analyzing-security-logs-with-splunk

Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

Quality

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

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 delivers concrete, domain-specific SPL examples in a clear six-step workflow, but it conflates multiple searches per code block in a way that hurts executability, omits verification checkpoints, and fails to link its existing bundle files. Tightening the query blocks and signaling the api-reference/script bundles would lift the weaker dimensions.

Suggestions

Split each code block so every distinct SPL search is its own separately fenced and individually executable query, replacing the '|' comment separators that currently merge multiple searches into one invalid pipeline.

Add validation checkpoints between workflow steps (e.g., verify index/sourcetype coverage after scoping, confirm CIM field normalization before interpreting results) to strengthen the investigation feedback loop.

Reference the existing bundle from the body — point to references/api-reference.md for SDK automation and scripts/agent.py for programmatic investigation so the overview correctly signals its one-level-deep detail material.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient with concrete SPL and minimal preamble, but the 'Key Concepts' table redefines terms Claude already knows (SPL, CIM, Sourcetype) and the output template adds length that could be tightened, fitting the 'mostly efficient but could be tightened' anchor.	2 / 3
Actionability	Queries use real Splunk syntax with specific indexes and EventCodes, but multiple distinct searches are concatenated inside single code blocks separated by '\|' comment lines that would not run as separate executable searches, leaving the guidance incomplete rather than copy-paste ready.	2 / 3
Workflow Clarity	A clear six-step sequence is present, but there are no validation or verification checkpoints (e.g., confirming data coverage or CIM field normalization before interpreting results), so it sits at the 'sequence present but checkpoints missing' anchor.	2 / 3
Progressive Disclosure	Bundle files (references/api-reference.md and scripts/agent.py) exist but are never referenced or signaled from the body, so overview-to-detail navigation is incomplete; structure exists but externalized content is not clearly linked.	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, uses third person, names concrete capabilities, and provides explicit natural trigger terms for when it activates. It clearly answers both 'what' and 'when' without padding or over-claims.

Dimension	Reasoning	Score
Specificity	Names multiple concrete actions ('log correlation, timeline reconstruction, and anomaly detection') and specific log types (Windows event, firewall, proxy, authentication), matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both what (correlate/reconstruct/detect across named log sources) and when ('Activates for requests involving...'), satisfying the explicit-trigger requirement rather than capping at 2.	3 / 3
Trigger Term Quality	Includes natural terms users would say ('Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation') with good coverage, matching the top anchor.	3 / 3
Distinctiveness Conflict Risk	The Splunk/SIEM security-log niche with domain-specific triggers is clearly distinguishable and unlikely to fire for unrelated skills, matching the 'clear niche with distinct triggers' anchor.	3 / 3
	Total	12 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: mukul975/Anthropic-Cybersecurity-Skills
Commit: 673da1f

Reviewed: 13 days ago

Table of Contents

Discovery Implementation Validation

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