Lists multiple specific concrete actions: analyzes 6 named ad platforms via parallel audit agents, generates health scores per platform and aggregate scores (0-100), and references specific sub-skills for additional capabilities.

Clearly answers both 'what' (multi-platform paid advertising audit with parallel subagent delegation, health scores) and 'when' (explicit 'Use when user says...' clause with extensive trigger terms).

Excellent coverage of natural trigger terms users would say: 'audit', 'full ad check', 'analyze my ads', 'account health check', 'paid media audit', 'paid advertising audit', 'ad spend audit', 'advertising audit', 'PPC audit'. These are highly natural phrases.

Highly distinctive — focuses on full multi-platform paid advertising audits with parallel execution, clearly differentiates itself from standalone sub-skills (ads-amazon, ads-attribution, ads-server-side-tracking), and the trigger terms are specific to advertising audits rather than generic analysis.

The content is reasonably efficient but includes some unnecessary detail that Claude could infer, such as the full report structure section which is essentially a template outline, and the thinking framework preamble. The scoring weights table and priority definitions are useful reference material but the report structure section is verbose.

The skill provides a clear process with named subagents and specific check counts, scoring formulas, and output file names. However, it lacks executable code or commands — there are no actual API calls, script invocations, or concrete delegation syntax. The subagent references (audit-google, audit-meta, etc.) are named but not shown how to invoke. The Quick Wins criteria pseudocode is illustrative but not executable.

The process is clearly sequenced with numbered steps, includes two explicit validation checkpoints (step 2: confirm data availability before proceeding; step 6: verify subagent scores before aggregating), and has a logical flow from collection through delegation, scoring, and reporting. The data collection section provides fallback paths (screenshots/manual entry if no exports).

The skill references external files (ads/references/thinking-framework.md, ads/references/scoring-system.md) and subagent skills, which is good progressive disclosure. However, no bundle files were provided to verify these references exist, and the report structure and cross-platform analysis sections are quite detailed inline content that could potentially be split into reference files. The balance between overview and inline detail leans toward too much inline.