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.
91
86%
Does it follow best practices?
Impact
99%
1.23xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that hits all the marks. It provides specific concrete actions, comprehensive trigger terms that users would naturally use, explicit 'Use when' guidance with multiple scenarios, and a distinct focus on function-level profiling that differentiates it from other performance or debugging skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Profile programs at the function/method level', 'identify performance hotspots, bottlenecks', 'Records execution time, memory usage, and call frequency', 'Generates actionable recommendations and visualizations'. | 3 / 3 |
Completeness | Clearly answers both what (profiling, recording metrics, generating recommendations) AND when with explicit 'Use when users need to...' clause covering 5 specific trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'performance', 'hotspots', 'bottlenecks', 'optimization', 'execution time', 'memory usage', 'profile', 'slow functions', 'Python, Java, C/C++', 'flame graphs', 'performance reports'. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on function-level profiling with distinct triggers like 'flame graphs', 'call frequency', 'profile Python/Java/C++'. Unlikely to conflict with general code review or debugging skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with strong actionability and good progressive disclosure. The main weaknesses are some verbosity in the guidelines/notes sections that explain concepts Claude already knows, and missing validation checkpoints in the workflow (e.g., verifying profiling data was captured correctly before generating visualizations).
Suggestions
Add a validation step after profiling (e.g., 'Verify profile_results.json exists and contains data before proceeding to visualization')
Trim the 'Optimization Guidelines' section - these are general principles Claude already knows; keep only project-specific guidance
Add a brief verification step after visualization generation to confirm the HTML report is valid before presenting to user
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient but includes some unnecessary explanation (e.g., 'Profile first, optimize second' and other optimization guidelines that Claude already knows). The troubleshooting section and some notes could be tightened. | 2 / 3 |
Actionability | Provides fully executable bash commands for all three languages, specific script paths, concrete examples with arguments explained, and clear output file names. Commands are copy-paste ready. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but lacks explicit validation checkpoints. No feedback loops for error recovery - if profiling fails or produces unexpected results, there's no 'verify and retry' guidance before proceeding. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview, well-organized sections, and appropriate references to external files (profiling-tools.md, optimization-patterns.md). References are one level deep and clearly signaled with descriptive links. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- ArabelaTso/Skills-4-SE
- Commit
0f00a4f
- Reviewed
Table of Contents
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