analyzing-logs

Analyze application logs for performance insights and issue detection including slow requests, error patterns, and resource usage. Use when troubleshooting performance issues or debugging errors. Trigger with phrases like "analyze logs", "find slow requests", or "detect error patterns".

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 a verbose, abstract overview that explains logging concepts Claude already knows and gives no executable guidance, while ignoring the real bundled scripts. It needs tightening and concrete, script-backed steps.

Suggestions

Replace the prose Overview/How It Works sections with lean instructions that assume Claude's competence and cut concept explanations like appropriate log levels and log rotation.

Make the Instructions actionable by invoking the bundled scripts (e.g., 'Run scripts/analyze_log_file.py <path> to extract KPIs') instead of abstract steps like 'Apply pattern matching'.

Add validation checkpoints to the workflow (e.g., confirm the parsed timestamp/error counts are non-empty before aggregating) and reference scripts/aggregate_logs.py and scripts/analyze_log_file.py via signaled links.

Dimension	Reasoning	Score
Conciseness	The body is padded with concepts Claude already knows ('This skill empowers Claude to automatically analyze application logs, pinpoint performance bottlenecks') and generic Best Practices teaching logging basics, wasting tokens.	1 / 3
Actionability	Instructions are abstract ('Extract relevant data', 'Apply pattern matching to identify slow requests') with no executable code or commands, even though real scripts exist in the bundle and are never invoked.	1 / 3
Workflow Clarity	A six-step Instructions sequence is present, but there are no validation checkpoints or feedback loops for what is effectively a batch analysis operation.	2 / 3
Progressive Disclosure	Sections give some structure, but the body never references the bundled scripts/references with signaled links, so discoverable one-level-deep navigation is missing.	2 / 3
	Total	6 / 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 well-formed: concrete capabilities, natural trigger terms, and explicit use-when guidance with low conflict risk. It is a strong, concise frontmatter description.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'slow requests, error patterns, and resource usage' — within the log analysis domain rather than vague language.	3 / 3
Completeness	Explicitly answers both what it does and when to use it via 'Use when troubleshooting performance issues or debugging errors' plus explicit trigger phrases.	3 / 3
Trigger Term Quality	Provides natural phrases a user would actually say — 'analyze logs', 'find slow requests', 'detect error patterns' — with good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (application log analysis) with distinct 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: about 4 hours ago

Table of Contents

Discovery Implementation Validation

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