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Skills service-mesh-observability
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service-mesh-observability
Content revision r1 High Risk ⚙️ External commands🌐 Network access📁 Filesystem access
Implement Service Mesh Observability
Service mesh teams need clear telemetry patterns to debug latency, errors, and dependencies. This skill provides metrics, tracing, dashboard, and alerting templates for Istio, Linkerd, and OpenTelemetry.
Supports: Claude Codex Code(CC)
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Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "service-mesh-observability" from https://skillstore.io/skills/sickn33-service-mesh-observability.md and its manifest at https://skillstore.io/api/skills/sickn33-service-mesh-observability/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "service-mesh-observability". I need to monitor Istio service errors and latency.
Expected outcome:
A monitoring plan with Prometheus signal definitions, Grafana panel goals, P99 latency alerts, and validation steps.
Using "service-mesh-observability". My Linkerd service calls are slow after a deployment.
Expected outcome:
A troubleshooting sequence using route metrics, live traffic inspection, dependency edges, and trace correlation.
Using "service-mesh-observability". We need production-ready mesh observability.
Expected outcome:
A rollout checklist covering telemetry collection, dashboard ownership, alert thresholds, retention, sampling, and security controls.
Security Audit
High Riskv4 • 7/7/2026 Open versioned report
Most static external command findings are Markdown formatting false positives, and the hardcoded documentation or in-cluster URLs are benign examples. The OpenTelemetry template binds telemetry ports to 0.0.0.0, and the Kiali template uses anonymous authentication with broad namespace visibility. No prompt injection text or malicious exfiltration intent was found.
1
Files scanned
396
Lines analyzed
4
Review items
0
False positives ignored
Confirmed security concerns (1)
High
Insecure Kiali Dashboard Configuration
The Kiali template sets anonymous authentication and grants visibility across all namespaces. If deployed as written, it can expose mesh topology and workload metadata to unauthorized users.
The YAML explicitly sets auth.strategy to anonymous and accessible_namespaces to ** in the same Kiali configuration block. This is a clear insecure default for shared or production clusters.
Capability review items (4)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Medium
Hardcoded IP address
endpoint: 0.0.0.0:4317
The OpenTelemetry template binds a receiver or exporter to 0.0.0.0, which exposes telemetry endpoints on all container interfaces if deployed without network controls. This can be legitimate in Kubernetes, but it needs explicit access restrictions.
Medium
Hardcoded IP address
endpoint: 0.0.0.0:4318
The OpenTelemetry template binds a receiver or exporter to 0.0.0.0, which exposes telemetry endpoints on all container interfaces if deployed without network controls. This can be legitimate in Kubernetes, but it needs explicit access restrictions.
Medium
Hardcoded IP address
endpoint: 0.0.0.0:9411
The OpenTelemetry template binds a receiver or exporter to 0.0.0.0, which exposes telemetry endpoints on all container interfaces if deployed without network controls. This can be legitimate in Kubernetes, but it needs explicit access restrictions.
Medium
Hardcoded IP address
endpoint: 0.0.0.0:8889
The OpenTelemetry template binds a receiver or exporter to 0.0.0.0, which exposes telemetry endpoints on all container interfaces if deployed without network controls. This can be legitimate in Kubernetes, but it needs explicit access restrictions.
Risk Factors
⚙️ External commands (18)
🌐 Network access (11)
📁 Filesystem access (1)
Audited by: codex View Audit History →
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APA citation
sickn33. (2026). service-mesh-observability security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-service-mesh-observability/audits/4BibTeX citation
@techreport{sickn33-sickn33-service-mesh-observability-2026,
author = {sickn33},
title = {service-mesh-observability security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/sickn33-service-mesh-observability/audits/4},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "service-mesh-observability security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-07"
url: "https://skillstore.io/skills/sickn33-service-mesh-observability/audits/4"
identifiers:
- type: other
value: "skillstore:sickn33-service-mesh-observability:audit:4"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Higher Skillstore usage
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
wshobson Recommended
wshobson-service-mesh-observability
Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 13
Updated
2026-08-21
sickn33 Current
sickn33-service-mesh-observability
Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 9
Updated
2026-08-21
Skillstore Score
Why this score Evidence Confidence: Medium 55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance
What You Can Build
Debug Mesh Latency
Trace slow service calls and compare P99 latency with request rate and error metrics.
Build Mesh Dashboards
Create Grafana and Kiali views that show traffic, errors, dependencies, and topology.
Define Service SLOs
Choose mesh-level signals and alert thresholds for service communication reliability.
Try These Prompts
Plan Basic Monitoring
Create a basic service mesh observability plan for my cluster. Include metrics, dashboards, and alerts for request rate, errors, and latency.
Add Distributed Tracing
Help me add distributed tracing to an Istio mesh with Jaeger. Explain sampling, trace context, and validation steps.
Investigate Latency
Guide me through debugging high P99 latency between two services in a service mesh. Use metrics, traces, and dependency views.
Design Production Observability
Design a production service mesh observability rollout with Prometheus, Grafana, Kiali, OpenTelemetry, SLOs, and security hardening.
Best Practices
- Start with request rate, error rate, latency, and saturation before adding custom metrics.
- Use lower trace sampling in production and keep higher sampling for development or incidents.
- Protect dashboards and telemetry endpoints with authentication, authorization, and network policy.
Avoid
- Do not deploy example dashboards without checking label names and cardinality.
- Do not expose Kiali, Grafana, Jaeger, or collector endpoints without access controls.
- Do not alert on every metric without clear service ownership and response guidance.
Frequently Asked Questions
Which service meshes does this skill cover?
It covers common observability patterns for Istio and Linkerd, with examples for Prometheus, Grafana, Jaeger, Kiali, and OpenTelemetry.
Can it generate production-ready configuration?
It can provide strong starting templates, but production use requires review for versions, labels, access controls, and network policy.
Does it connect to my cluster?
No. The skill provides guidance and templates. Any live cluster checks require explicit user-approved commands.
What metrics should I start with?
Start with request rate, error rate, latency percentiles, and saturation. Add service-specific metrics after the baseline is stable.
How should tracing be sampled?
Use high sampling in development and lower sampling in production. Increase sampling temporarily during incidents when storage capacity allows.
Is anonymous dashboard access safe?
No. Dashboards can expose service topology and operational data. Use authentication and limit access by namespace and role.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/service-mesh-observabilityRef
9f814fc6a43fd99946f2da5e0df231c65a38bc76
Maintenance freshness
7/18/2026
Usage
6 downloads · 105 views
File structure
📄 SKILL.md
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