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building-detection-rules-with-sigma

Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into platform-specific queries using sigmac or pySigma backends.

60

Quality

71%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Critical

Do not install without reviewing

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/building-detection-rules-with-sigma/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Discovery

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.

This is an excellent skill description that clearly defines a specific security engineering niche. It provides concrete actions, comprehensive trigger terms that security professionals would naturally use, and an explicit 'Use when' clause with well-defined scenarios. The description is concise yet thorough, with no vague language or unnecessary fluff.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: building vendor-agnostic detection rules, mapping rules to MITRE ATT&CK techniques, converting community Sigma rules into platform-specific queries using sigmac or pySigma backends.

3 / 3

Completeness

Clearly answers both 'what' (builds vendor-agnostic detection rules using Sigma format across SIEM platforms) and 'when' (explicit 'Use when' clause covering creating detection logic from threat intelligence, mapping to MITRE ATT&CK, or converting Sigma rules to platform-specific queries).

3 / 3

Trigger Term Quality

Excellent coverage of natural terms a security professional would use: 'Sigma rule', 'SIEM', 'Splunk', 'Elastic', 'Microsoft Sentinel', 'MITRE ATT&CK', 'detection rules', 'threat intelligence', 'sigmac', 'pySigma', 'detection logic'. These are highly specific and naturally used keywords.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche focused specifically on Sigma detection rules and SIEM platforms. The combination of Sigma format, specific SIEM platforms, MITRE ATT&CK mapping, and sigmac/pySigma backends makes it very unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Implementation

42%

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

The skill excels at actionability with complete, executable code examples covering the full Sigma rule lifecycle across multiple SIEM platforms. However, it is significantly over-long and monolithic, cramming ~250 lines of content into a single file with no progressive disclosure. Several sections (Key Concepts, Tools & Systems, Common Scenarios) add bulk without actionable value, and the workflow lacks explicit validation checkpoints after critical steps like conversion and deployment.

Suggestions

Extract platform-specific conversion examples (Splunk, Elastic, Sentinel) into separate reference files (e.g., SPLUNK.md, ELASTIC.md, SENTINEL.md) and reference them from the main skill

Remove the 'Key Concepts' table, 'Tools & Systems' section, and 'Common Scenarios' list — these explain things Claude already knows or are descriptive rather than instructive

Add explicit validation checkpoints after Step 3 (verify converted query against expected output) and Step 6 (verify deployed rule fires on test data before marking complete)

Move the ATT&CK Navigator coverage script and CI/CD workflow YAML into separate bundle files, keeping only brief references in the main skill

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~250+ lines. It includes unnecessary sections like 'Key Concepts' defining terms Claude already knows (what a backend is, what YAML is), 'Common Scenarios' that are descriptive rather than instructive, a 'Tools & Systems' section with marketing-style descriptions, and an elaborate output format template. The 'When to Use' section over-explains context. Much of this could be cut by 60%+ without losing actionable value.

1 / 3

Actionability

The skill provides fully executable Python code for rule creation, validation, and conversion across three SIEM platforms. Code examples are complete and copy-paste ready with proper imports, pipeline setup, and expected outputs shown. The CI/CD YAML and Sigma rule YAML are also concrete and deployable.

3 / 3

Workflow Clarity

The 7-step workflow is clearly sequenced and logically ordered from rule creation through deployment. However, validation checkpoints are weak — Step 2 validates syntax but there's no explicit feedback loop after conversion (Step 3) to verify the output query is correct, and Step 5's testing guidance is vague ('manually test' or use a framework with no concrete validation criteria). The deployment step lacks rollback or verification steps.

2 / 3

Progressive Disclosure

The content is a monolithic wall of text with no references to external files. Everything — rule examples, conversion code for 3 platforms, ATT&CK Navigator layer generation, CI/CD config, deployment examples for multiple SIEMs, key concepts table, tools list, common scenarios, and output format — is crammed into a single file. The platform-specific conversion examples, CI/CD config, and ATT&CK coverage mapping script should each be in separate referenced files.

1 / 3

Total

7

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
mukul975/Anthropic-Cybersecurity-Skills
Reviewed

Table of Contents

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