Conciseness

The body is information-dense with almost no concept over-explanation — exact JSON schemas, exact flags (--model, --half-life, --format), and a compact attribution-model table instead of prose. It is not a 5 because the 'How to Use' section largely re-lists commands already shown in 'Typical Analysis Workflow' and repeats the --format flag from 'Output Formats', and Best Practice #7 references 'the provided template' that is never located anywhere — trimming would be straightforward. It is above anchor 3 because the padding is confined to one duplicative section rather than scattered over-explanation.

Actionability

Fully executable, copy-paste-ready commands for every tool with variants (default run, specific model, JSON output, custom half-life), complete input schemas for all three scripts, expected error behavior (KeyError/ValueError/TypeError), and a pre-flight validation command ('python -m json.tool your_file.json'). This matches 'copy-paste ready code or commands; specific examples cover the common cases' — the common cases are exactly what's shown.

Workflow Clarity

'Typical Analysis Workflow' gives a clear 3-step sequence with commands, per-step purpose comments, and a chaining rationale (attribution results → focus funnel analysis → validate ROI), plus pre-run input validation with documented failure modes. It falls short of anchor 5 because there is no post-run feedback loop (e.g., what to do when a script errors mid-workflow) and no explicit checkpoint confirming output validity before moving to the next step; it sits above anchor 3 because validation is present and the sequence is unambiguous.

Progressive Disclosure

The written structure is excellent — an overview with quick-start workflow, command reference, a Reference Guides table pointing one level deep to three clearly-purposed reference files, plus Best Practices/Limitations/Related Skills. It does not reach anchor 5 because the referenced bundle files (scripts/attribution_analyzer.py, references/attribution-models-guide.md, references/campaign-metrics-benchmarks.md, references/funnel-optimization-framework.md, assets/sample_campaign_data.json) are not present in the bundle to verify, and Best Practice #7 cites a 'provided template' with no path — leaving navigation partially unverifiable/dangling.

Specificity

The description lists multiple concrete actions — 'multi-touch attribution, funnel conversion analysis, and ROI calculation' plus 'calculating marketing ROI, ROAS, CPA, and campaign metrics' — giving comprehensive coverage of the skill's capabilities. This matches the anchor 'Lists multiple specific concrete actions; comprehensive coverage'; the only near-generic phrase is 'for marketing optimization', which is not enough to drop it below the anchor-5 example's level of specificity.

Completeness

It explicitly answers both questions: what ('Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation') and when ('Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA...'). This mirrors the anchor-5 example's 'clear what + explicit Use-when with concrete trigger phrases' pattern exactly.

Trigger Term Quality

Good natural-term coverage: 'marketing campaigns', 'ad performance', 'attribution models', 'conversion rates', 'ROI, ROAS, CPA', 'campaign metrics across channels' — phrases a marketing user would actually say. A few natural synonyms are missing ('ad spend', 'funnel analysis' as a trigger, 'marketing analytics/performance'), so it sits at the 'good keyword coverage; a few natural terms missing' anchor rather than comprehensive anchor 5.

Distinctiveness Conflict Risk

The niche is clear (campaign/marketing analytics) and the triggers are domain-distinctive — ROAS, CPA, and attribution models are phrases only a marketing-analytics request would contain, giving minimal conflict risk with generic analytics or data skills. Anchor 5 ('clear niche with distinct triggers; minimal conflict risk') fits; it is well above the 'minor overlap risk with closely related skills' of anchor 4.