Lists multiple specific concrete actions: 'Diagnose where users come from', 'measure channel efficiency', 'identify organic vs. paid mix health', 'calculate CAC by segment', and 'find the highest-leverage acquisition channels'. These are clearly defined analytical tasks.

Clearly answers both 'what' (diagnose user sources, measure channel efficiency, calculate CAC, etc.) and 'when' with an explicit 'Use when' clause covering three distinct trigger scenarios: understanding acquisition economics, optimizing channel mix, or diagnosing stalled top-of-funnel growth.

Includes strong natural keywords users would say: 'acquisition', 'channel mix', 'CAC', 'organic vs. paid', 'growth is stalling', 'top of the funnel', 'acquisition economics', 'channel efficiency'. These cover a good range of terms a PM would naturally use.

Occupies a clear niche around user acquisition channel analysis and CAC economics. The specific focus on acquisition channels, organic vs. paid mix, and top-of-funnel diagnostics makes it highly distinguishable from general analytics or other PM skills.

Extremely verbose at ~300+ lines. Extensively explains concepts Claude already knows (what organic vs paid channels are, what viral coefficient means, what CAC is). The channel classification taxonomy, the health thresholds, and the anti-plays section all contain significant padding. Much of this could be condensed to a fraction of the size while preserving all actionable guidance.

Provides structured frameworks with specific tables, metrics, and thresholds (e.g., >40% organic = healthy, K-factor ranges), which is useful concrete guidance. However, there is no executable code, no actual analytics queries, no specific tool commands — it's entirely descriptive frameworks and prompt instructions rather than copy-paste-ready analysis steps.

The 5-step sequence is clearly laid out and logically ordered (decompose → measure quality → assess leverage → identify bottlenecks → recommend). However, there are no validation checkpoints or feedback loops — no steps to verify data quality, cross-check numbers, or iterate if analysis reveals gaps. The workflow is linear without error recovery.

References other skills (build-metric-tree, diagnose-activation, create-chart, craft-experiment-design) in the Tips section, which is good navigation. However, the main body is a monolithic wall of text with everything inline — the detailed channel classification taxonomy, anti-plays, and output format could all be separated. No bundle files exist to support progressive disclosure.