3 179 marketing skills from 354 authors. They cover promotion, ads, search and everything aimed at an audience. Half of them fit into 2 029 tokens or less — that is what one costs your context window when the agent loads it. 597 ship runnable scripts rather than instructions alone. 35 of them cannot work without an MCP server, most often rube. We also found 247 copies of these same skills sitting in other people's repositories — counted once here, not 247 times.
3 179 unique 354 authors 1 612 updated this month 121 from vendors
Generate marketing intelligence briefings from compound intelligence across agents — surfaces learnings, cross-agent patterns, confidence distribution, and playbooks. Use when reviewing accumulated marketing learnings, preparing for quarterly planning, onboarding team members, or identifying knowledge gaps.
Build a content cluster plan from seed keywords — pillar+spokes architecture with internal-link map, intent grouping, and quality scorecard. Use when: planning topical authority, designing a content hub, deduping cannibalising pages, or staging a programmatic content rollout.
Research keyword expansion, intent, and gaps. Use when: mapping search intent, finding content gaps, or long-tail discovery.
Audit landing pages. Use when: scoring above-fold clarity, trust signals, form friction, message match, or mobile UX.
Launch paid ad campaigns. Use when: deploying ads on Google, Meta, LinkedIn, or TikTok with targeting and safeguards.
Orchestrate the full multi-channel launch of an approved campaign plan — pre-launch checklist, asset readiness gate, channel-by-channel activation, CRM campaign record creation, kickoff comms, day-1 monitor setup. Broader than /launch-ad-campaign (which is paid-ads only).
Save a marketing learning or insight. Use when: capturing knowledge, recording campaign results, building compound intelligence.
Audit local SEO health. Use when: reviewing GBP optimization, NAP consistency, local citations, or local pack rankings.
Build local SEO strategy. Use when: optimizing Google Business Profile, fixing NAP consistency, improving local pack rankings.
Localize campaigns for multiple markets. Use when: translating assets, adapting references, adjusting compliance.
Identify and model growth loops. Use when: detecting viral, content, or paid loops, modeling effectiveness, proposing new loops.
Design marketing automation workflows. Use when: building lead scoring, nurture sequences, drip campaigns, or behavioral triggers.
Audit the martech stack. Use when: evaluating marketing tools, recommending consolidation, or choosing between platforms.
Map the competitive narrative landscape. Use when: analyzing positioning territories, gaps, competitor claims, differentiation.
Track AI engine brand narratives. Use when: detecting narrative drift, misrepresentation, or competitor narrative gains over time.
Analyze SEO for a single page. Use when: auditing on-page signals, schema, content quality, E-E-A-T, or AI search readiness.
Plan paid advertising campaigns. Use when: managing Google Ads, Meta Ads, LinkedIn Ads, bid strategy, or budget optimization.
Pull live marketing metrics for a performance snapshot: KPIs vs targets, trend comparison, and cross-platform overview. Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.
Create media pitch packages. Use when: building pitch templates, media lists, outreach strategy, or journalist-request-platform responses (Qwoted, Featured, Source of Sources).
Test pricing strategies with synthetic data. Use when: simulating willingness to pay, price sensitivity, or optimal price points.
Plan programmatic SEO pages. Use when: building template engines, URL patterns, thin content safeguards, or quality gates.
Publish blog posts. Use when: deploying to WordPress or Webflow with SEO optimization, categories, and scheduling.
Monitor keyword rankings and SERP features. Use when: tracking keyword positions, detecting ranking drops, alerting on position changes, or tracking SERP-feature presence (AI Overviews, snippets, PAA, local pack).
Recall marketing learnings. Use when: querying what we know about a channel, audience, objective, or past campaign.
Calculate marketing ROI. Use when: measuring campaign ROAS, CAC, CPL, LTV, or multi-channel attribution returns.
Save brand knowledge to memory. Use when: persisting campaign learnings, guidelines, or competitive intel for retrieval.
Schedule social media posts. Use when: publishing to Twitter/X, Instagram, LinkedIn, TikTok, YouTube, or Pinterest.
Search stored brand knowledge. Use when: recalling past learnings, voice guidelines, or competitor insights via semantic search.
Create audience segments. Use when: building or updating CRM or email platform segments for campaign targeting.
Send email campaigns. Use when: deploying via SendGrid, Klaviyo, Customer.io, Brevo, or Mailchimp with A/B testing.
