Lists multiple specific concrete actions and domains: CPU, memory, disk, network, containers, process-level telemetry, timeseries queries, anomaly detection, forecasting, seasonality analysis. Very detailed about what it covers.

Clearly answers both 'what' (host and process metrics including CPU, memory, disk, network, containers, process-level telemetry) and 'when' (explicit 'Use when' clause plus extensive trigger list). Also includes explicit 'Do NOT use' guidance with redirects to other skills, which goes above and beyond.

Excellent coverage of natural trigger terms users would actually say, including 'show hosts', 'CPU usage', 'memory utilization', 'disk space', 'high CPU', 'top hosts by CPU', 'top processes by memory', 'container monitoring', 'CPU forecast', 'memory anomaly'. These are highly natural and varied.

Highly distinctive with explicit boundary-setting via 'Do NOT use' clauses that redirect to specific alternative skills (dt-obs-kubernetes, dt-obs-aws, dt-obs-services). This makes it very clear what falls inside vs outside this skill's scope, minimizing conflict risk.

The skill is fairly long (~350 lines) and includes some content Claude already knows (well-known port numbers, workload type lists, basic unit conversion). However, most content is domain-specific DQL syntax and platform-specific attributes that earn their place. The 'When to Use This Skill' section largely duplicates the frontmatter description. Some sections like alert thresholds and best practices are useful but could be tighter.

The skill provides numerous executable DQL queries covering all major workflows — host discovery, resource monitoring, process analysis, technology inventory, port scanning, container monitoring, cost attribution, and cross-layer correlation. Queries are copy-paste ready with concrete metric names, field references, and filter patterns. The analytical workflow section gives specific tool selection guidance and parameter recommendations.

Multi-step analytical workflows (anomaly detection, forecasting, seasonality) are clearly sequenced with explicit steps (construct query → pass to tool). The skill includes clear decision points (adaptive-anomaly-detector vs timeseries-novelty-detection), scope boundaries (host vs service metrics), and specific parameter guidance (novelty type exclusions, minimum historical data windows). The troubleshooting table provides error recovery paths. Response construction guidelines serve as validation checkpoints.

Excellent progressive disclosure structure: the main skill covers 80% of use cases with inline queries, then provides clearly signaled one-level-deep references to four specialized files (host-metrics.md, process-monitoring.md, container-monitoring.md, inventory-discovery.md). The 'When to Load References' section gives explicit trigger conditions for each reference file. References are linked inline at relevant points throughout the document with anchor-level specificity.