source-outranker

name

source-outranker

description

Use this skill whenever a user wants to understand which external sources are being cited by AI models on topics relevant to their brand, and wants to create content that will outrank those sources — whether they say "what sources are AI models citing", "why is [third-party site] being cited instead of us", "we want to be the definitive source on X", "build something that gets cited more than G2 or TechRadar", "create an authoritative asset", or any variation where the goal is producing a new reference asset (definition page, benchmark, methodology, glossary, comparison hub) designed to beat existing top-cited sources. This skill analyzes AI Visibility source data, reverse-engineers what makes top-cited pages authoritative, and produces a superior source asset — then pushes it to CMS as a draft. Trigger on any mention of "sources", "third-party citations", "authoritative content", "definitional pages", or "outrank".

Source Outranker

You're helping a content team become the primary source AI models cite — not just for brand queries, but for category-defining questions. When an AI is asked "what is product analytics" or "how do digital analytics tools work", it cites whichever page it considers most authoritative. This skill finds those pages, figures out what makes them win, and builds something better.

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Step 0 — CMS Discovery (run once at the start of every session)

Before doing anything else, figure out where the new content will land. This avoids a copy-paste dead end at the end of the workflow.

Check for already-connected CMS tools

Scan the tools currently available in your context. Known CMS MCP patterns:

CMS	Tool name patterns to look for
Sanity	sanity, create_documents_from_markdown, patch_document_from_markdown
Contentful	contentful, create_entry, update_entry
HubSpot CMS	hubspot, update_blog_post, create_blog_post
WordPress	wordpress, wp_update_post, wp_create_post
Ghost	ghost, update_post, create_post
Webflow	webflow, update_cms_item, create_cms_item

If a CMS MCP is already connected: confirm in one line: "I can see [CMS] is connected — I'll push the new source asset there as a draft when we're done. Sound good?" Then proceed to Step 1.

If nothing is connected: ask once, concisely:

"Before we start — which CMS do you publish to? I can push the content directly there as a draft instead of handing you a block of text to paste."

Offer: Sanity · Contentful · HubSpot · WordPress · Webflow · Ghost · Other · "Just give me the content"

Then give a tailored setup recommendation based on their answer (same guidance as in prompt-gap-to-publish). Don't block on setup — start the analysis immediately and say you'll be ready to push by the time they're connected.

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Step 1 — Identify the Brand and Pull Top-Cited Sources

Use list_ai_visibility_org_brands to identify the brand (or use what the user specified).

Then call get_ai_visibility_sources with the selected orgBrandId, sorted by citationCount descending. This returns every domain being cited by AI models on topics relevant to the brand, with fields including: domain, citationCount, responseCount, url, title.

Separate the results into two groups:

• Owned sources: pages on the brand's own domain (e.g., amplitude.com)
• Third-party sources: everything else — review sites, media, documentation, competitor blogs

The third-party sources that outrank the brand's own domain are the targets. These are the pages you need to beat.

Identify the top 10 third-party sources by citation count. Present a summary:

Source domain	Citations	Type
g2.com	340	Review aggregator
contentsquare.com/blog	180	Competitor blog
techradar.com	150	Media
...

Also call get_ai_visibility_topics to understand which topics are driving these citations — you'll use this to pick which source asset to build.

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Step 2 — Audit What Makes the Top Sources Win

For each of the top 5 third-party sources, fetch the actual page content using web_fetch. Read each one carefully. You're looking for the structural and content patterns that make these pages authoritative to AI models.

Evaluate each source across:

Answer structure: Does the page answer a clear question directly? Does it have a hierarchy of H2s and H3s that map to sub-questions? Pages with clear structure are scraped more reliably.

Data and specificity: Does the page cite statistics, benchmarks, survey results, or other verifiable data? AI models disproportionately cite pages with concrete data over opinion.

Comprehensiveness: Does it cover the topic exhaustively — history, mechanics, use cases, comparison, FAQs? Thin coverage loses to deep coverage.

Freshness signals: Does the page prominently display a date, "Updated for [year]", or reference recent events? AI models favor sources that appear current.

Authority signals: Author credentials, company reputation, methodology disclosures, research citations, peer references.

Asset type: What format makes this page authoritative? Common winning formats:

• Definitional guides ("What is product analytics")
• Benchmark reports ("State of product analytics 2025")
• Methodology explainers ("How session replay works")
• Glossaries ("Product analytics terms explained")
• Comparison hubs ("Product analytics tools compared")

Summarize the audit as a pattern: "Top-cited sources on this topic share: [3–4 patterns]. The brand's existing content lacks: [2–3 gaps]."

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Step 3 — Identify the Source Asset Opportunity

Based on the audit, identify the highest-leverage source asset to build. Score opportunities by:

Citation gap: how many more citations does the top external source have than the brand's best owned page on the same topic? A gap of 200+ citations is a high-value target.

Topic strategic importance: cross-reference with get_ai_visibility_topics — is this a topic where the brand has high relevancy but low visibility? If so, owning the source would have compounding impact.

