Score visible effort evidence on specified URLs. Use when the user asks for a Content Effort audit, rater-style effort scoring, low-effort AI content review, or whether a page would look Lowest under QRG §3.2. Distinct from library keep/refresh/prune (seo-aeo-content-strategy) and from on-page ski-ramp implementation (on-page-seo-aeo-optimization).
Content Effort Audit scores visible effort evidence on specified URLs. It is not a library keep/refresh/prune pass and it is not an E-E-A-T trust review. The question is whether the Main Content shows satisfying, non-replicable human work a stranger can point at: first-party facts, original artifacts, synthesis that changes the reader's map, craft matched to page purpose, curation choices, and added value versus substitutes.
Google's Search Quality Evaluator Guidelines judge Main Content by effort, originality, and talent or skill. 2025 updates made little to no effort, originality, and added value a Lowest rating path, including AI paraphrase. Generative AI alone does not determine the rating. This skill operationalizes that rater language as a six-signal scorecard (0–2 each, 0–12 total) with QRG-aligned bands. The leaked contentEffort warehouse attribute stays in a separate inference column. Do not brief it as a confirmed ranking factor.
SEO leads after helpful-content or core-update volatility, consultants who keep getting asked to "add E-E-A-T" when the real failure is a SERP paraphrase, and teams shipping AI-assisted drafts who need a residue test rather than an origin ban. It also fits editors who must brief writers with a named missing object (dated screenshot, worked pricing, primary document) instead of "write more."
YMYL and informational pages still need an accuracy pass. This skill does not replace expert review. It also does not replace the content quality / library audit: a URL can be a keep and still be low effort.
Classify page purpose first. The bar for a short clip is not the bar for a documentary. Fetch the live URL and score only what is visible. Ignore time logs, outline decks, and "human review" checkboxes. Fill the six-signal table, map the total to Lowest / Low / High evidence language, list missing evidence, and rank effort adds. If three or more URLs share a skeleton, escalate from a page rewrite to a production-system flag (scaled low-effort). Hand off implementation to On-Page SEO and AEO Optimization.
Typical adds: a measurement only this team could publish, dated product screenshots, a decision rule the current SERP does not already repeat, a worked pricing example at two seat counts, or a named failure vendor docs omit.
Practitioners ask whether word count equals effort. It does not. A 3,000-word paraphrase can be Lowest-risk evidence. A shorter page with first-party artifacts can score High. They also ask whether AI content can pass. Yes, when artifacts and added value are on the page. No, when the model restated ranking pages and a human only fixed tone.
Another question is how this relates to information gain, helpful content, and E-E-A-T. Information gain is unique useful information. Effort is unique visible work. Helpful, people-first questions overlap on "do not just rewrite sources." E-E-A-T is trust. Score the matching failure. Do not pick the trendier acronym.
Attach Content Effort Audit to a task with a URL list and, if known, each page's purpose. Ask for the six-signal table, QRG-aligned band, missing-evidence list, and prioritized adds. Pair with SEO & AEO Content Strategy when the job is library keep/refresh/prune, and with On-Page SEO and AEO Optimization when the verdict is ready to implement. The matching Card Library template is Content Effort Audit; the hub article is the Content Effort guide.
See Content Effort guide for more on this workflow. Run it as a ready-made workflow with the Content Effort audit template.