SEO, ads, social media, landing pages, and copywriting (3.1K skills)
Turn weekly Google and Meta advertising data into a plain English briefing. Compare results with the previous week and four-week average, identify urgent issues and successful campaigns, and receive specific priorities for the coming week.
Analyze a site and target keywords to identify technical SEO issues, page improvements, missing content, and backlink opportunities. Receive a prioritized action plan and a writing brief with an outline, questions, and suggested internal links.
Turn Google and Meta campaign metrics into a plain English executive summary for client reports. Highlights results with specific numbers, explains likely causes, puts problems in context, and suggests next steps connected to the performance data.
Build a question set to check whether AI assistants mention your brand. Analyze the answers you collect, compare visibility against competitors, trace cited sources, and receive a prioritized list of opportunities to address gaps.
Forecast advertising return for Google and Meta over the next 30, 60, and 90 days. Uses performance trends, seasonal patterns, and planned changes to provide scenario ranges, underlying assumptions, risks, and revenue estimates tied to planned spending.
Restructure existing web pages so AI assistants can extract and cite answers accurately. Get rewritten sections, comparison tables, FAQs, and JSON-LD structured data, a machine-readable description of page content, while preserving readability.
Review daily Meta ad performance to spot signs that audiences are tiring of your creatives. Classify ads by urgency using click rates, frequency, costs, and engagement trends, then plan which creatives to replace and when.
Track how AI assistants describe a brand by comparing repeated answers to a fixed set of questions. The skill flags factual errors and changes in tone, identifies likely sources, and drafts correction requests and updates to owned content.
Analyze spending, revenue, and conversions across advertising channels to recommend budget shifts. Compare overall efficiency with the revenue gained from additional spending, identify saturation, and produce a gradual reallocation plan with monitoring priorities.
Compare how different attribution models, or ways of assigning conversion credit, change campaign results across Google and Meta. The skill highlights shifts in credited conversions and return on ad spend, then suggests budget adjustments based on the comparison.
Analyze successful Google and Meta ads to find useful patterns in headlines, body text, and calls to action. Generate new copy variants that preserve those patterns, with platform formatting, test priorities, and suggested pairings for comparison.
Analyze Google and Meta conversion journeys from the first ad click to a purchase or inquiry. Identify common paths, drop-offs, campaign contributions, and conversion delays to guide budget and targeting adjustments.
Compare Meta ad sets to identify shared audiences and estimate how internal competition affects advertising costs. Receive an overlap map, targeting issues, and recommendations for combining ad sets or excluding audiences to reduce overlap.
Analyze Google Ads campaign history, conversion volume, and budgets to choose suitable bidding strategies for each campaign. Get recommended targets, reasons for each choice, and a transition plan covering timing and short-term risks.
Analyze A/B test results to assess whether observed differences are likely due to chance. Check test duration and sample size, compare audience segments, distinguish statistical findings from practical value, and generate hypotheses for follow-up tests.
Review Google and Meta campaign structures against goals, budgets, and conversion data. Identify excessive segmentation, overlapping targeting, and fragmented budgets, then get consolidation recommendations, an explained account structure, and a migration plan.
Find search terms, placements, audiences, and ads that spend money but generate few or no conversions in Google and Meta accounts. The skill prepares uploadable exclusion lists and groups spending by category to help prioritize account cleanup.
Plan search pages at scale using data a business already owns. Compare seven approaches, select suitable ones, and define page templates, URL patterns, publishing stages, and checks for whether AI search services cite the pages.
Create consistent naming rules for Google and Meta campaigns, ad groups, ad sets, and ads. Get field definitions, allowed values, a team reference sheet, and mappings from existing names to support filtering and reporting.
Track Google and Meta ad spending against monthly budgets across accounts and campaigns. Project spending at month end, flag overuse or underuse, and calculate daily budget changes while accounting for weekday and weekend spending patterns.
Analyze competitor ads from Meta Ad Library and Google Ads Transparency Center, organize their messages and formats, and compare them with your strategy to find unused angles and ideas for campaign tests.
Review Google Ads extensions, the extra links and details shown with ads, across campaigns. Find missing coverage, weak performance, and outdated information, then get replacement copy based on successful ads and landing pages, plus rollout priorities.
Turn a long article into LinkedIn posts, Twitter/X threads, newsletter snippets, ad openings, and short video scripts. The skill adapts structure and length for each channel while keeping the original message and brand voice consistent.
Review landing pages alongside Google and Meta ads to identify mismatched promises, unclear calls to action, and burdensome forms. Get specific recommendations for headlines, offers, and the content visitors see before scrolling.