Send SMS or WhatsApp messages. Use when: deploying marketing messages via Twilio or Brevo with compliance checks.
Run comprehensive SEO audit. Use when: checking technical health, on-page, content quality, E-E-A-T, or link profile.
Compare two SEO snapshots (GSC, GSC AI Performance, rank tracker, AEO probe) and surface biggest movers per metric — impressions, clicks, position, AI citations. Use when: monthly performance reviews, post-Core-Update triage, AI Mode citation tracking, or before/after content-refresh evaluation.
Execute SEO changes. Use when: updating meta tags, schema markup, canonicals, redirects, or indexing via CMS MCP.
Build SEO strategy and roadmap. Use when: planning site architecture, content strategy, or phased implementation.
Deprecated — merged into rank-monitor. Use /digital-marketing-pro:rank-monitor --features for SERP-feature tracking (AI Overviews, snippets, PAA, local pack).
Measure share of voice. Use when: comparing keyword visibility, SERP presence, ad share, or AI citations vs competitors.
Build social media strategy. Use when: defining content pillars, posting cadence, engagement tactics, or growth plans.
Batch sync session learnings to memory. Use when: persisting campaign insights and performance history across sessions.
Run technical SEO audit. Use when: checking Core Web Vitals, crawlability, indexation, speed, or structured data.
Deep technical SEO analysis. Use when: optimizing crawlability, Core Web Vitals, rendering, redirects, or sitemaps.
Translate marketing content. Use when: localizing with brand voice preservation, quality scoring, or transcreation.
Verify marketing claims. Use when: cross-checking statistics, awards, certifications, or performance claims with sources.
Produce the 12-month operational Yearly Planner — the calendar companion to the Growth Plan in Part 8. Translates strategy into month-by-month execution.
> Build reusable conversion models — funnel/step conversion rates, drop-off, and time-to-convert — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute a conversion rate, funnel, step completion, drop-off, activation-funnel, signup-to-paid, or any "what % of users who did A went on to do B (within N days)" metric. Covers the funnel model (ordered steps, the conversion-window time-box, strict vs any-order), the person-vs-group aggregation unit, overall vs step-to-step conversion (two different numbers), breakdown attribution, and when a saved funnel insight beats a warehouse view. On PostHog, model funnels in HogQL with windowFunnel; in dbt, stage the event stream and compute an fct_conversion mart with tests. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics; pairs with query-funnel for interactive analysis.
> Build reusable dimension / lookup tables for a star schema — country/region, timezone, currency, date, plan/product, and other descriptive attributes — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model dimension tables, lookup tables, a star schema, conformed dimensions, or wants to enrich events/revenue/usage with country, region, timezone, plan, or currency attributes without repeating JOINs. Covers sourcing the dimension data (upload, warehouse source, or derive from events), shaping it into an aliased one-row-per-entity view (optionally materialized on a slow schedule since dimensions change rarely), and attaching it to facts via a saved or person join so its hand-rolled rate table. Read modeling-warehouse-foundations first; dimensions here are reused by the revenue, conversion, activation, and product-usage modeling skills.
> data-warehouse views / materialized views (HogQL, via the view-* MCP tools), or an external dbt project (sources.yml + staging/marts + schema tests) run against your own or PostHog's managed warehouse. Read before authoring any specific business model — covers the PostHog-vs-dbt decision, the view-create → view-materialize → sync_frequency workflow and the HogQL column-aliasing rule, the dbt project skeleton and the honest "no native dbt integration" picture, warehouse joins and star-schema dimensions, currency conversion with convertCurrency(), and checking/registering models in the data catalog for reuse. Companion to the domain skills modeling-revenue-metrics, modeling-conversion-metrics, modeling-activation-metrics, modeling-product-usage-metrics, and modeling-dimension-tables. Use when the user asks how to build a view, materialized view, or dbt model in PostHog, or which of the two stacks to use.
> AI hotspot monitoring and trending topic discovery across multiple sources (Bing, Google, DuckDuckGo, HackerNews, monitoring keywords, tech/AI news discovery, generating hotspot reports, "最近有什么热点", "帮我关注XX动态", "查一下XX最新消息", "生成热点报告", "monitor XX", "what's trending in XX", or any request to search/track/discover current events and trending content across Chinese and international platforms.