performance-optimization
Profiles and reduces frontend/backend costs: split bundles, optimize assets, apply caching, and fix Core Web Vitals regressions. Use when profiling Lighthouse/CI regressions, reducing bundle size, or fixing high CLS/LCP/TTI metrics.
100
100%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
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 a strong skill description that clearly defines concrete actions (bundle splitting, asset optimization, caching, Core Web Vitals fixes), includes an explicit 'Use when' clause with natural trigger terms, and occupies a distinct niche around frontend/backend performance profiling. The description is concise, uses third person voice, and provides enough specificity for Claude to confidently select it from a large skill set.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: split bundles, optimize assets, apply caching, fix Core Web Vitals regressions, profile Lighthouse/CI regressions, reduce bundle size, fix high CLS/LCP/TTI metrics. | 3 / 3 |
Completeness | Clearly answers both what ('profiles and reduces frontend/backend costs: split bundles, optimize assets, apply caching, fix Core Web Vitals regressions') and when ('Use when profiling Lighthouse/CI regressions, reducing bundle size, or fixing high CLS/LCP/TTI metrics'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'bundle size', 'Lighthouse', 'CLS', 'LCP', 'TTI', 'Core Web Vitals', 'caching', 'CI regressions'. These cover common terms developers use when discussing performance optimization. | 3 / 3 |
Distinctiveness Conflict Risk | Targets a clear niche of web performance optimization with distinct triggers like Lighthouse, Core Web Vitals, CLS/LCP/TTI, and bundle splitting that are unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a high-quality skill that balances breadth and conciseness effectively. It provides actionable, executable examples across multiple domains while maintaining a clear profiling workflow with validation checkpoints. The progressive disclosure to REFERENCE.md and external links keeps the main file focused and navigable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It uses a table for domain patterns instead of verbose paragraphs, avoids explaining what tools like Lighthouse or React.memo are, and defers detailed domain-specific patterns to REFERENCE.md. Every section earns its place. | 3 / 3 |
Actionability | Provides fully executable code examples (debounce, dynamic import, React.memo with Profiler), specific tool names and commands, and concrete patterns. The examples are copy-paste ready and cover both frontend and backend scenarios. | 3 / 3 |
Workflow Clarity | The profiling workflow has clear numbered steps with explicit checkpoints at each stage, recovery actions (e.g., reproduce locally if noisy), iteration loops (step 5), and a rollback plan. The review checklist adds a validation layer for completeness. | 3 / 3 |
Progressive Disclosure | The skill explicitly references REFERENCE.md for domain-specific patterns, keeps the main file as a concise overview with key patterns and examples, and includes external reference links. Content is well-structured with clear section headers for easy navigation. | 3 / 3 |
Total | 12 / 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
- monkilabs/opencastle
- Commit
f5c8508
- Reviewed
Table of Contents
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