tracking-resource-usage

Track and optimize resource usage across application stack including CPU, memory, disk, and network I/O. Use when identifying bottlenecks or optimizing costs. Trigger with phrases like "track resource usage", "monitor CPU and memory", or "optimize resource allocation".

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

50%

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

The content is well-sectioned and clearly scoped but remains abstract: it describes a process rather than giving executable commands, duplicates the workflow across two sections, and fails to point at the bundled scripts that actually exist. Adding concrete tool invocations and links to the bundled analyzer scripts would lift the weaker dimensions.

Suggestions

Replace abstract Instructions with concrete, executable commands (e.g. `top -b -n1`, `vmstat 1 5`, `iostat -x 1`, `ps aux --sort=-%mem`) so guidance is copy-paste ready.

Reference the bundled scripts from the body (e.g. 'Run scripts/data_analyzer.py <metrics_dir> to summarize collected data') so progressive disclosure actually connects to the bundle.

Collapse the redundant 'How It Works' and 'Instructions' sections and insert an explicit validate->fix->retry checkpoint after metric collection to remove padding and add a feedback loop.

Dimension	Reasoning	Score
Conciseness	The body is padded with generic restatements ('comprehensive solution', 'leverages the plugin') and duplicates the same process across 'How It Works' and 'Instructions'; it is mostly readable but could be tightened considerably. Not a 1 because it avoids explaining concepts Claude already knows.	2 / 3
Actionability	Names concrete tools (top, ps, vmstat, iostat) and metric types (heap, stack, RSS, connection pool) but provides no executable commands or code; 'Collect real-time metrics using system tools' describes rather than instructs. Not a 3 because nothing is copy-paste ready; not a 1 because it does give some concrete specifics.	2 / 3
Workflow Clarity	Steps are sequenced in 'How It Works' and 'Instructions', but there are no validation checkpoints or validate-fix-retry loops; the 'Error Handling' section is a detached troubleshooting list rather than inline verification. Per the rubric, missing validation in batch/collection operations caps this at 2.	2 / 3
Progressive Disclosure	The body is sectioned but never references the bundled scripts (data_analyzer.py and the others listed in scripts/README.md) or the assets dashboard template, so navigation to existing bundle materials is poor. Not a 1 because sections are organized and references are only one level deep where present.	2 / 3
	Total	8 / 12 Passed

Description

100%

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 is concise, specific, and follows best practices with concrete actions, explicit Use-when triggers, and natural user phrasing in third person. It cleanly answers both what the skill does and when to invoke it.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Track and optimize resource usage') across named dimensions (CPU, memory, disk, and network I/O) plus 'identifying bottlenecks or optimizing costs', matching the multiple-specific-actions anchor.	3 / 3
Completeness	Clearly answers both what (track/optimize resource usage across the stack) and when ('Use when identifying bottlenecks or optimizing costs. Trigger with phrases like...'), with an explicit Use-when clause.	3 / 3
Trigger Term Quality	Explicit natural trigger phrases ('track resource usage', 'monitor CPU and memory', 'optimize resource allocation') are terms a user would naturally say, giving good coverage.	3 / 3
Distinctiveness Conflict Risk	Targets a clear niche (system resource monitoring with specific metric categories) and uses distinct, specific triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3022dd3

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.