.claude/skills/coverage-analysis/SKILL.md
Analyzes detection coverage using Sigma, Splunk, and Elastic rules. Use when checking coverage for techniques, tactics, threat actors, or generating Navigator layers from detections.
npx skillsauth add mhaggis/security-detections-mcp detection-coverage-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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analyze_coverage(source_type: "elastic")
Returns coverage % by tactic, top techniques, weak spots.
identify_gaps(threat_profile: "ransomware")
identify_gaps(threat_profile: "apt")
identify_gaps(threat_profile: "persistence")
Returns prioritized P0/P1/P2 gaps with recommendations.
suggest_detections(technique_id: "T1059.001")
Returns existing detections, data sources needed, detection ideas.
generate_navigator_layer(
name: "Elastic Initial Access",
source_type: "elastic",
tactic: "initial-access"
)
Returns ready-to-import Navigator JSON.
get_technique_ids(source_type: "elastic", tactic: "persistence")
Returns ~200 bytes instead of ~50KB.
| Profile | Key Techniques | |---------|----------------| | ransomware | T1486, T1490, T1027, T1547 | | apt | T1003, T1021, T1053, T1071 | | initial-access | T1566, T1190, T1078 | | persistence | T1547, T1543, T1053 | | credential-access | T1003.*, T1555, T1552 | | defense-evasion | T1027, T1070, T1055 |
# BAD - returns 200+ full detection objects
list_by_mitre_tactic(tactic: "execution")
# GOOD - returns stats only
analyze_coverage(source_type: "elastic")
| Old Approach | New Approach | |--------------|--------------| | list_by_mitre_tactic → ~50KB | analyze_coverage → ~2KB | | Parse in context | Done server-side | | 25x more tokens | Efficient |
testing
Expert at analyzing unstructured threat intelligence reports (CISA alerts, vendor blogs, research papers) and extracting actionable detection logic, TTPs, behavioral indicators, and MITRE ATT&CK mappings. Focuses on behaviors over IOCs. Use when provided with threat reports, security advisories, or campaign documentation.
testing
Analyze software supply chain attacks across package registries (npm, PyPI, RubyGems), CI/CD pipelines (GitHub Actions, GitLab CI), and container ecosystems. Includes detection engineering patterns for Splunk, Sentinel, Elastic, and Sigma.
testing
Optimize detection queries for performance across Splunk (SPL), Microsoft Sentinel (KQL), and Elastic Security (EQL/ES|QL). Covers search pipeline internals, common anti-patterns, and optimization techniques for detection rules on each platform.
tools
Analyze pull requests for detection coverage gaps and recommend additional detections, story alignments, and test coverage to extend PRs before merge.