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siem-detection

Engineer and audit SIEM detection rules — log source coverage, Sigma / KQL / SPL / Elastic query authoring, MITRE ATT&CK mapping, false-positive tuning, and detection-as-code workflows. Use when the user mentions 'SIEM,' 'detection engineering,' 'detection rules,' 'Sigma,' 'KQL,' 'SPL,' 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle,' 'detection-as-code,' 'MITRE ATT&CK mapping,' 'log coverage,' 'alert tuning,' 'use case development,' or needs help building or improving security detections.

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

A well-engineered skill body: executable rule examples across four query languages, a six-step methodology with real validation checkpoints (gap analysis, experimental deployment, FP-ratio thresholds, deliberate-test verification), and concrete detection-as-code CI checks. The main improvement opportunities are trimming a few conversational asides and moving the per-backend query examples and reference list into one-level-deep files to slim the main document.

Suggestions

Trim conversational asides ("future-you will thank you", "pays for itself the first time someone asks...") and drop the definitional sentence about Sigma being a cross-SIEM format — Claude already knows this, and every token in SKILL.md competes with conversation context.

Move the per-backend query examples (KQL, SPL, ES|QL) into a references/ file (e.g., references/backend-queries.md), keeping only the canonical Sigma example inline, to shorten the main document while keeping translations one level deep.

Convert the References section from a bare name list into linked pointers (URLs for the ATT&CK matrix, SigmaHQ repo, detection maturity matrix, Pyramid of Pain) so the listed resources are actually usable.

DimensionReasoningScore

Conciseness

The body is largely dense and efficient — categorization and detection-model tables, four complete query examples, a repo layout, and a CI checklist with specific commands. It is not a 5 because a few tokens don't earn their place: conversational asides ("future-you will thank you", "pays for itself the first time someone asks 'what do we detect?'") and a definitional sentence Claude doesn't need ("Sigma is the cross-SIEM detection format"). It sits above the score-3 anchor because these are minor trims, not whole padded sections.

4 / 5

Actionability

Guidance is fully executable throughout: complete Sigma, KQL, SPL, and ES|QL rule examples that are copy-paste ready, concrete CI commands ("sigma-cli check", "sigma convert -t splunk"), a directory layout for detections-as-code, and a filled-in coverage-assessment output template. This matches the score-5 anchor 'fully executable; copy-paste ready code or commands; specific examples cover the common cases' and exceeds the score-4 anchor, which allows minor gaps — none are apparent.

5 / 5

Workflow Clarity

The six-step methodology is clearly sequenced with explicit validation checkpoints and feedback loops: a gap check before writing any rules ("Techniques with NO source mapped are blind spots — write them down before writing any rules"), the experimental-to-production tuning lifecycle with an FP-ratio check ("if FPs are > 80% after tuning, the detection model is wrong"), verification of never-fired rules via "a deliberate-test event through the system," and error recovery ("Roll back via Git revert"). This matches the score-5 anchor; no validation gaps significant enough to fall to 4.

5 / 5

Progressive Disclosure

As a single-file skill with no bundle files, the content is well organized under clear section headers (Authorization, Scope, Methodology steps, Output Format, Boundaries, References) with nothing buried or nested — good structure with most content appropriately placed. It falls short of 5 because the skill is long (~240 lines) and the per-backend query examples (KQL, SPL, ES|QL) plus the general References list would sit more naturally in one-level-deep reference files, and the References section names resources without links; these are minor organization gaps fitting the score-4 anchor rather than the score-3 'content that should be separate is inline' anchor, since what remains inline serves the core workflow.

4 / 5

Total

18

/

20

Passed

Description

96%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 description: it states concrete capabilities in third person, names the domain's query languages and workflow activities, and includes an explicit 'Use when...' clause with an extensive, natural trigger-term list. The only weakness is that several bare platform names (Splunk, Sentinel, Elastic) could pull the skill in for non-detection tasks on those platforms.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions with comprehensive coverage: "Engineer and audit SIEM detection rules — log source coverage, Sigma / KQL / SPL / Elastic query authoring, MITRE ATT&CK mapping, false-positive tuning, and detection-as-code workflows." It matches the anchor 'lists multiple specific concrete actions; comprehensive coverage' — the actions are named and complete for the domain, so it does not fall to the score-4 anchor with coverage gaps.

5 / 5

Completeness

It explicitly answers both questions: a clear 'what' ("Engineer and audit SIEM detection rules — ... false-positive tuning, and detection-as-code workflows") and an explicit 'when' with concrete trigger phrases ("Use when the user mentions 'SIEM,' ... or needs help building or improving security detections."). This is a direct match for the score-5 anchor; a missing or weak 'Use when' clause would have capped it at 3, but the trigger guidance is fully explicit.

5 / 5

Trigger Term Quality

The trigger clause covers natural terms comprehensively, including synonyms and platform names users actually say: "'SIEM,' 'detection engineering,' 'detection rules,' 'Sigma,' 'KQL,' 'SPL,' 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle,' 'detection-as-code,' 'MITRE ATT&CK mapping,' 'log coverage,' 'alert tuning,' 'use case development.'" This matches the anchor 'comprehensive coverage of natural terms including synonyms' and clearly exceeds the score-4 'a few natural terms missing' anchor.

5 / 5

Distinctiveness Conflict Risk

The detection-engineering niche is clear and terms like 'Sigma,' 'detection-as-code,' and 'MITRE ATT&CK mapping' are unambiguous triggers. However, the list also includes bare platform names — 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle' — that a user could mention for non-detection work (dashboarding, search performance, platform administration), creating minor overlap risk with closely related log-management skills. This fits the score-4 anchor ('mostly distinct; minor overlap risk with closely related skills') better than the score-5 anchor's 'minimal conflict risk,' and is well above the score-3 'could still overlap with similar skills' anchor.

4 / 5

Total

19

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository

briiirussell/cybersecurity-skills

Path

skills/siem-detection/SKILL.md

Reviewed

Table of Contents

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