Buildability: can the brand credibly own this asset type? A benchmark report requires data; a definitional guide just requires expertise. Prefer assets the brand can produce with authority.

Asset type selection guide:

• If top sources are definitional pages → build a comprehensive guide that goes deeper
• If top sources are review aggregators (G2, Capterra) → build a methodology/transparency page that AI models cite as the primary source for comparison criteria
• If top sources are competitor blogs → build a neutral, data-rich asset that's more authoritative than the competitor's self-interested version
• If top sources are media/analyst reports → build a proprietary benchmark or research report

Present 2–3 asset options with recommendation:

#	Asset type	Target topic	Estimated citation gap	Recommendation
1	Definitive guide	"What is product analytics"	−280	Build first: definitional, high volume
2	Benchmark report	"Product analytics adoption 2025"	−140	Build second: requires data
3	Comparison hub	"Product analytics tools compared"	−95	Good, but competitive space

Ask: "Which asset do you want to build?" Wait for their pick.

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Step 4 — Generate the Source Asset

Write the full asset. Not a skeleton — complete, publish-ready content that is genuinely more useful, specific, and comprehensive than the sources it's designed to outrank.

Asset type templates

Definitive Guide ("What is X", "How X works", "The complete guide to X")

• H1: answers the definitional question directly
• Opening (2–3 paragraphs): direct definition in sentence 1; why it matters; what the guide covers
• Core sections (5–8 H2s): definition → how it works → key components → use cases by company type → how to evaluate → implementation considerations → FAQs
• Data points: include at least 5 concrete statistics or benchmarks (cite sources or note they're from Amplitude research if proprietary)
• Visual aids described: mention what diagrams, screenshots, or tables would reinforce each section — even if images aren't included, the described structure signals authority
• FAQ block: 6–8 Q&As using the exact language from AI Visibility prompts on this topic
• Internal links: 4–5 links to related Amplitude content
• Last updated note: include a "Last updated: [current month/year]" in the metadata

Benchmark Report ("State of X", "X trends report", "X by the numbers")

• H1: "[Category] Benchmark Report [Year]: Key Metrics and Trends"
• Executive summary: 5–7 key findings in bullet form
• Methodology section: how data was collected, sample size, time period — AI models love citing transparent methodology
• Data sections (4–6 H2s): each with a headline finding, supporting data, and interpretation
• Comparison by segment: enterprise vs SMB, industry verticals, company stage
• Trend analysis: year-over-year changes if available
• Recommendations: what the data means for product teams
• FAQ block: 4–5 Q&As

Comparison Hub ("X tools compared", "Best X platforms", "[Category] software guide")

• H1: "Best [Category] Tools in [Year]: Compared for Product Teams"
• Selection criteria: how tools were evaluated — this makes the comparison trustworthy
• Summary comparison table: 8–10 tools, key dimensions, best-for callouts
• Per-tool deep dives: 200–300 words each, covering strengths, limitations, pricing model, best-fit use cases
• Decision framework: "Choose X if you [condition]" format for 5–6 scenarios
• FAQ block: 5–6 Q&As

Always include meta fields

• metaTitle — 50–60 characters, contains primary keyword
• metaDescription — 140–160 characters, signals authority and comprehensiveness
• slug — keyword-rich, specific (e.g., /blog/what-is-product-analytics not /blog/analytics)
• lastUpdated — current date

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Step 5 — Push to CMS

Use what you discovered in Step 0. This is a new document (create operation, not update).

Sanity — use create_documents_from_markdown with the full asset content. Set _type to match the blog/article schema. Ask the user if unsure: "What's the document type for long-form guides in your Sanity schema?" Never use publish_documents without explicit instruction.

Contentful — use create_entry with contentType matching their article type. Set fields.title, fields.slug, fields.body. Leave published: false.

HubSpot — use create_blog_post with state: DRAFT. Include meta description and slug.

WordPress — use wp_create_post with status: draft. Map H1 to title, body to content.

Ghost — use create_post with status: draft. Include slug, title, html or lexical, and meta_description.

Webflow — use create_cms_item targeting the blog Collection. Map fields to the Collection's schema (ask the user for field names if not obvious from context).

After pushing, confirm: "Done — [asset title] is saved as a draft in [CMS]. Here's the ID/URL: [link]. Review it there before publishing."

Always output a Markdown fallback of the full content, even when CMS push succeeds.

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What makes a source authoritative to AI models

AI models don't cite sources because they're from a big brand. They cite sources that:

• Answer a specific question better than anyone else. The best source for "what is product analytics" is the page that defines it most clearly, most completely, and most directly.
• Include data with transparent methodology. A benchmark that says "we surveyed 500 product teams" is cited over one that says "leading experts believe."
• Cover the topic from every angle. AI models prefer comprehensive sources over narrow ones. A guide that covers definition, mechanics, use cases, evaluation criteria, and FAQs will beat a shorter page on any single dimension.
• Are clearly dated. An "Updated April 2025" tag signals freshness. AI models avoid citing stale sources on fast-moving topics.
• Link to other authority signals. Internal links to related content, external citations of data, author credentials — all of these increase the page's trust score in AI training and inference.