Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a substantively rich skill that brings genuine domain expertise about Meta's 2025-2026 ad delivery stack changes, creative diversity scoring, and comprehensive audit criteria. Its main weaknesses are the lack of executable code/commands for actually performing the audit steps, missing validation checkpoints in the workflow, and inline content that could benefit from being split into referenced files. The threshold tables and scoring rubrics are strong actionable elements.
Suggestions
Add concrete executable examples for key steps — e.g., a Python snippet for querying MAPI v25 for EMQ scores, or a shell command for exporting Ads Manager data, to make the process steps truly actionable.
Add validation checkpoints to the process workflow — e.g., 'Step 2: Verify all 50 checks have data sources before scoring; if EMQ data is missing, flag as INCOMPLETE rather than FAIL' to create feedback loops.
Move the detailed Entity-ID Clustering Predictor heuristics and Creative-as-targeting scoring rubric into separate referenced files (e.g., ads/references/creative-clustering.md) to keep SKILL.md as a leaner overview.
Add a concrete example of the creative-cluster-risk.md deliverable output so Claude knows exactly what format to produce.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and contains domain-specific knowledge Claude wouldn't inherently know (Andromeda/GEM/Lattice stack, Entity-ID clustering, EMQ optimization). However, some sections like the 'What to Analyze' checklists are verbose with items that could be more compressed, and explanations like 'CAPI active (30-40% data loss without it post-iOS 14.5)' add parenthetical context that's borderline unnecessary for Claude. | 2 / 3 |
Actionability | The skill provides concrete scoring rubrics, threshold tables, and specific metrics (EMQ ≥8.0, dedup ≥90%, etc.) which are highly actionable. However, it lacks executable code/commands — there are no actual scripts, API calls, or copy-paste-ready commands for data collection, MAPI queries, or report generation. The process section is a numbered list of high-level steps without concrete implementation details. | 2 / 3 |
Workflow Clarity | The 7-step process is clearly sequenced and references external files for detailed checks, scoring, and benchmarks. However, there are no validation checkpoints or feedback loops — no step says 'verify data completeness before proceeding' or 'if EMQ data is unavailable, do X instead.' For a 50-check audit with weighted scoring, the absence of error handling or verification steps is a gap. | 2 / 3 |
Progressive Disclosure | The skill references external files (meta-audit.md, benchmarks.md, scoring-system.md, compliance.md) which is good progressive disclosure design. However, no bundle files are provided, so these references are unverifiable. Additionally, the Andromeda/GEM/Lattice section and the detailed 'What to Analyze' checklists are quite long inline — the creative-as-targeting rubric and Entity-ID clustering predictor could arguably be in a referenced file to keep the SKILL.md leaner. | 2 / 3 |
Total | 8 / 12 Passed |