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linear.app

Record Wy25LQY9U · May 31, 2026

for “issue tracking software for startups”

This audit was run before live answers existed, so it has no observed verdict.

54/100 readiness

Readiness needs work

How ready the page is to be cited, across 30 signals. Readiness, not a citation count.

Comparison not run. This audit ran without any competing pages to compare against, so there is no ranking to show.

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The live answer

No live answer for this audit

This audit doesn't include a live answer from Claude. The score, comparison and fixes below are complete.

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How your page compares

No comparison pages were available for this audit.

Readiness, signal by signal

30 signals, each measured from the page itself: 17 pass, 2 weak, 11 missing. Open any signal for the detail and the fix. This record was scored on an earlier, 30-signal version of the rubric; it has 47 signals today. Every signal is documented in the rubric.

Structure

50/100
  • JSON-LD structured data0

    No JSON-LD blocks found. Answer engines key off schema.org markup to ground citations.

    Fix: Add at least one schema.org JSON-LD block: Article, Product, FAQPage, or HowTo are the highest-lift types.

  • JSON-LD validity100

    No JSON-LD blocks to validate (the presence signal covers that).

  • Schema type matches query intent0

    No schema types: for a informational query, add one of: Article, BlogPosting, FAQPage, HowTo.

    Fix: Add an Article JSON-LD block: the highest-leverage schema type for a "issue tracking software for startups" query.

  • FAQ / Q&A schema0

    No FAQPage / QAPage schema: AI Overviews preferentially cite pages exposing structured Q&A.

    Fix: Add a FAQPage schema with 3-5 questions buyers actually ask. The single most direct path to AI Overview citation.

  • H1 quality60

    H1: "The product development system for teams and agentsThe produ…" (152 chars).

    Fix: Adjust H1 length to 20-80 chars: shorter than 20 lacks context, longer than 80 loses retrieval focus.

  • H2 subtopic coverage100

    8 H2 headings: answer engines decompose a buyer query into subtopics and match each to an H2.

  • Lists (bulleted / numbered)100

    12 lists found.

  • Comparison tables50

    No tables. Comparison / specification tables are heavily cited for product and technical queries.

  • Image alt-text coverage45

    14 of 31 images have alt text (45%).

    Fix: Add descriptive alt text to every image: engines index alt text as part of page content.

  • Open Graph metadata100

    Open Graph title, description, and image all present.

Authority

35/100
  • Outbound links to authoritative sources0

    0 links to authoritative domains (.edu, .gov, named sources).

    Fix: Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.

  • Inline citations and attributions0

    0 bracketed citations + 0 attribution phrases (per 200 words: 0%).

    Fix: Add inline citations ([1], [2]) or attribution phrases ("according to X"). GEO factor #1 for credibility-weighted citation.

  • Statistic density100

    29 numerical facts (~2.9 per 100 words).

  • Expert quotations75

    3 blockquotes + 0 long inline quotes.

  • Author byline0

    No author byline. E-E-A-T penalises anonymous content.

    Fix: Add a visible author byline (a `<div class="byline">` or `[rel="author"]`) AND a `<meta name="author">` tag.

  • Publish + update date markup0

    No structured publish date. Freshness ranking can't be applied.

    Fix: Add `<time datetime="...">` for published date AND a `<meta property="article:modified_time">` for updates.

  • Content freshness0

    No parseable date: engines treat undated content as stale.

    Fix: Add a parseable date: `<time datetime="2026-01-15">` or `<meta property="article:modified_time">`.

  • Technical / domain vocabulary100

    319 multi-syllable words (~31.8% of total).

Content

62/100
  • Direct answer in the lead20

    1 of 5 query terms appear in the first paragraph.

    Fix: Open the page with a direct, complete answer to the buyer query: engines extract their citation snippet from the lead paragraph.

  • Query-term coverage100

    100% of query terms appear somewhere on the page.

  • Named-entity coverage100

    56 distinct proper-noun entities mentioned.

  • Readability (Flesch Reading Ease)20

    Reading-ease score: 21.7 (target 60-70).

