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

82%

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Quality

Content

72%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.

Well-structured, lean, and actionable with strong progressive disclosure pointing to real bundle files. The main gap is workflow clarity: production/batch performance work needs an explicit validate-and-revert feedback loop rather than an implied 'verify' step.

Suggestions

Add an explicit feedback loop to the Golden Rule or workflow: after 'verify improvement', state the recovery path — e.g., 'if no measurable improvement or regressions appear, revert the change and re-profile to locate the real bottleneck' — to satisfy the validation checkpoint requirement for production/batch operations.

Tighten the Overview and Golden Rule sections by removing the restated capability summary and the 'Wrong:/Right:' commentary; the before/after measurement instruction alone conveys the rule with fewer tokens.

Inline one concrete profiling invocation per runtime (e.g., a py-spy or pprof command) alongside the existing profiler.py call so the quick-start is fully copy-paste ready rather than deferring all specifics to the references.

Dimension	Reasoning	Score
Conciseness	The body is lean — terse bullets, short bash snippets, no padding explaining what flamegraphs or heaps are — but the 'Golden Rule' commentary ('Wrong:... / Right:...') and the restated overview add a little over-explanation that could be trimmed, keeping it just below fully efficient.	4 / 5
Actionability	Provides concrete executable commands ('python3 scripts/performance_profiler.py /path/to/project --json', '--large-file-threshold-kb 256') and names specific tools per capability (py-spy, pprof, k6, Artillery, EXPLAIN ANALYZE); it stays one level above the deep recipes, leaving minor gaps in inline specifics.	4 / 5
Workflow Clarity	Sequences the 'establish baseline, profile, optimize, verify' flow and mandates before/after measurement, but for destructive/batch-adjacent operations (production load tests, DB optimization) it lacks an explicit error-recovery feedback loop — 'verify improvement' is stated without an 'if no improvement, revert/re-investigate' checkpoint, capping it at 3.	3 / 5
Progressive Disclosure	Clean overview pointing to verified one-level-deep references ('See references/profiling-recipes.md for details') plus a References section listing both real files with descriptions; content is appropriately split and easy to navigate.	5 / 5
	Total	16 / 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, specific description that clearly delineates capabilities and provides concrete, natural-language trigger phrases. Minor room to broaden trigger-term synonyms, but otherwise comprehensive and distinct.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions with named tools — 'generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery' — giving comprehensive coverage of capabilities.	5 / 5
Completeness	Explicitly answers both 'what' (full capability list) and 'when' via the concrete 'Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production' trigger clause.	5 / 5
Trigger Term Quality	Includes natural phrases users would say — 'investigating a slow endpoint', 'hunting a memory leak', 'performance budget' — but omits some common variants/synonyms like 'latency', 'throughput', or specific bundle file extensions, so a few natural terms are missing.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (performance profiling across Node.js, Python, Go) with distinct triggers — 'slow endpoint', 'memory leak', 'performance budget' — minimizing conflict with adjacent skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

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

Reviewed: ~5 hours ago

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