service-mesh-observability
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
80
53%
Does it follow best practices?
Impact
98%
1.24xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/cloud-infrastructure/skills/service-mesh-observability/SKILL.mdQuality
Discovery
92%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong description that clearly articulates specific capabilities and includes an explicit 'Use when' clause with relevant trigger scenarios. The domain-specific terminology is appropriate and matches how practitioners would naturally describe these tasks. The only minor weakness is potential overlap with general observability or monitoring skills, though the service mesh focus provides reasonable distinctiveness.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'distributed tracing', 'metrics', 'visualization', 'mesh monitoring', 'debugging latency issues', and 'implementing SLOs for service communication'. These are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (implement observability including distributed tracing, metrics, visualization) and 'when' (explicit 'Use when' clause covering mesh monitoring setup, debugging latency, implementing SLOs). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'service mesh', 'distributed tracing', 'metrics', 'monitoring', 'latency issues', 'SLOs', 'observability', 'visualization'. These cover the domain well and match how practitioners naturally describe these tasks. | 3 / 3 |
Distinctiveness Conflict Risk | While 'service mesh' narrows the scope, terms like 'observability', 'metrics', 'monitoring', and 'distributed tracing' could overlap with general monitoring/observability skills or APM-focused skills. The service mesh focus helps but doesn't fully eliminate conflict risk. | 2 / 3 |
Total | 11 / 12 Passed |
Implementation
14%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a reference dump than an actionable guide. While it contains useful concrete artifacts (YAML manifests, PromQL queries, alerting rules), it lacks any workflow sequencing, validation steps, or progressive disclosure. The content is bloated with concepts Claude already knows and redundant query definitions, making it inefficient for the context window.
Suggestions
Add a clear numbered workflow (e.g., 1. Install Prometheus → verify scraping works, 2. Deploy Jaeger → verify traces appear, 3. Configure dashboards → validate golden signals) with explicit validation checkpoints at each step.
Remove the 'Core Concepts' section entirely—Claude knows the three pillars of observability and golden signals. Replace with a brief decision table: 'Istio → use Templates 1-3, Linkerd → use Template 4'.
Extract the Grafana dashboard JSON, alerting rules, and OTel collector config into separate referenced files to reduce the main skill to an actionable overview under 100 lines.
Eliminate redundant PromQL queries that appear in both the metrics template and the Grafana dashboard JSON—define them once and reference them.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines, with significant redundancy (e.g., the same PromQL queries appear in both the metrics template and the Grafana dashboard JSON). The 'Core Concepts' section explaining the three pillars of observability and golden signals is textbook knowledge Claude already knows. The ASCII diagram adds no value. | 1 / 3 |
Actionability | The YAML manifests and PromQL queries are concrete and mostly copy-paste ready, which is good. However, there's no clear workflow tying them together—it's a collection of templates without guidance on how to compose them into a working setup. Key details like prerequisites, namespace setup, and dependency ordering are missing. | 2 / 3 |
Workflow Clarity | There is no sequenced workflow at all. The content is a flat collection of templates with no ordering, no validation checkpoints, and no feedback loops. A user wouldn't know whether to install Prometheus first or Jaeger first, or how to verify each component is working before proceeding. For a multi-component setup involving destructive/complex operations, this is a significant gap. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of YAML, PromQL, JSON, and bash with no references to external files and no layered structure. The Grafana dashboard JSON alone is ~40 lines that should be in a separate file. Everything is inlined with no navigation aids or content splitting. | 1 / 3 |
Total | 5 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- wshobson/agents
- Commit
112197c
- Reviewed
Table of Contents
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