    Fix: Page reads too academic: shorter sentences and simpler vocabulary lift citation rate by ~12% (GEO §4.3).

  • Content depth100

    1003 words. Target 300-2500: engines bias toward focused depth.

  • Information density100

    Unique-word ratio: 52.9% (target 40-60%).

  • Definitional sentences0

    0 definitional sentences.

    Fix: Include definitional sentences ("X is...", "X refers to..."). Engines specifically extract definitions for AI Overview snippets.

Trust

100/100
  • HTTPS100

    Page served over HTTPS.

  • Canonical URL100

    Canonical: https://linear.app

  • Mobile-friendly viewport100

    Viewport meta tag present.

  • Twitter Card100

    Twitter Card metadata present.

  • Meta description100

    Meta description: 64 chars (target 50-160).

The Fix Kit

Readiness needs work (54/100): 10 structural patches needed. Paste-ready patches, filled from what your page already states. Anything in [brackets] is yours to fill in before publishing.

Article JSON-LD

Schema markup · High priority

A Article schema is the strongest single AEO signal for a informational query. Paste this in the <head>.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "The product development system for teams and agentsThe product developmentsystem for teams and agentsThe product development system for teams and agents",
  "description": "Purpose-built for planning and building products with AI agents.",
  "url": "https://linear.app/",
  "datePublished": "2026-05-31",
  "author": {
    "@type": "Person",
    "name": "Add author name"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Linear",
    "logo": {
      "@type": "ImageObject",
      "url": "https://linear.app/logo.png"
    }
  }
}
</script>

FAQPage JSON-LD

Schema markup · High priority

FAQPage is the single most-cited schema type by Google AI Overviews. Add 3-5 Q&A pairs covering what buyers ask.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is A new species of product tool. Purpose-built for modern teams with AI workflows at its core, Linear sets a new standard for planning and building products?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Make product \noperations self-driving?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Define the \nproduct direction?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Move work forward \nacross teams and agents?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    }
  ]
}
</script>

Organization JSON-LD

Schema markup · Medium priority

Organization schema gives the engine an E-E-A-T anchor: pages without one are treated as anonymous.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Linear",
  "url": "https://linear.app",
  "logo": "https://linear.app/logo.png",
  "sameAs": [
    "https://twitter.com/your-handle",
    "https://linkedin.com/company/your-org"
  ]
}
</script>

H1 quality

Page structure · Medium priority

H1: "The product development system for teams and agentsThe produ…" (152 chars).

Adjust H1 length to 20-80 chars: shorter than 20 lacks context, longer than 80 loses retrieval focus.

Outbound links to authoritative sources

Page structure · High priority

0 links to authoritative domains (.edu, .gov, named sources).

Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.

Inline citations and attributions

Page structure · High priority

0 bracketed citations + 0 attribution phrases (per 200 words: 0%).

Add inline citations ([1], [2]) or attribution phrases ("according to X"). GEO factor #1 for credibility-weighted citation.

Author byline

Page structure · High priority

No author byline. E-E-A-T penalises anonymous content.

Add a visible author byline (a `<div class="byline">` or `[rel="author"]`) AND a `<meta name="author">` tag.

Publish + update date markup

Page structure · High priority

No structured publish date. Freshness ranking can't be applied.

Add `<time datetime="...">` for published date AND a `<meta property="article:modified_time">` for updates.

Content freshness

Page structure · High priority

No parseable date: engines treat undated content as stale.

Add a parseable date: `<time datetime="2026-01-15">` or `<meta property="article:modified_time">`.

H1 rewrite suggestions

Copy · High priority

Three H1 candidates that lead with the buyer's core query term and stay within the 20-80 character AEO sweet spot.

• Carta (software company): A Practical Guide for 2026
• What Carta (software company) Actually Means for Teams
• Issue tracking software for startups: Explained in 5 Minutes

Rather not ship these yourself?

The Digiocular team can make the changes and check the answer again with you.

See done-for-you

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Re-check schedule

  1. Checked:54/100 readiness
  2. OpenRe-check after you ship the fixes

Audit by VectorCite · vectorcite.digiocular.com