monitoring-observability

Monitoring and observability strategy, implementation, and troubleshooting. Use this skill whenever the user mentions monitoring, observability, metrics, logs, traces, alerting, SLOs, Prometheus, Grafana, Datadog, Loki, or OpenTelemetry. Triggers include designing metrics strategy (Four Golden Signals, RED/USE), setting up Prometheus/Grafana/Loki, creating alerts or dashboards, calculating SLOs and error budgets, instrumenting with OpenTelemetry, analyzing performance issues, choosing between monitoring tools, optimizing Datadog costs, migrating to open-source stack, and setting up distributed tracing.

Quality

93%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

86%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 comprehensive, highly actionable observability skill with clear navigation and verified bundle references. Its main weaknesses are minor redundancy between inline sections and the migration workflow lacking explicit inter-phase validation gates.

Suggestions

Remove the duplicated PromQL block in 'Quick Reference Commands' (or replace it with a pointer to section 1) to tighten token efficiency.

Add explicit validation checkpoints between the four Datadog migration phases (e.g., 'Validate metric parity before proceeding to Phase 2') to strengthen feedback loops.

Move the alert-severity and SLO-target tables into their respective reference files, keeping only a brief inline pointer, to reduce inline reference duplication.

Dimension	Reasoning	Score
Conciseness	Largely lean and executable with minimal concept re-teaching, but the PromQL block repeats verbatim in section 1 and the 'Quick Reference Commands' section, and some inline reference tables (alert severity, SLO targets) duplicate material that belongs in references.	4 / 5
Actionability	Abundant copy-paste-ready material — PromQL queries, curl/cloudwatch commands, python script invocations with flags, OTel instrumentation code, and YAML alert rules — that directly covers the common cases.	5 / 5
Workflow Clarity	A clear top-level decision tree sequences the nine sections and the alert_quality_checker provides validation, but the Datadog migration phases are listed without explicit validate-then-proceed gates or feedback loops between phases.	4 / 5
Progressive Disclosure	SKILL.md acts as a well-organized overview with one-level-deep, clearly signaled 'Deep dive: references/...' pointers; all referenced scripts, references, and template files exist in the bundle and are easy to navigate.	5 / 5
	Total	18 / 20 Passed

Description

100%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, well-structured description that concretely states capabilities, enumerates natural trigger terms and tool names, and explicitly pairs 'what' with 'when' in third-person voice. No ambiguity or fluff to penalize.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'designing metrics strategy (Four Golden Signals, RED/USE)', 'creating alerts or dashboards', 'calculating SLOs and error budgets', 'instrumenting with OpenTelemetry', 'optimizing Datadog costs', 'migrating to open-source stack' — giving comprehensive coverage rather than vague abstractions.	5 / 5
Completeness	Explicitly answers both 'what' ('Monitoring and observability strategy, implementation, and troubleshooting') and 'when' ('Use this skill whenever the user mentions...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Comprehensive natural-term coverage including synonyms and tool names: 'monitoring, observability, metrics, logs, traces, alerting, SLOs, Prometheus, Grafana, Datadog, Loki, or OpenTelemetry' — terms a user would naturally say.	5 / 5
Distinctiveness Conflict Risk	Clear niche anchored to specific monitoring tools and methodologies, with trigger terms unlikely to fire for unrelated skills.	5 / 5
	Total	20 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 4 deeper-than-1-level	Warning
referenced_paths_exist	Referenced path issues: 8 deeper-than-1-level	Warning

	Total	14 / 16 Passed

Repository: ahmedasmar/devops-claude-skills
Path: monitoring-observability/skills/SKILL.md
Commit: 1489c33

Reviewed: 7 days ago

Table of Contents

Discovery Implementation Validation

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