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/references/api-reference.md

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+# SIEM Use Case Tuning API Reference
+
+## Splunk Notable Event Export
+
+### Export Notables via SPL
+```spl
+| inputlookup notable_events
+| search status_label IN ("New", "In Progress", "Resolved")
+| table rule_name, _time, status_label, src, dest, user, urgency
+| rename status_label as disposition, _time as timestamp
+| outputlookup alert_export.csv
+```
+
+### Splunk ES Correlation Search Tuning
+```spl
+# Measure FP rate per correlation search over 30 days
+| inputlookup notable_events where earliest=-30d
+| eval is_fp=if(status_label="Resolved" AND disposition="False Positive", 1, 0)
+| stats count as total, sum(is_fp) as fp_count by rule_name
+| eval fp_rate=round(fp_count/total, 4)
+| sort -fp_rate
+```
+
+### Update Correlation Search Threshold
+```
+POST /servicesNS/nobody/SplunkEnterpriseSecuritySuite/saved/searches/{search_name}
+Content-Type: application/x-www-form-urlencoded
+
+search=<updated_spl_with_new_threshold>
+```
+
+## Elastic Detection Rule Tuning
+
+### List Detection Rules
+```
+GET /_security/detection_engine/rules/_find?per_page=100
+Authorization: ApiKey <base64_api_key>
+```
+
+### Add Exception to Rule
+```json
+POST /_security/detection_engine/rules/exceptions
+{
+  "rule_id": "rule-uuid",
+  "name": "Whitelist scanner IPs",
+  "entries": [
+    {
+      "field": "source.ip",
+      "operator": "is_one_of",
+      "value": ["10.0.1.50", "10.0.1.51"],
+      "type": "match_any"
+    }
+  ]
+}
+```
+
+### Query Rule Execution Stats (Kibana)
+```kql
+event.kind: "signal" AND kibana.alert.rule.name: "Brute Force Detection"
+| stats count by kibana.alert.workflow_status
+```
+
+## Alert Tuning Metrics
+
+| Metric | Formula | Target |
+|---|---|---|
+| False Positive Rate | FP / (FP + TP) | < 30% |
+| Precision | TP / (TP + FP) | > 70% |
+| Alert-to-Incident Ratio | Incidents / Total Alerts | > 20% |
+| Mean Time to Triage | avg(triage_end - alert_time) | < 15 min |
+
+## CLI Usage
+
+```bash
+# Analyze alert CSV export
+python agent.py --alert-csv notable_export.csv --output tuning.json
+
+# Adjust FP threshold for whitelist candidates
+python agent.py --alert-csv alerts.csv --fp-threshold 0.9 --top-rules 10
+
+# CSV format: rule_name,timestamp,disposition,source,user,severity
+```