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

89%

Does it follow best practices?

Impact

99%

1.23x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, actionable skill body with executable examples across languages and clean progressive disclosure to real reference files. The main improvement area is tightening generic optimization advice and adding an explicit results-validation step.

Suggestions

Trim the 'Optimization Guidelines' section, which restates principles Claude already knows, or move it into references/optimization-patterns.md.

Add an explicit validation checkpoint in the workflow (e.g., after running profiling, verify profile_results.json contains a non-empty hotspots list before generating visualizations).

Consider noting prerequisite checks (JDK 11+ for Java, perf availability for C/C++) at the start of step 3 rather than only in Troubleshooting.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean and action-oriented with executable commands, but the 'Optimization Guidelines' section restates general principles Claude already knows (e.g., 'Algorithm > micro-optimizations: O(n²) → O(n log n) beats loop tweaks') and could be trimmed.	4 / 5
Actionability	Provides copy-paste-ready commands for all three languages with documented arguments ('python scripts/profile_java.py MainClass ./bin 30'), plus a concrete visualization command, covering the common cases fully.	5 / 5
Workflow Clarity	A clear six-step sequence with a troubleshooting section for error recovery, but the main workflow lacks an explicit validation checkpoint (e.g., verify profile_results.json contains hotspots before analyzing); operations are non-destructive so the 3-cap does not apply.	4 / 5
Progressive Disclosure	SKILL.md serves as a clear overview with well-signaled one-level-deep references to references/profiling-tools.md and references/optimization-patterns.md (both real files), with detailed content appropriately split out and scripts organized under scripts/.	5 / 5
	Total	18 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that concretely states capabilities and provides explicit numbered use-when triggers. It is comprehensive and distinct, with only minor room to add synonymous trigger phrasing.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Profile programs at the function/method level', 'Records execution time, memory usage, and call frequency', 'Generates actionable recommendations and visualizations' — with comprehensive coverage, matching the top anchor.	5 / 5
Completeness	Clearly states what it does in the opening sentences and gives an explicit 'Use when users need to (1)...(5)...' trigger clause with concrete scenarios, satisfying the top anchor for both what and when.	5 / 5
Trigger Term Quality	Strong natural phrases users would say ('analyze program performance', 'identify slow functions or bottlenecks', 'profile Python, Java, or C/C++ programs', 'flame graphs'), but lacks synonyms or extension-based variants that would push it to a 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (function/method-level profiling with hotspots, memory, and flame graphs) with distinct triggers and minimal overlap with unrelated skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: ArabelaTso/Skills-4-SE
Path: skills/interval-profiling-performance-analyzer/SKILL.md
Commit: 4f38503

Reviewed: 5 days ago

Table of Contents

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