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performance-profiling

Performance profiling principles. Measurement, analysis, and optimization techniques.

39

Quality

37%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/performance-profiling/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%Scale 1-5

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 well-organized reference card for performance profiling concepts, but it reads more like a cheat sheet than an actionable skill. It excels at concise tabular presentation but lacks the executable code, concrete commands, and specific implementation examples that would make it truly actionable for Claude. The workflow section outlines a process conceptually but doesn't provide the concrete steps needed to execute it.

Suggestions

Add executable code examples for key actions like bundle analysis, code splitting, and runtime profiling — e.g., actual webpack-bundle-analyzer commands, specific DevTools protocol commands, or script snippets for common optimizations.

Expand the 4-step profiling workflow with concrete commands at each step, validation criteria, and a feedback loop (e.g., 'If LCP > 2.5s after fix, re-profile with DevTools Performance tab to identify next bottleneck').

Provide the referenced lighthouse_audit.py script in the bundle, or remove the reference if it doesn't exist.

Remove the 'When to Use' boilerplate section and the anti-patterns table (which contains general advice Claude already knows) to improve conciseness.

DimensionReasoningScore

Conciseness

The content is efficiently structured using tables throughout, which is token-efficient. However, some tables contain information Claude already knows (e.g., what LCP/INP/CLS measure, what 'Growing heap' means, the anti-patterns section). The final 'When to Use' section is boilerplate filler. Minor trimming opportunities exist but overall it's reasonably lean.

4 / 5

Actionability

The skill is almost entirely descriptive tables mapping symptoms to causes and findings to actions, but provides no executable code, commands, or concrete examples beyond one script invocation. Guidance like 'Code split routes' or 'Tree shake' is high-level direction without specific implementation steps. The single script reference is the only concrete executable element.

2 / 5

Workflow Clarity

The 4-step profiling workflow (Baseline → Identify → Fix → Validate) provides a clear sequence, but it's presented as abstract pseudocode with no concrete validation checkpoints, no feedback loops for when validation fails, and no specific commands or tools tied to each step. The workflow is a conceptual framework rather than an actionable procedure.

3 / 5

Progressive Disclosure

The content is well-organized with clear section headers and numbered sections, making navigation easy. However, the skill references a script (scripts/lighthouse_audit.py) that isn't provided in the bundle, and the amount of tabular reference content could benefit from being split into separate files. No bundle files are provided to support the references made.

3 / 5

Total

12

/

20

Passed

Description

25%Scale 1-5

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 description is too abstract and vague to effectively guide skill selection. It names the general domain of performance profiling but fails to specify concrete actions, lacks a 'when to use' clause, and uses overly generic terms like 'analysis' and 'optimization techniques' that could apply to many different skills.

Suggestions

Add concrete actions such as 'Profile CPU and memory usage, identify bottlenecks, generate flame graphs, analyze latency issues, benchmark code performance'.

Add an explicit 'Use when...' clause with natural trigger terms like 'Use when the user mentions slow performance, bottlenecks, profiling, benchmarking, latency, memory leaks, or CPU usage'.

Specify the technology scope (e.g., which languages, tools, or frameworks) to reduce overlap risk with other optimization-related skills.

DimensionReasoningScore

Specificity

Names the domain ('performance profiling') and lists abstract categories ('measurement, analysis, optimization techniques') but provides no concrete actions like 'profile CPU usage', 'identify bottlenecks', or 'generate flame graphs'.

2 / 5

Completeness

Has a vague 'what' (profiling principles and techniques) but completely lacks any 'when' clause or trigger guidance. No 'Use when...' or equivalent is present, which per the rubric should cap completeness at 3, and the 'what' itself is too vague to merit even that.

2 / 5

Trigger Term Quality

Includes 'performance profiling', 'measurement', 'analysis', and 'optimization' which are somewhat relevant but very generic. Missing natural user terms like 'slow', 'bottleneck', 'latency', 'benchmark', 'flame graph', 'CPU', 'memory leak', etc.

2 / 5

Distinctiveness Conflict Risk

Terms like 'analysis' and 'optimization techniques' are extremely broad and could overlap with many other skills (code review, database optimization, algorithm design, etc.). 'Performance profiling' provides some specificity but the rest is too generic.

2 / 5

Total

8

/

20

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
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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