monitoring-cpu-usage

Monitor this skill enables AI assistant to monitor and analyze cpu usage patterns within applications. it helps identify cpu hotspots, analyze algorithmic complexity, and detect blocking operations. use this skill when the user asks to "monitor cpu usage", "opt... Use when setting up monitoring or observability. Trigger with phrases like 'monitor', 'metrics', or 'alerts'.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

20%

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

The body is mostly filler and abstraction: it restates the same capabilities repeatedly, contains no executable code or commands, invokes a fictional plugin, and ignores the bundled scripts entirely. Workflow and file structure are present but weak and unsignaled.

Suggestions

Replace the abstract "activates the cpu-usage-monitor plugin" step with concrete, executable usage of the bundled scripts (e.g., `python scripts/cpu_analyzer.py <target> --output report.txt`), and link to them from the body.

Cut the redundant Overview/When to Use/Examples restatements and the generic Instructions/Output/Resources/Error Handling filler; keep only what Claude does not already know.

Add a validation/review checkpoint to the workflow (e.g., confirm flagged hotspots by re-profiling after a suggested change) so the sequence has explicit verification rather than ending at "provide recommendations".

Dimension	Reasoning	Score
Conciseness	The body is padded with redundant restatements of the same three capabilities across Overview, How It Works, When to Use, and Examples, plus generic filler sections ("The skill produces structured output relevant to the task.", Instructions, Resources) that add no value — matching the verbose/padded anchor.	1 / 3
Actionability	No executable code or commands appear anywhere in the body; guidance is abstract ("Analyze the provided Python script for CPU-intensive functions", "Consider using asynchronous operations"), and the bundled scripts in scripts/ are never referenced or shown, so it describes rather than instructs.	1 / 3
Workflow Clarity	"How It Works" gives a 3-step sequence (Initiate → Code Analysis → Recommendations), but the steps are abstract, reference a fictional "cpu-usage-monitor plugin", and lack any validation checkpoints or concrete commands.	2 / 3
Progressive Disclosure	Sections are organized with headers, but the body is largely a monolithic inline wall of text that never signals the available bundle files (scripts/cpu_analyzer.py, scripts/example_usage.py, assets), and the bundle READMEs themselves are inconsistent (listing cpu_profiler.py/example_usage.sh that are absent).	2 / 3
	Total	6 / 12 Passed

Description

77%

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 conveys concrete capabilities and explicit trigger guidance, but it is malformed — two concatenated drafts with a truncated trigger list — and its broad observability triggers create conflict risk with general monitoring skills.

Suggestions

Rewrite the description as a single coherent sentence so the merged "Monitor this skill enables AI assistant to monitor..." prefix and the "opt..." truncation are removed.

Narrow the triggers to CPU-specific phrasings (e.g., "cpu load", "profiling", "hot code paths") and drop generic terms like "metrics" and "alerts" that overlap with general observability skills.

Keep the explicit "Use when..." clause but ensure it reads as third-person, properly capitalized, and self-contained.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "identify cpu hotspots, analyze algorithmic complexity, and detect blocking operations" — matching the anchor for listing several specific concrete actions, despite a malformed prefix.	3 / 3
Completeness	Explicitly states what it does (monitor/analyze cpu usage, find hotspots/complexity/blocking) and when to use it ("use this skill when the user asks to \"monitor cpu usage\"... Use when setting up monitoring or observability"), satisfying both what and when with explicit triggers.	3 / 3
Trigger Term Quality	Includes natural terms ("monitor cpu usage", "monitor", "metrics", "alerts"), but the trigger list is truncated ("opt...") and leans on generic observability words while missing common CPU-specific variations like "cpu load", "profiling", or "slow code".	2 / 3
Distinctiveness Conflict Risk	The CPU-specific niche is present, but broad triggers ("monitoring or observability", "metrics", "alerts") overlap with general observability skills, so it could trigger for the wrong skill.	2 / 3
	Total	10 / 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

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