nist0/copilot_dev/.github/skills/engineering/observability/elastic
copilot_dev/.github/skills/engineering/observability/elastic/SKILL.md
# Elasticsearch & Kibana (Queries, Dashboards, Root Cause) ## When to use - You need to find the “first symptom” in logs quickly - You need to build queries/dashboards that support incident response - You need guidance on common query patterns (KQL/Lucene) and grouping ## Workflow 1) Define the incident window and scope - Time range (start, detection time, deployment window) - Services involved, environments, correlation IDs if any 2) Find the first symptom - Narrow by service + env
development
Updated Apr 8, 2026
$ install --global
skillsauthnpx skillsauth add nist0/copilotdev-tmp-consumer copilot_dev/.github/skills/engineering/observability/elasticInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 8, 2026, 2:09 AM27.0s1 file scanned
SKILL.md
Elasticsearch & Kibana (Queries, Dashboards, Root Cause)
When to use
- You need to find the “first symptom” in logs quickly
- You need to build queries/dashboards that support incident response
- You need guidance on common query patterns (KQL/Lucene) and grouping
Workflow
- Define the incident window and scope
- Time range (start, detection time, deployment window)
- Services involved, environments, correlation IDs if any
- Find the first symptom
- Narrow by service + env + time window
- Sort by time ascending; locate the earliest failure chain
- Query patterns (KQL-style)
- Filter by service:
service.name : "api-*" - Filter by status:
http.response.status_code >= 500 - Filter by exception type:
error.type : "TimeoutException" - Group by: exception type, endpoint, pod, node
- Filter by service:
- Correlate with deployments
- Filter by
kubernetes.pod.name,kubernetes.deployment.name,container.image.tag - Compare before/after deployment timestamps
- Filter by
- Avoid traps
- Noisy logs: use sampling / severity thresholds
- Mixed schemas: ensure consistent fields (service.name, trace.id, transaction.id)
- Improve dashboards and alerts
- Add panels for: error rate, latency percentiles, top exceptions, top endpoints
- Add runbook links and correlation fields
Outputs
- Next 3–7 queries to run (with rationale)
- Root cause hypothesis candidates (ranked)
- Dashboard panel suggestions + alert rule ideas
- Log schema improvements (fields to standardize)
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