Add 5 new cybersecurity skills with full implementations

- implementing-vulnerability-management-with-greenbone: python-gvm GMP API, scan task creation, XML report parsing
- detecting-email-account-compromise: Microsoft Graph inbox rules, impossible travel detection, OAuth grant analysis
- performing-threat-intelligence-sharing-with-misp: PyMISP event creation, attribute management, sharing validation
- analyzing-cobaltstrike-malleable-c2-profiles: dissect.cobaltstrike C2Profile parsing, Suricata rule generation
- hunting-for-registry-run-key-persistence: Sysmon Event 13 analysis, T1547.001 detection, Sigma rule generation

skills/implementing-siem-use-case-tuning/SKILL.md

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+---
+name: implementing-siem-use-case-tuning
+description: Tune SIEM detection rules to reduce false positives by analyzing alert volumes, creating whitelists, adjusting thresholds, and measuring detection efficacy metrics in Splunk and Elastic
+domain: cybersecurity
+subdomain: security-operations
+tags:
+  - siem
+  - detection-engineering
+  - false-positive-reduction
+  - splunk
+  - elastic
+  - alert-tuning
+  - soc
+version: "1.0"
+author: mahipal
+license: Apache-2.0
+---
+
+# Implementing SIEM Use Case Tuning
+
+## Overview
+
+SIEM use case tuning reduces alert fatigue by systematically analyzing detection rules for false positive rates, adjusting thresholds based on environmental baselines, creating context-aware whitelists, and measuring detection efficacy through precision/recall metrics. This skill covers tuning workflows for Splunk correlation searches and Elastic detection rules, including statistical baselining, exclusion list management, and alert-to-incident conversion tracking.
+
+## Prerequisites
+
+- Splunk Enterprise/Cloud with ES or Elastic SIEM with detection rules enabled
+- Historical alert data (minimum 30 days) for baseline analysis
+- Python 3.8+ with `requests` library
+- SIEM admin credentials or API tokens
+
+## Steps
+
+1. Export current alert volumes per detection rule from SIEM
+2. Calculate false positive rate per rule using analyst disposition data
+3. Identify top noise-generating rules by volume and FP rate
+4. Build environmental baselines for thresholds (e.g., login counts, process spawns)
+5. Create whitelist entries for known-good entities (service accounts, scanners)
+6. Adjust rule thresholds using statistical analysis (mean + N standard deviations)
+7. Measure tuning impact via before/after precision and alert-to-incident ratio
+
+## Expected Output
+
+JSON report with per-rule tuning recommendations including current FP rate, suggested threshold adjustments, whitelist entries, and projected alert reduction percentages.