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.
60
51%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agent/skills/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 structure with explicit 'what' and 'when' clauses, earning full marks for completeness. However, it relies on somewhat abstract terminology (profiling, observability, tuning) rather than concrete actions, and lacks the natural trigger terms users would actually say when experiencing performance issues.
Suggestions
Add concrete actions like 'analyze flame graphs, reduce bundle size, optimize database queries, identify memory leaks' to improve specificity.
Include natural user trigger terms like 'slow', 'latency', 'bottleneck', 'speed up', 'loading time' that users would actually say when they have performance problems.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (performance optimization) and mentions some actions (profiling, observability, backend/frontend tuning), but these are high-level categories rather than concrete specific actions like 'analyze flame graphs' or 'reduce bundle size'. | 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 trigger clause. | 3 / 3 |
Trigger Term Quality | Includes some relevant terms like 'performance optimization', 'profiling', 'observability', but misses common natural variations users might say like 'slow', 'latency', 'bottleneck', 'speed up', 'optimize queries', or 'memory leak'. | 2 / 3 |
Distinctiveness Conflict Risk | The 'across the stack' qualifier helps distinguish from single-layer optimization skills, but terms like 'profiling' and 'observability' could overlap with more specialized monitoring or debugging 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 functions primarily as a navigation hub to 13 sub-skills rather than providing actionable guidance itself. While the structure and organization are reasonable, the main file lacks any concrete examples, executable code, or specific techniques—everything is deferred to linked files. The extended thinking block and verbose section headers consume tokens without adding value.
Suggestions
Add at least one concrete, executable example in the main skill (e.g., a quick profiling command or a baseline measurement script) before linking to sub-skills
Remove the extended thinking block or convert it to a brief 1-2 sentence rationale
Add explicit validation checkpoints to the 4-step instructions (e.g., 'Baseline established when: p50/p95/p99 latencies documented for top 10 endpoints')
Include a minimal quick-start section with one actionable technique that doesn't require reading sub-skills
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The extended thinking block adds unnecessary verbosity explaining the workflow philosophy. The 'Use this skill when' and 'Do not use this skill when' sections are reasonable but could be tighter. The numbered list of 13 sub-skills with redundant numbering (both in list and link text) adds bloat. | 2 / 3 |
Actionability | The skill provides no concrete code, commands, or executable guidance. The 4-step instructions are abstract ('Establish baselines with profiling') without specifying how. All actual content is deferred to 13 sub-skill files with no inline examples or copy-paste ready guidance. | 1 / 3 |
Workflow Clarity | There is a clear 4-phase structure and the sub-skills are sequenced logically. However, there are no validation checkpoints, no feedback loops for error recovery, and no explicit criteria for when to proceed between phases. The safety section mentions rollback plans but doesn't specify how to implement them. | 2 / 3 |
Progressive Disclosure | The skill appropriately links to 13 sub-skill files for detailed content, which is good structure. However, the overview provides almost no substantive content itself—it's essentially just a table of contents. The sub-skill links are one level deep but there's no quick-start content or minimal working example before diving into references. | 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
- Dokhacgiakhoa/antigravity-ide
- Commit
332e58b
- Reviewed
Table of Contents
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