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.

1.39x

Quality

74%

Does it follow best practices?

Impact

95%

1.39x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./tests/ext_conformance/artifacts/agents-wshobson/cloud-infrastructure/skills/service-mesh-observability/SKILL.md

The canonical home for this skill is service-mesh-observability in wshobson/agents

Quality

Content

65%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A code-rich, actionable reference packed with executable templates, but it functions as a monolithic catalog rather than a guided workflow, lacks validation checkpoints for deployment operations, and does not split bulk material into bundle files.

Suggestions

Add a sequenced deployment workflow with explicit validation checkpoints (e.g. apply manifests -> verify pods ready -> confirm Prometheus targets up -> check Jaeger UI for traces) instead of presenting templates as a flat catalog.

Move the large Grafana dashboard JSON and full installation manifests into files under references/ and link to them one level deep, keeping SKILL.md as a lean overview.

Trim the 'Three Pillars of Observability' ASCII diagram and 'Golden Signals' table, which restate concepts Claude already knows, to improve token efficiency.

Dimension	Reasoning	Score
Conciseness	Heavy on executable code with minimal prose, but the 'Three Pillars of Observability' ASCII diagram and 'Golden Signals for Mesh' table restate concepts Claude already knows and could be trimmed.	4 / 5
Actionability	Provides copy-paste-ready PromQL, YAML manifests, bash commands, and Grafana JSON covering common cases, though a few templates (e.g. the Prometheus ConfigMap) are partial snippets rather than complete manifests.	4 / 5
Workflow Clarity	Content is organized as parallel numbered templates rather than a sequenced workflow, and the kubectl-apply/deploy operations lack validation checkpoints or feedback loops, capping this dimension.	3 / 5
Progressive Disclosure	No bundle files exist in references/scripts/assets, and large inlineable content (full Grafana dashboard JSON, lengthy manifests) lives entirely in SKILL.md with only external doc links rather than one-level-deep internal references.	3 / 5
	Total	14 / 20 Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that clearly states capabilities and provides an explicit 'Use when' clause with natural trigger phrases. It is mostly distinct to the service-mesh niche with only minor overlap risk.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete actions ('distributed tracing, metrics, and visualization'), but 'comprehensive observability' is slightly fluffy and the action list is only three items rather than fully comprehensive.	4 / 5
Completeness	Explicitly answers both what ('Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization') and when ('Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication') with concrete trigger phrases, in third person.	5 / 5
Trigger Term Quality	Natural phrases users would say appear ('setting up mesh monitoring', 'debugging latency issues', 'implementing SLOs'), but synonyms like specific mesh names (Istio/Linkerd) or 'tracing'/'dashboards' are missing.	4 / 5
Distinctiveness Conflict Risk	'Service meshes' is a clear niche with distinct triggers, but 'debugging latency issues' and general 'observability' framing create minor overlap risk with broader monitoring skills.	4 / 5
	Total	17 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: Dicklesworthstone/pi_agent_rust
Path: tests/ext_conformance/artifacts/agents-wshobson/cloud-infrastructure/skills/service-mesh-observability/SKILL.md
Commit: 6453c0b

Reviewed: about 9 hours ago

Table of Contents

Discovery Implementation Validation

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