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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.

43

Quality

43%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.agent/skills/application-performance-performance-optimization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

20%

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

This skill is essentially a table of contents with 13 links to sub-skills and almost no actionable content of its own. The instructions are vague abstractions ('establish baselines', 'execute phased optimizations') with no concrete guidance, commands, or examples. The extended thinking block wastes tokens explaining the workflow philosophy rather than providing executable steps.

Suggestions

Replace the abstract 4-step instructions with concrete, actionable guidance—e.g., specific profiling commands, tool recommendations with example invocations, or a decision tree for identifying bottleneck types.

Remove the extended thinking block entirely; it provides no actionable value and wastes significant token budget.

Add at least one concrete example workflow showing a real optimization scenario end-to-end (e.g., 'Profile with `py-spy top --pid <PID>`, identify hot function, optimize, validate with load test').

Group the 13 sub-skills into logical phases (Profiling, Optimization, Validation, Monitoring) with brief descriptions of when each applies, rather than presenting a flat numbered list.

DimensionReasoningScore

Conciseness

The extended thinking block is unnecessary filler that explains concepts Claude already understands. The 'Use this skill when' and 'Do not use this skill when' sections add bulk without actionable value. The numbered sub-skill list is mostly a table of contents with no substantive content in the skill itself.

1 / 3

Actionability

The instructions are entirely abstract ('Confirm performance goals', 'Establish baselines', 'Execute phased optimizations') with no concrete code, commands, tools, or specific techniques. Everything actionable is deferred to 13 sub-skill files, leaving the main skill as a vague outline.

1 / 3

Workflow Clarity

There is a 4-step high-level sequence in the Instructions section and the sub-skills are numbered suggesting an order, but there are no validation checkpoints, no feedback loops, no explicit criteria for moving between phases, and no error recovery guidance.

2 / 3

Progressive Disclosure

The skill does reference 13 sub-skill files with clear paths, which is good progressive disclosure structure. However, no bundle files were provided to verify these references exist, the overview content is too thin to stand alone, and the sub-skills are not grouped or signaled by phase/category—they're just a flat numbered list with redundant heading formatting.

2 / 3

Total

6

/

12

Passed

Description

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 listing specific concrete actions, and it lacks the natural trigger terms users would actually use when experiencing performance issues (e.g., 'slow page load,' 'high latency,' 'memory leak'). The cross-stack framing provides some distinctiveness but could still overlap with more specialized performance skills.

Suggestions

Add specific concrete actions such as 'analyze flame graphs, set up distributed tracing, optimize database queries, reduce bundle size, configure caching strategies' to improve specificity.

Include natural user trigger terms like 'slow,' 'latency,' 'bottleneck,' 'memory leak,' 'load time,' 'response time,' 'CPU usage' in the 'Use when' clause to better match how users describe performance problems.

DimensionReasoningScore

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 user terms such as 'slow,' 'latency,' 'bottleneck,' 'load time,' 'memory leak,' 'CPU usage,' 'caching,' 'response time,' or 'APM.'

2 / 3

Distinctiveness Conflict Risk

The scope is somewhat specific to cross-stack performance optimization, but terms like 'profiling,' 'observability,' and 'tuning' could overlap with more focused skills for backend optimization, frontend optimization, or monitoring/observability individually.

2 / 3

Total

9

/

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
Dokhacgiakhoa/antigravity-ide
Reviewed

Table of Contents

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