler-setup/SKILL.md

Evaluation results

90%

Memory Usage Investigation for Python Data Pipeline

Production-ready Python memory profiling

Criteria

Without context

With context

Step-by-step structure

100%

Installation step included

100%

Profiler decorator or API usage

100%

Production-ready configuration

100%

Best practices comment or note

100%

Output interpretation guidance

100%

Validation step

80%

100%

No hardcoded secrets

100%

Tool compatibility note

37%

Benchmark or baseline measurement

87%

100%

Performance monitoring integration

30%

50%

Without context: $0.5301 · 2m 48s · 23 turns · 23 in / 7,912 out tokens

With context: $0.4909 · 2m 11s · 24 turns · 57 in / 7,484 out tokens

89%

Performance Testing Suite for a New REST API Service

k6 load and stress testing setup

Criteria

Without context

With context

k6 or JMeter used

100%

Load test scenario included

100%

Stress test scenario included

100%

Benchmarking element

100%

Performance monitoring integration

100%

Production-ready script structure

100%

Threshold/pass-fail criteria

100%

Step-by-step setup guide

100%

Validation or dry-run step

25%

Memory metric tracking

25%

37%

No hardcoded target URLs

100%

Without context: $0.4918 · 2m 11s · 21 turns · 22 in / 8,693 out tokens

With context: $0.6624 · 2m 49s · 31 turns · 63 in / 9,419 out tokens

91%

Memory Leak Investigation for Node.js Backend Service

Node.js memory profiling with validation

Criteria

Without context

With context

Step-by-step structure

100%

Installation/dependency step

100%

Profiler instrumentation

100%

Production-ready conditional profiling

70%

100%

Validation step

80%

Output interpretation

100%

Common standards adherence

100%

Cleanup or teardown step

50%

62%

Best practice warning or note

62%

100%

Performance monitoring tie-in

62%

50%

Benchmark or baseline measurement

100%

Without context: $0.5789 · 3m 8s · 20 turns · 21 in / 12,003 out tokens

With context: $0.6365 · 3m · 28 turns · 322 in / 9,816 out tokens

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 0c08951

Evaluated: 17 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Memory Usage Investigation for Python Data Pipeline Performance Testing Suite for a New REST API Service Memory Leak Investigation for Node.js Backend Service

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