application-performance-performance-optimization
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
65
51%
Does it follow best practices?
Impact
89%
1.71xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-application-performance-performance-optimization/SKILL.mdQuality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description has good structural completeness with an explicit 'Use when' clause and covers the general domain well. However, it relies on broad category terms rather than specific concrete actions, and its trigger terms lack the natural language variations users would employ when experiencing performance issues. The description would benefit from more specific actions and richer trigger vocabulary.
Suggestions
Add specific concrete actions such as 'profile CPU/memory usage, analyze flame graphs, configure distributed tracing, optimize database queries, reduce bundle size, implement caching strategies'
Expand trigger terms to include natural user language like 'slow,' 'latency,' 'bottleneck,' 'load time,' 'memory leak,' 'response time,' 'page speed,' 'throughput'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (performance optimization) and mentions some areas like 'profiling, observability, and backend/frontend tuning,' but these are broad categories rather than concrete specific actions like 'run flame graphs,' 'set up distributed tracing,' or 'optimize database queries.' | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize end-to-end application performance with profiling, observability, and backend/frontend tuning) and 'when' (use when coordinating performance optimization across the stack), with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'performance,' 'profiling,' 'observability,' and 'tuning,' but misses many natural terms users would say such as 'slow,' 'latency,' 'bottleneck,' 'load time,' 'memory leak,' 'CPU usage,' 'caching,' 'response time,' or 'APM.' | 2 / 3 |
Distinctiveness Conflict Risk | The 'across the stack' framing provides some distinctiveness, but terms like 'performance optimization,' 'backend tuning,' and 'frontend tuning' could easily overlap with more specific backend-only or frontend-only performance skills, or general code optimization skills. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a comprehensive but overly verbose orchestration guide for performance optimization. While the phased structure and success criteria are valuable, the content is bloated with explanations of concepts Claude already understands, and the subagent prompt templates are excessively detailed. The workflow lacks explicit validation checkpoints between phases and feedback loops for when optimizations fail or degrade performance.
Suggestions
Reduce each step to a concise description of the goal, key inputs, and expected outputs—remove explanatory content about standard tools and concepts (e.g., don't explain what Core Web Vitals are or how Redis caching works).
Add explicit validation checkpoints after each phase (e.g., 'Compare metrics against Phase 1 baselines before proceeding; if regression detected, rollback and investigate').
Extract the detailed subagent prompt templates into a separate reference file (e.g., PROMPTS.md) and keep SKILL.md as a lean overview of the workflow phases.
Remove the extended thinking block entirely—it adds no actionable value and wastes tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~150+ lines with extensive explanations Claude already knows (what Core Web Vitals are, what APM tools do, how caching works). The extended thinking block adds unnecessary meta-commentary. Each step includes lengthy prompt templates that over-explain standard performance engineering concepts. | 1 / 3 |
Actionability | The skill provides structured prompts for subagents with specific tool references (k6, Gatling, DataDog), but contains no executable code or commands—everything is delegated via prompt templates with placeholder variables like {context_from_phase_1}. The guidance is specific in intent but not directly executable. | 2 / 3 |
Workflow Clarity | The 5-phase, 13-step sequence is clearly laid out with logical progression and dependencies between phases. However, there are no explicit validation checkpoints or feedback loops—step 10 (load testing) comes late with no intermediate validation, and there's no guidance on what to do if optimizations degrade performance mid-workflow. The safety section mentions rollback plans but doesn't integrate them into the workflow steps. | 2 / 3 |
Progressive Disclosure | Content is organized into phases with clear headers, but it's a monolithic wall of text with no references to external files. The detailed prompt templates for each of 13 steps could be split into separate reference files, with SKILL.md serving as a concise overview. Configuration options and success criteria are useful but add to the length. | 2 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- boisenoise/skills-collections
- Commit
f1697b6
- Reviewed
Table of Contents
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