Name: performance-profiler
Rating: 64.8 (1 reviews)
Author: alirezarezvani

performance-profiler

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.

Quality

78%

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Securityby

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tessl review fix ./.gemini/skills/performance-profiler/SKILL.md

The canonical home for this skill is performance-profiler in alirezarezvani/claude-skills

Quality

Content

63%Weight 40%Scale 1-5

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

The body is well-organized and concise with a sound measure-first workflow, but its value is undermined because all detailed content is offloaded to reference files and a script that are not present in the bundle. Adding the missing files or inlining the key profiling recipes would resolve the largest gaps.

Suggestions

Ship the referenced bundle files (references/profiling-recipes.md, references/optimization-playbook.md, scripts/performance_profiler.py) so the offloaded detail and Quick Start commands are actually available.

Inline at least one concrete profiling recipe (e.g. a py-spy or pprof flamegraph command) so the body is actionable even before opening references.

Convert the "Golden Rule: Measure First" from comment prose into an explicit numbered workflow with a concrete validation/re-measure step.

Dimension	Reasoning	Score
Conciseness	The body is lean and assumes Claude's competence (bullet lists, command snippets), but the Overview near-duplicates the frontmatter description and the "Golden Rule" section is comment-only prose that could be trimmed, keeping it just below a 5.	4 / 5
Actionability	Quick Start gives concrete commands, but the substantive profiling know-how (flamegraph generation, py-spy, pprof, EXPLAIN ANALYZE) is deferred to references/profiling-recipes.md and the Golden Rule section is comment-only — and the referenced script/files do not exist in the bundle, leaving key details missing.	3 / 5
Workflow Clarity	The "Golden Rule: Measure First" lays out a clear before/after sequence (baseline → profile → confirm bottleneck → fix → measure → verify) with an implicit verification checkpoint, but it is presented as comments rather than an explicit numbered workflow with validation commands, so it stops short of a 5.	4 / 5
Progressive Disclosure	The body is well-sectioned and clearly signals one-level-deep references (profiling-recipes.md, optimization-playbook.md), but those referenced files and the cited script are absent from the bundle, so the disclosure chain leads to dead ends and core profiling content is unreachable.	3 / 5
	Total	14 / 20 Passed

Description

92%Weight 40%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.

A strong, third-person description that concretely enumerates capabilities and pairs them with explicit, natural-language use-when triggers. It is comprehensive and distinctive, with only minor room to add synonyms for trigger terms.

Dimension	Reasoning	Score
Specificity	Names the domain (Node.js/Python/Go profiling) and lists many concrete actions — "Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery" — giving comprehensive coverage rather than the minor gaps of a 4.	5 / 5
Completeness	Explicitly answers both what ("Systematic performance profiling... Identifies... generates... optimizes... runs load tests") and when ("Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production") with concrete trigger phrases, matching the 5 anchor rather than the less-explicit 4.	5 / 5
Trigger Term Quality	Includes natural phrases users would say ("slow endpoint", "performance budget", "memory leak") but is missing common synonyms (e.g. "latency", "profiling", "flamegraph") and file extensions that would push it to a 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (multi-language performance profiling with named tools k6/Artillery) and uses distinct triggers (slow endpoint, memory leak, performance budget), giving minimal overlap with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 2 missing	Warning
referenced_paths_exist	Referenced path issues: 8 missing	Warning

	Total	14 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/performance-profiler/SKILL.md
Commit: 19392f7

Reviewed: ~6 hours ago

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